{"site":{"name":"Koji","description":"AI-native customer research platform that helps teams conduct, analyze, and synthesize customer interviews at scale.","url":"https://www.koji.so","contentTypes":["blog","documentation"],"lastUpdated":"2026-04-25T22:49:02.943Z"},"content":[{"type":"documentation","id":"980380d7-1617-4c48-b350-3ee78eb3bff9","slug":"sentiment-analysis-interviews","title":"Sentiment Analysis in Qualitative Research: Understanding Emotional Patterns","url":"https://www.koji.so/docs/sentiment-analysis-interviews","summary":"This guide explains how to identify and interpret emotional patterns in qualitative interview data through sentiment analysis. Key insight: customers with high emotional connection are 52% more valuable than those who are merely satisfied, making emotional analysis a commercial imperative, not a soft skill. Covers coding techniques, journey mapping, and AI-assisted analysis.","content":"Sentiment analysis in qualitative research is the process of identifying and interpreting emotional tone, feelings, and attitudes expressed in interview transcripts, open-ended survey responses, and other qualitative data. While traditional thematic analysis focuses on *what* participants say, sentiment analysis focuses on *how they feel* about it — and those emotional signals often contain the most actionable insights of all.\n\nResearch published in the *Journal of Marketing Research* found that customers who report high emotional connection with a product are 52% more valuable over their lifetime than those who are merely satisfied. Yet the majority of research teams focus almost exclusively on functional insights and miss the emotional layer entirely.\n\n## What Is Qualitative Sentiment Analysis?\n\nIn a quantitative context, \"sentiment analysis\" typically refers to automated NLP (natural language processing) tools that classify text as positive, negative, or neutral. In qualitative research, the concept is richer and more nuanced.\n\nQualitative sentiment analysis goes beyond positive/negative classifications to identify:\n- **Emotional intensity** — not just \"they were satisfied\" but \"they were delighted,\" \"they were frustrated,\" or \"they were resigned\"\n- **Emotional contradictions** — participants who say they are satisfied but use emotionally negative language throughout (\"It works, I guess\" / \"It's fine for now\")\n- **Emotional patterns across participants** — clusters of people who share the same emotional response to the same trigger\n- **Emotional journey** — how a participant's emotional state changed across different stages of a process or experience\n\nBraun and Clarke, whose 2006 thematic analysis framework has accumulated over 190,000 academic citations, emphasized that qualitative analysis must capture the full texture of participants' experiences — including the emotional register in which they describe them. Thematic analysis without sentiment awareness produces a skeleton of findings without the flesh that makes them meaningful.\n\n## Why Emotional Patterns Matter More Than Opinions\n\nMost research questions focus on behaviors and opinions: \"What do users do?\" and \"What do users think?\" But behaviors and opinions are often post-hoc rationalizations. Emotions are more direct signals of underlying motivation.\n\nConsider the difference between:\n- **Stated opinion**: \"The onboarding was pretty good.\"\n- **Emotion embedded in narrative**: \"I remember feeling like I finally got it — like this thing is actually going to work for me.\"\n\nThe second response contains a powerful emotional signal: a \"finally\" moment — relief, resolution, hope. That signal reveals far more about the product's value proposition (and what the participant was anxious about before using it) than the opinion statement does.\n\nRobert Cialdini's foundational research on influence and decision-making demonstrates that most adoption and purchasing decisions are driven primarily by emotional state, with rational justifications constructed afterward. Qualitative researchers who capture emotional patterns are capturing the real drivers of behavior — not the post-hoc stories people tell about those decisions.\n\n## Types of Emotional Signals in Qualitative Data\n\n### Explicit Emotion Words\nThe most obvious signal: participants directly naming their emotional state.\n- \"I was frustrated when...\"\n- \"It was exciting to finally...\"\n- \"I felt overwhelmed by the options\"\n- \"Honestly, I was embarrassed\"\n\n### Intensity Markers\nWords and phrases that signal the strength of an emotional response without naming the emotion:\n- \"Finally\" / \"at last\" (relief, resolution after struggle)\n- \"Every single time\" / \"always\" / \"never\" (entrenched frustration)\n- \"I actually told my team about this\" (genuine delight, advocacy)\n- \"I guess\" / \"I suppose\" / \"it's fine\" (low-intensity negative masked by politeness)\n\n### Metaphors and Comparisons\nParticipants reveal emotional experience through the images they reach for:\n- \"It felt like starting from scratch every time\" (frustration, futility)\n- \"It was like finally having a GPS instead of driving by memory\" (relief, empowerment)\n- \"It was like talking to a wall\" (isolation, dismissal)\n\n### Investment Signals\nThe amount of detail and energy a participant invests in describing an experience signals its emotional significance:\n- A participant who spends 90 seconds describing a specific negative moment cares about it more than one who mentions it in passing\n- A participant who returns to the same topic multiple times is signaling unresolved emotional weight\n\n## How to Analyze Sentiment in Qualitative Data: Step by Step\n\n### Step 1: Identify Emotional Language in Transcripts\n\nIn your first pass through transcripts, highlight all words, phrases, and passages that carry emotional weight. Use a specific color code or tag to distinguish emotional coding from thematic coding.\n\nFlag:\n- Explicit emotion words\n- Intensity markers (\"finally,\" \"always,\" \"never\")\n- Hedging language that signals masked negative emotion\n- Metaphors with emotional valence\n- Passages where the participant invests unusual detail\n\n### Step 2: Code for Valence and Intensity\n\nAssign each emotional highlight to a code that captures both the direction (positive/negative) and the intensity (low/medium/high):\n\n| Code | Example Quote | Valence | Intensity |\n|------|---------------|---------|-----------|\n| Relief | \"I finally felt like I understood it\" | Positive | High |\n| Frustration | \"I just gave up and called support\" | Negative | High |\n| Resignation | \"It's fine, I've just gotten used to it\" | Negative | Low |\n| Delight | \"I actually told my team — they need to try this\" | Positive | High |\n| Anxiety | \"I was worried I'd break something\" | Negative | Medium |\n| Pride | \"I figured it out before anyone else on my team did\" | Positive | Medium |\n\n### Step 3: Map Emotions to Journey Stages\n\nLayer emotional codes onto the participant's experience timeline. Where do positive emotions cluster? Where do negative emotions spike? This mapping reveals:\n\n- **Delight moments** — experiences that exceed expectations (these are your differentiators worth amplifying)\n- **Pain points** — experiences that consistently generate frustration or anxiety (these are roadmap priorities)\n- **Resignation zones** — areas where participants have stopped expecting improvement (these are churn risk signals)\n- **Anxiety spikes** — moments where fear of failure or confusion peaks (these are onboarding and UX design targets)\n\n### Step 4: Look for Emotional Contradictions\n\nSome of the most revealing data comes from participants whose stated opinions conflict with their emotional language. If someone says \"I'm satisfied with the product\" but uses primarily negative emotional language throughout the interview — \"I guess it works,\" \"it's not terrible,\" \"I've gotten used to it\" — that gap is telling.\n\nTheir stated satisfaction is rational and social. Their emotional relationship with the product is weak. These participants are your most vulnerable churners.\n\n### Step 5: Synthesize Emotional Themes\n\nJust as you synthesize thematic findings, synthesize emotional patterns into insight statements:\n\n- \"Users express significant relief at the moment of first successful completion, indicating the onboarding process generates substantial prior anxiety that the product must address earlier.\"\n- \"Power users show strong emotional ownership ('my workflow,' 'the way I do it'), while new users use distancing language ('the tool,' 'your thing') — suggesting onboarding fails to transfer a sense of ownership.\"\n- \"Every participant who mentioned a specific support interaction used high-intensity negative emotional language, regardless of whether their issue was resolved.\"\n\n## A Real-World Example\n\nA SaaS product team runs 12 exit interviews with churned users. In the screener, all 12 said they churned due to \"price\" or \"finding a cheaper alternative.\"\n\nA functional thematic analysis confirms this finding: price is the primary stated reason. The product team considers a pricing adjustment.\n\nBut a sentiment analysis of the same transcripts reveals something different:\n\n- Eight of the twelve participants used low-intensity resignation language when describing their main use of the product (\"It worked fine for basic stuff,\" \"It got the job done, I guess\")\n- Four participants used high-intensity frustration language about a specific moment — a report export flow — and then pivoted to price as their stated reason for leaving\n\nThe emotional analysis reveals that \"price\" was the rational justification, but there are two distinct churn profiles: resigned underutilizers (who never developed emotional commitment) and frustrated high-intent users (who hit a specific failure moment and disengaged).\n\nThese profiles require completely different retention strategies. A price change addresses neither.\n\n## Common Mistakes in Qualitative Sentiment Analysis\n\n1. **Treating hedging language as neutral.** \"It's fine\" is not neutral — it is a low-intensity negative masked by social politeness. The absence of enthusiasm is itself a signal worth coding.\n\n2. **Missing non-verbal emotion in video interviews.** Facial expressions, pauses, laughs, and sighs carry emotional data that transcripts strip away. Review video clips for participants who seem emotionally significant in the written record.\n\n3. **Over-relying on explicit emotion words.** Many participants do not explicitly name their emotions. They reveal them through metaphors, story structure, and the detail they invest in particular moments.\n\n4. **Ignoring positive emotions.** Research teams often focus disproportionately on pain points. Identifying what participants find genuinely delightful is equally important for product differentiation and positioning.\n\n5. **Reporting emotions without context.** \"Users feel frustrated with the checkout flow\" is far less useful than \"Users feel frustrated specifically at the moment of form validation failure, because they have already invested five minutes in the process and do not understand what went wrong.\"\n\n6. **Conflating sentiment with theme.** Sentiment analysis and thematic analysis are complementary, not interchangeable. Themes describe *what* participants discuss; sentiment describes *how they feel* about those things. Both dimensions are needed for a complete analysis.\n\n## How AI Changes Qualitative Sentiment Analysis\n\nManual sentiment analysis is time-intensive. For a thirty-interview study, a thorough emotional coding pass can add fifteen to twenty hours to the analysis phase — hours that most research teams do not have.\n\nAI-native research platforms like Koji automatically surface emotional patterns across interview transcripts, flagging high-intensity emotional moments, identifying sentiment clusters, and highlighting contradictions between stated opinions and emotional language. What once took days of manual analysis can be surfaced in minutes across dozens of simultaneous interviews.\n\nThis does not eliminate researcher judgment. Interpreting emotional patterns requires contextual and cultural understanding that automated tools currently lack. But it dramatically accelerates the discovery phase, allowing researchers to focus their interpretive energy on the most significant emotional signals rather than reading every line of every transcript from scratch.\n\nTeams that implement AI-assisted sentiment analysis alongside human interpretation report being able to run three to four times as many research cycles per quarter — translating directly into faster product decisions with better emotional grounding.\n\n## Integrating Sentiment Analysis With Thematic Analysis\n\nThe most complete qualitative analysis combines both dimensions:\n\n1. **Run thematic analysis first** to identify what participants are talking about and map the primary themes\n2. **Layer sentiment analysis** to understand how participants feel about each theme\n3. **Build emotional theme profiles** — for each major theme, document the dominant emotional register, key emotional contradictions, and journey-position context\n4. **Prioritize recommendations** based on both thematic frequency and emotional intensity — a theme that appears in 80% of interviews with low emotional intensity is less urgent than one that appears in 40% with high-intensity frustration\n\nFor a deeper foundation on the thematic analysis process, see our [complete guide to thematic analysis](/docs/thematic-analysis-guide) and [how to code qualitative data](/docs/coding-qualitative-data).\n\n## Key Takeaways\n\n- Qualitative sentiment analysis captures emotional tone, not just opinions — and emotions are stronger predictors of long-term behavior than rational evaluations\n- Emotional coding goes beyond positive/negative to capture intensity, journey position, and contradictions between stated and felt experience\n- Hedging language (\"I guess,\" \"it's fine\") is often a low-intensity negative signal disguised as neutrality\n- Mapping emotions to journey stages reveals delight moments, pain points, resignation zones, and anxiety spikes\n- The gap between stated opinions and emotional language is often the most revealing data in a transcript\n- AI-powered platforms can automate emotional pattern detection across large interview datasets, compressing multi-day analysis into minutes\n\n## Frequently Asked Questions\n\n**Q: Is qualitative sentiment analysis the same as NLP sentiment scoring?**\nA: No. NLP sentiment tools classify text as positive, negative, or neutral using statistical models — useful for large volumes of text but too blunt for nuanced qualitative analysis. Qualitative sentiment analysis is interpretive and captures emotional intensity, contradictions, and journey context that automated tools miss entirely.\n\n**Q: How many transcripts do I need for meaningful sentiment analysis?**\nA: Even a single transcript can yield valuable emotional insights. Meaningful patterns typically emerge after five to eight interviews with a homogeneous participant group. For cross-segment comparison, aim for eight to twelve interviews per segment.\n\n**Q: Should sentiment analysis replace thematic analysis?**\nA: No — they are complementary. Thematic analysis identifies what participants are talking about; sentiment analysis reveals how they feel about it. The most complete qualitative analysis combines both: themes with associated emotional textures and journey-mapped emotion profiles.\n\n**Q: How do I handle participants who seem emotionally flat or controlled in their responses?**\nA: Very controlled emotional presentation is itself meaningful data. It may indicate that the topic feels risky to discuss honestly — suggesting that rapport-building techniques are needed — or it may indicate genuine disengagement with the topic. Either interpretation is analytically significant.\n\n**Q: Can sentiment analysis be done retroactively on existing transcripts?**\nA: Yes — and this is one of its highest-value applications. Existing interview repositories often contain rich emotional data that was never analyzed for sentiment. Retrospective analysis on historical interviews can surface patterns that were missed on first pass and reframe conclusions from previous research cycles.\n","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:16.033567+00:00","metaTitle":"Sentiment Analysis in Qualitative Research — Koji","metaDescription":"Learn how to identify emotional patterns in qualitative interview data. Understand why feelings predict behavior better than stated opinions, and how AI automates the process.","keywords":["sentiment analysis qualitative research","emotional patterns interviews","qualitative data analysis","emotional coding interviews","how to analyze interview transcripts","qualitative sentiment analysis","interview emotional insights"],"aiSummary":"This guide explains how to identify and interpret emotional patterns in qualitative interview data through sentiment analysis. Key insight: customers with high emotional connection are 52% more valuable than those who are merely satisfied, making emotional analysis a commercial imperative, not a soft skill. Covers coding techniques, journey mapping, and AI-assisted analysis.","aiPrerequisites":["thematic-analysis-guide","coding-qualitative-data"],"aiLearningOutcomes":["Distinguish emotional coding from thematic analysis and understand when to use each","Identify explicit and implicit emotional signals in qualitative transcripts","Map emotional patterns to journey stages to find delight moments and pain points","Recognize emotional contradictions as the most revealing data in a transcript","Apply AI-assisted sentiment analysis to compress multi-day analysis into hours"],"aiDifficulty":"intermediate","aiEstimatedTime":"14 min read"},{"type":"documentation","id":"a8b4d0d3-13e8-4bd7-a2c5-16be0f1059d4","slug":"building-rapport-interviews","title":"Building Rapport in Research Interviews: How to Make Participants Open Up","url":"https://www.koji.so/docs/building-rapport-interviews","summary":"This guide covers how to build trust and comfort with research participants across the full interview journey—before, during, and after the session. Key insight: rapport is the foundation of honest qualitative data, and poor facilitation rapport is one of the leading causes of invalid research findings.","content":"Building rapport in research interviews is the single most important skill a qualitative researcher can develop. Without trust and comfort, participants give surface-level answers — polished, careful, and not particularly useful. With strong rapport, they share stories, frustrations, and honest opinions they would never reveal in a survey.\n\nResearch consistently shows that rapport problems are a leading cause of failed user interviews. According to Nielsen Norman Group, poor rapport is one of the five critical facilitation mistakes that can invalidate your research findings — yet it is the skill most training programs spend the least time on.\n\n## What Is Rapport — and Why It Matters\n\nRapport is a state of mutual trust, comfort, and connection between two people. In a research context, it is the psychological foundation that allows participants to lower their guard, speak honestly, and engage with your questions as a collaborative partner rather than a test subject.\n\nThe stakes are high. When participants do not feel rapport with the interviewer:\n- They give socially desirable answers rather than honest ones\n- They stay at the surface level, answering exactly what was asked and nothing more\n- They are less likely to volunteer important context or tangents\n- They are more likely to agree with leading questions\n\nA study by Nielsen Norman Group found that participants who felt comfortable in interview sessions produced three to four times more usable insights compared to sessions where participants remained guarded. According to the *State of User Research Report 2025*, 92% of researchers cite user interviews as their primary method — meaning that poor rapport systematically degrades the industry's most-used research tool.\n\n## The Rapport-Building Timeline\n\nRapport is not built in a single moment — it is constructed across the entire participant journey.\n\n### Before the Interview\n\n**Personalize your recruiting touchpoints.** When you reach out to schedule the interview, use the participant's name, acknowledge their context (e.g., \"I saw you're a product designer — that perspective will be invaluable\"), and explain why their specific experience matters.\n\n**Send a detailed briefing.** Uncertainty creates anxiety, and anxiety is rapport's enemy. Before the interview, tell participants exactly what to expect: the approximate duration, whether you'll record the session, who else will be present, and what kinds of topics you'll cover. Remove as much ambiguity as possible.\n\n**Make the logistics frictionless.** Confusing calendar invites, broken video links, or unclear instructions for joining the call create friction before the interview even begins. Every point of friction erodes the participant's sense that this is a professionally run, trustworthy engagement.\n\n### The Opening: First Five Minutes\n\nThis is the most critical window. Research on first impressions in conversational settings shows that the emotional tone of an interaction is largely set in the first few minutes — and is difficult to reset.\n\n**Start with genuine small talk.** Not forced small talk — genuine, context-appropriate conversation. Ask about something specific from their profile or background. This signals that you are a human, not a survey bot.\n\n**Explain your role clearly.** Many participants assume you built the product being discussed and will feel reluctant to criticize it. Clarify early: \"I'm a researcher — my job is to understand your experience, not to defend any decisions that were made. There are no wrong answers here.\"\n\n**Frame the session as a conversation, not a test.** Explicitly tell participants: \"I want to hear about your actual experiences — you're the expert here. I'll be asking about things you've done, not testing your knowledge. Please push back on any question that doesn't make sense.\"\n\n**Ask for permission to record.** If you need to record the session, ask warmly rather than formally: \"Would it be okay if I record our conversation? It's just so I can focus on listening rather than taking notes — it won't be shared outside the team.\" Most people agree, and the asking itself signals respect.\n\n### During the Interview\n\n**Use active listening signals.** Nod, make brief verbal acknowledgments (\"I see,\" \"right,\" \"that makes sense\"), and maintain appropriate eye contact. These signals tell participants you are genuinely engaged. Silence signals you are waiting for them to stop rather than actually listening.\n\n**Follow up on emotional cues.** When participants use words that signal emotion — \"frustrating,\" \"finally,\" \"I was surprised,\" \"it was kind of embarrassing\" — probe those words directly: \"You said 'finally' — tell me more about what you mean.\" This shows you are listening deeply, not just working through a question list.\n\n**Avoid the clipboard posture.** Researchers who are visibly reading from a script, typing notes, or checking their question list communicate that they are more focused on the process than on the person. Keep your notes or guide in your peripheral awareness, not your primary focus.\n\n**Match your energy to the participant.** Some participants are quiet and thoughtful; others are animated and expressive. Adapting your communication style — not mimicking, but calibrating — creates resonance. This is a subtle but powerful rapport signal.\n\n**Let silence work for you.** After a participant finishes answering, wait three to five seconds before asking the next question. This pause serves two functions: it signals that you are processing what they said (a respect cue), and it often prompts participants to continue talking, adding context they would not have otherwise shared.\n\n## When to Use Rapport-Building Techniques\n\n| Situation | Approach |\n|-----------|----------|\n| Exploratory research with new users | ✅ Extended warm-up, 8–10 minutes before diving into topics |\n| Sensitive topics (health, finances, work stress) | ✅ Normalize experiences before asking the participant to claim them |\n| Short research sessions (15–20 min) | ✅ Brief but warm — 2–3 minutes of small talk, then clear framing |\n| Expert/senior participants with limited time | ✅ Skip extended small talk; respect time, jump to substance quickly |\n| Remote video interviews | ✅ More verbal signals needed to compensate for limited body language |\n| Concept validation with existing customers | ❌ Don't oversell rapport — keep it professional and focused |\n\n## Techniques for Sensitive Topics\n\nSome research topics are inherently sensitive — financial behavior, health decisions, interpersonal conflicts, or professional failures. These require extra care.\n\n**Acknowledge the sensitivity directly.** \"I know this might touch on some personal decisions — I want you to know that everything you share stays within our research team, and I appreciate you being open with me.\"\n\n**Use third-person framing to reduce defensiveness.** Instead of \"Tell me about a time you made a mistake with X,\" try \"A lot of people we've spoken with have told us they sometimes struggle with X. Is that something you've ever experienced?\" The third-person framing normalizes the experience before asking the participant to claim it.\n\n**Accept incomplete answers graciously.** If a participant seems uncomfortable answering a question, don't push. Accept what they've shared, move on, and try a different angle later. Forcing an answer destroys rapport far more than it yields insight.\n\n## Common Rapport Mistakes to Avoid\n\n1. **Over-preparing the script at the expense of listening.** Researchers who are too focused on getting through their question list miss participants' non-verbal cues and emotional signals — which are often the most valuable data.\n\n2. **Correcting participants.** If a participant says something factually incorrect about your product or industry, resist the urge to correct them. Their perception is the data.\n\n3. **Making visible facial reactions to unexpected answers.** A raised eyebrow or a surprised look tells participants their answer was \"wrong\" — and they will self-correct on subsequent questions.\n\n4. **Being too formal.** Overly clinical language, stiff introductions, and an impersonal tone create a clinical, evaluation-like atmosphere. Participants clam up.\n\n5. **Talking about yourself too much.** Brief self-disclosure can build connection (\"I use a similar tool myself\"), but extended personal commentary shifts focus away from the participant.\n\n6. **Multitasking during the session.** Nielsen Norman Group identifies this as one of the most compromising facilitation mistakes — it breaks the conversational thread and signals to participants that their answers are not genuinely important.\n\n## A Real-World Example\n\nImagine you are researching why product managers abandon project management tools after the trial period. You start the interview with a cold, formal tone: \"We're going to be asking you about your experience with the product. Please answer as accurately as possible.\"\n\nThe participant, sensing an evaluation, becomes guarded. They say the product \"worked fine\" and that they switched because of \"budget reasons.\"\n\nNow imagine starting differently: \"I saw from your screener that you're leading product at a startup — that context is exactly what we need for this conversation. How long have you been in that role?\"\n\nThree minutes of genuine conversation later, the participant mentions they've been under pressure to launch two features simultaneously. When you ask about the tool switch, they say: \"Honestly? I just felt like I was spending more time managing the tool than managing my team. It started to feel like another thing I was behind on.\"\n\nSame participant. Completely different answer. The second response is actionable product insight. The first is noise.\n\n## How AI-Moderated Interviews Handle Rapport\n\nOne of the most interesting findings from teams using AI-moderated interview platforms is that many participants report more candor with an AI interviewer than a human one. The absence of a real human removes the social performance anxiety that can suppress honest responses — particularly for sensitive topics like churn reasons, financial behavior, and workplace frustrations.\n\nAI-native platforms like Koji run voice and text interviews where an AI consultant asks questions, listens, and probes responses — allowing participants to share without the social pressure of a human observer. For topics where social desirability bias is a concern, this format can yield more honest data than traditional moderated interviews.\n\nThat said, human interviewers who build genuine rapport still unlock a depth of narrative and emotional context that makes live interviews irreplaceable for deep exploratory research. The most effective research programs use both formats strategically.\n\n## Key Takeaways\n\n- Rapport is built before, during, and after the interview — not just in the opening\n- Remove uncertainty for participants: brief them thoroughly on what to expect before the session\n- The first five minutes set the emotional tone for the entire interview\n- Active listening signals — nods, pauses, verbal acknowledgments — are critical for maintaining openness\n- Probe emotional language: words like \"frustrating\" or \"finally\" are invitations to go deeper\n- Never correct, judge, or visibly react to participant answers\n- For sensitive topics, normalize the experience before asking the participant to claim it\n- Multitasking while facilitating is one of the most damaging mistakes an interviewer can make\n\n## Frequently Asked Questions\n\n**Q: How long does it take to build rapport in a research interview?**\nA: Most experienced interviewers can establish functional rapport in the first five to ten minutes with a warm opening, a clear explanation of their role, and genuine small talk. Deep rapport — where participants share sensitive or emotionally laden information — may take fifteen to twenty minutes to develop.\n\n**Q: Does rapport look different in remote vs. in-person interviews?**\nA: Yes. Remote interviews require more active verbal signaling since participants cannot see your full body language. They also require better logistics — stable video, clear audio, and tested meeting links. In-person interviews benefit from physical presence and the ability to read non-verbal cues more fully. Both formats benefit equally from warm openings and active listening.\n\n**Q: Can you recover from a poor start to an interview?**\nA: Often yes. If the opening was awkward, acknowledge it lightly: \"Let me start over — I want this to feel like a conversation, not a deposition.\" A moment of self-awareness and even light humor can actually improve rapport relative to a stiff but technically correct opening.\n\n**Q: Should I share my own opinions or experiences to build rapport?**\nA: Brief, relevant self-disclosure can build connection — but keep it genuinely brief. The moment you start sharing extended personal opinions or experiences, you shift the dynamic and risk anchoring the participant's answers to your framing.\n\n**Q: How do I build rapport when the participant seems rushed or disengaged?**\nA: Acknowledge it directly: \"I know you're busy — I want to make this as useful as possible for you. If at any point you want to stop or there's a question that doesn't apply, just say so.\" This shows respect for their time and gives them agency, which often increases engagement rather than ending the session early.\n\n\n---\n\n## Related Resources\n\n- [Active Listening Techniques](/docs/active-listening-techniques) — Deep listening skills\n- [Probing and Follow-Up Questions](/docs/probing-and-follow-up-questions) — Going deeper\n- [Empathy Interview Guide](/docs/empathy-interview-guide) — Empathetic research\n- [Avoiding Bias in Interviews](/docs/avoiding-bias-in-interviews) — Reduce research bias\n- [Remote Interview Best Practices](/docs/remote-interview-best-practices) — Remote rapport\n\n*Use [structured questions](/docs/structured-questions-guide) to create safe, structured interview experiences.*","category":"Interview Techniques","lastModified":"2026-04-25T19:14:16.033567+00:00","metaTitle":"Building Rapport in Research Interviews — Koji","metaDescription":"Learn how to build genuine rapport with research participants so they share honest, detailed insights. Covers techniques before, during, and after the interview.","keywords":["building rapport interviews","research interview techniques","how to make interview participants comfortable","interview facilitation","qualitative research rapport","user interview best practices","interview participant trust"],"aiSummary":"This guide covers how to build trust and comfort with research participants across the full interview journey—before, during, and after the session. Key insight: rapport is the foundation of honest qualitative data, and poor facilitation rapport is one of the leading causes of invalid research findings.","aiPrerequisites":["user-interview-guide","writing-interview-questions"],"aiLearningOutcomes":["Build rapport before, during, and after a research interview","Use active listening signals to encourage participant openness","Handle sensitive research topics without breaking participant trust","Recover from a poor interview opening","Recognize when AI-moderated interviews may outperform live interviewing for rapport-sensitive topics"],"aiDifficulty":"beginner","aiEstimatedTime":"12 min read"},{"type":"documentation","id":"3fd8784d-216e-49d7-81e1-807764a13780","slug":"reducing-no-shows","title":"How to Reduce Research Interview No-Shows: Proven Strategies That Work","url":"https://www.koji.so/docs/reducing-no-shows","summary":"This guide covers proven strategies to reduce research participant no-shows, including the three-touch confirmation sequence, scheduling best practices, incentive structures, and async interview alternatives. Key insight: most no-shows are preventable—with the right systems, teams can reduce no-show rates from 30–40% to under 10%.","content":"Research participant no-shows are one of the most common and costly problems in qualitative research. Industry data suggests that between 20–40% of scheduled participants either cancel at the last minute or simply fail to show up — wasting recruiter time, disrupting research schedules, and inflating project costs significantly.\n\nThe good news: most no-shows are preventable. With the right confirmation sequences, scheduling practices, and incentive structures, experienced research operations teams routinely achieve show-up rates above 85–90% — even without large recruitment budgets.\n\n## Why Participants Don't Show Up\n\nUnderstanding the causes of no-shows is the first step to reducing them. No-shows cluster into four categories:\n\n1. **Forgot** — The most common cause. The interview was scheduled days or weeks in advance, and without adequate reminders, it slipped the participant's mind.\n\n2. **Life happened** — A work meeting was rescheduled, childcare fell through, or a commute ran long. These are unpredictable but manageable with a flexible rescheduling system.\n\n3. **Lost motivation** — The participant was interested when they signed up but has become less engaged by the time the interview arrives, especially if there was a long gap between recruiting and scheduling.\n\n4. **Logistics confusion** — Wrong meeting link, wrong time zone, unclear instructions. A participant who cannot figure out how to join will often not try to reach you — they will simply not show.\n\nAccording to the *State of User Research Report 2025* by User Interviews, participant logistics and scheduling are consistently among the top operational pain points reported by research teams. Teams running ten or more interviews per month spend an average of four to six hours per week on scheduling and no-show management alone — time that could be spent on analysis and synthesis.\n\n## Strategy 1: The Three-Touch Confirmation Sequence\n\nA single calendar invite is not a confirmation strategy. Participants who receive only one communication point have nothing reinforcing their commitment between signup and session date.\n\n**The proven three-touch sequence:**\n\n**Touch 1 — Immediately after scheduling:** Send a warm, personalized confirmation email. Include: the date and time with time zone, a one-click link to add to their calendar, the meeting link they will use, what to expect in the session (approximate duration, topics, whether you will record), and your contact information for rescheduling.\n\n**Touch 2 — 48 hours before:** A friendly reminder email. Keep it brief, re-include the meeting link, and make rescheduling easy. Tone: \"Just a friendly reminder about our conversation tomorrow — looking forward to it!\"\n\n**Touch 3 — 1–2 hours before:** A brief final reminder. A text message or calendar notification works best for this touch. Something like: \"Hi [Name], we're on for [time] today — here's the link if you need it. See you soon!\"\n\nTeams that implement this three-touch sequence consistently report 15–25% improvement in show-up rates compared to calendar-invite-only workflows.\n\n**The double opt-in addition:** Some teams add a fourth touch — asking participants to actively confirm attendance 48 hours out (\"Reply YES to confirm your spot\"). This small friction point increases commitment and identifies low-motivation participants before the session day, giving you time to recruit a replacement.\n\n## Strategy 2: Scheduling Best Practices\n\nWhen you schedule an interview significantly affects show-up rates. Research teams tracking attendance by scheduling variable consistently find the same patterns:\n\n| Factor | Lower No-Show Risk | Higher No-Show Risk |\n|--------|-------------------|---------------------|\n| Day of week | Tuesday–Thursday | Monday, Friday |\n| Time of day | Mid-morning (10am–12pm) or mid-afternoon (2–4pm) | Early morning, right after lunch, end of day |\n| Lead time | 3–7 days out | Same day, or 2+ weeks out |\n| Session duration | 30–45 minutes | 60+ minutes |\n| Scheduling method | Automated self-serve scheduling link | Manual back-and-forth email |\n\n**Avoid scheduling too far in advance.** Participant motivation decays over time. Interviews scheduled more than two weeks out have significantly higher no-show rates than those within a three-to-seven-day window. If recruitment is happening well in advance, use a \"warm hold\" — secure the slot with a calendar placeholder and send a firm confirmation closer to the date.\n\n**Offer multiple convenient time slots.** The more flexible you are, the less likely participants are to encounter a conflict. Use scheduling tools that show availability in the participant's local time zone without back-and-forth email negotiation.\n\n**Account for time zone confusion explicitly.** Many apparent no-shows are participants who showed up at the wrong time due to a time zone error. Always spell out the time zone in every confirmation and reminder, and use scheduling links that auto-detect the participant's local time zone.\n\n## Strategy 3: Incentive Structures That Increase Commitment\n\nIncentives are not just about attracting participants — they function as commitment mechanisms. The way you structure incentives significantly affects show-up rates.\n\n**Match incentive level to session length.** According to User Interviews' 2025 incentive benchmarks, the appropriate incentive for a 45-minute consumer interview is $50–75. B2B participants typically require $75–150 for the same duration. Below-threshold incentives feel disrespectful and signal that you do not value the participant's time — which reduces both show rates and data quality.\n\n**Be clear about when and how incentives will be paid.** Uncertainty about payment logistics is a trust issue. State clearly in every communication: \"You will receive [incentive] within 24 hours of completing your session.\" Ambiguity about this erodes the participant's sense of the research team as a trustworthy, organized operation.\n\n**Consider non-cash incentives for hard-to-reach segments.** For B2B participants — particularly senior executives — charitable donations in their name, access to research findings, or professional development resources may be more compelling than gift cards. The incentive should feel aligned with their professional identity, not just their bank account.\n\n**Avoid threatening \"no-show penalties.\"** Explicitly threatening a no-show penalty creates a hostile dynamic before the session even begins. Instead, frame it positively and practically: \"To respect everyone's time, please reschedule at least four hours in advance if you need to change your slot.\"\n\n## Strategy 4: Make Rescheduling Easier Than Not Showing Up\n\nMany no-shows are participants who intended to reschedule but encountered friction — or felt too awkward to cancel. The fix is to make rescheduling so easy that it always beats ghosting.\n\n- Include a one-click reschedule link in every reminder\n- Have a clear, warm cancellation policy stated upfront\n- Respond to cancellation requests immediately and offer alternative slots in the same reply\n- Never express frustration with cancellations — this only discourages future communication\n\nCounterintuitively, making it easy to cancel often reduces actual cancellations. Participants who know they can reschedule without friction are less likely to avoid the issue entirely — and more likely to stay engaged with your research program over time.\n\n## Strategy 5: Screen for High-Commitment Participants\n\nNo-show reduction starts at recruitment. Some participant populations have inherently higher no-show rates than others, and your screener can help identify them before you invest scheduling time.\n\n**Signals that predict lower commitment:**\n- Signing up via a generic screener with no personalized communication\n- Minimal or rushed screener answers suggesting low engagement with the topic\n- Long lag time between screener completion and scheduling\n- Participants recruited from incentive-heavy panel pools where they are signing up for many simultaneous studies\n\n**Signals that predict higher commitment:**\n- Personal referral from existing customers or users\n- Screener responses that mention a specific pain point or experience — they have a story to tell and are motivated to tell it\n- Fast response time on scheduling communications\n- Willingness to complete a longer screener\n\nTeams tracking no-show rates by recruitment source consistently find that personal referrals and warm outreach to existing customers yield two to three times lower no-show rates than cold panel recruiting. This does not mean panels are unusable — it means they require more aggressive confirmation sequences.\n\n## Strategy 6: Day-of Protocols\n\nEven with perfect preparation, some participants will need a nudge on the day itself.\n\n**Send the final reminder at the right time.** A text message or calendar ping one to two hours before the session, with the meeting link included, is the highest-conversion reminder touchpoint. Email is fine as a backup, but text has significantly higher open rates for same-day communications.\n\n**Have a five-minute window rule.** If a participant has not joined five minutes after the scheduled start, send a brief, friendly message: \"Hi [Name], we're on for right now — here's the link in case you need it: [link]. No worries if you need a few more minutes.\" This warm check-in rescues participants who are running late or had a technical issue.\n\n**Wait fifteen minutes, then mark as no-show.** Do not extend the window beyond fifteen minutes — it disrupts your schedule for subsequent sessions. After fifteen minutes, send a brief, warm follow-up: \"We missed you today — completely understand if something came up. Would you be open to finding a new time?\"\n\n## Strategy 7: The Async Interview Alternative\n\nOne of the most structurally effective ways to reduce no-shows is to eliminate the synchronous scheduling constraint entirely. Asynchronous interview formats — where participants respond on their own schedule within a defined window — remove the no-show problem at its root.\n\nAI-moderated interview platforms like Koji run fully asynchronous voice and text interviews. Participants receive a link, join when they are ready within the open window (typically 48–72 hours), and complete the interview without a live scheduling dependency. Research teams that have adopted async AI interviews report completion rates of 70–85% without any scheduling overhead — compared to the 60–80% show-up rates typical of synchronous moderated interviews even with aggressive reminder sequences.\n\nAsync interviews are not appropriate for all research designs. Exploratory research that requires real-time rapport-building and dynamic probing still benefits from live moderated interviews. But for structured discovery, concept testing, and large-scale research where you need high participant volume, async AI interviews eliminate the no-show problem entirely while dramatically reducing operational overhead.\n\n## Common Mistakes That Increase No-Show Rates\n\n1. **Relying on a single calendar invite.** No reminders, no follow-up, no confirmation sequence. This is the single most common and most fixable mistake in research operations.\n\n2. **Making rescheduling feel difficult.** If the only way to reschedule is to reply to an email and negotiate a new time manually, many participants will avoid doing it and simply not show.\n\n3. **Scheduling too far in advance.** Motivation decays rapidly. A three-week lead time dramatically increases no-show risk, particularly with panel-recruited participants.\n\n4. **Using unclear platform links or requiring downloads.** Test your links before sending. Verify that participants can access the meeting platform without downloading software or creating an account.\n\n5. **Not accounting for time zones.** Spell out the time zone explicitly in every communication. Verify that scheduling tools are displaying the correct local time for each participant.\n\n6. **Showing frustration with cancellations or late arrivals.** This discourages participants from communicating proactively in the future, turning potential reschedules into silent no-shows.\n\n## What No-Show Rates by Research Type Should Look Like\n\n| Research Type | Typical No-Show Rate (No Mitigation) | Target With Best Practices |\n|---------------|--------------------------------------|---------------------------|\n| Consumer interviews (warm outreach) | 15–25% | Under 10% |\n| Consumer interviews (cold panel) | 25–40% | 15–20% |\n| B2B interviews (senior IC/manager) | 20–35% | Under 15% |\n| B2B interviews (executive/C-suite) | 30–50% | 20–30% |\n| Async AI interviews | N/A (no scheduling) | 70–85% completion |\n\n## Key Takeaways\n\n- Most research no-shows are preventable: typical rates of 20–40% can be reduced to under 15% with the right systems\n- The three-touch confirmation sequence — immediately after scheduling, 48 hours before, one hour before — is the single highest-impact operational intervention\n- Schedule interviews three to seven days in advance for optimal commitment levels; avoid both same-day and two-weeks-plus windows\n- Make rescheduling frictionless — easy cancellation often reduces actual cancellations by removing avoidance behavior\n- Track no-show rates by recruitment source to identify high-commitment participant populations and optimize your recruitment mix\n- Async AI interview formats structurally eliminate the no-show problem for research designs that do not require live moderation\n\n## Frequently Asked Questions\n\n**Q: What is a realistic target show-up rate for user research interviews?**\nA: With a strong confirmation sequence and solid recruitment practices, 80–90% is achievable for consumer research. B2B research with senior participants typically runs 70–80% even with best practices. Anything below 60% suggests a systemic issue with either the confirmation process, incentives, or recruitment source quality.\n\n**Q: Should I overbook to compensate for no-shows?**\nA: Some teams recruit 20–30% more participants than their target sample size to account for no-shows. This works as a buffer but is expensive and creates logistical complexity. It is better to invest in reducing no-shows than to normalize overbooking as a permanent workaround.\n\n**Q: How do I handle same-day no-shows?**\nA: Send a brief, warm check-in message five minutes after the scheduled start time with the meeting link included. Wait ten to fifteen more minutes before marking as no-show. Some participants are simply running late or had a technical issue with the meeting platform.\n\n**Q: How long should I wait before reaching out to reschedule after a no-show?**\nA: Reach out within 24 hours while the missed appointment is still fresh. Keep the tone warm and non-accusatory: \"We missed you today — completely understand if something came up. Would you be open to finding a new time?\" Most participants who no-showed due to a genuine life event will appreciate the gracious follow-up.\n\n**Q: Does offering more money always reduce no-shows?**\nA: Not necessarily. Above a threshold of respectful compensation, increasing incentives yields diminishing returns on show-up rates. The bigger levers are confirmation sequences, scheduling practices, and recruitment source quality — not incentive levels alone.\n\n\n---\n\n## Related Resources\n\n- [Finding Research Participants](/docs/finding-research-participants) — Recruitment strategies\n- [Screening Participants](/docs/screening-participants-effectively) — Quality screening\n- [Incentive Strategies](/docs/incentive-strategies) — Effective incentive design\n- [Recruiting B2B Participants](/docs/recruiting-b2b-participants) — B2B recruitment\n- [Sharing Your Interview Link](/docs/sharing-your-interview-link) — Distribution methods\n\n*Use [structured questions](/docs/structured-questions-guide) for faster, more engaging research experiences.*","category":"Participant Recruitment","lastModified":"2026-04-25T19:14:16.033567+00:00","metaTitle":"Reduce Research Interview No-Shows — Koji","metaDescription":"Proven strategies to reduce research participant no-shows from 30% to under 10%. Covers confirmation sequences, scheduling tactics, incentives, and async interview alternatives.","keywords":["reduce research no-shows","research participant no-shows","interview confirmation best practices","user research scheduling","participant show-up rate","research recruitment tips","async user interviews"],"aiSummary":"This guide covers proven strategies to reduce research participant no-shows, including the three-touch confirmation sequence, scheduling best practices, incentive structures, and async interview alternatives. Key insight: most no-shows are preventable—with the right systems, teams can reduce no-show rates from 30–40% to under 10%.","aiPrerequisites":["finding-research-participants","incentive-strategies"],"aiLearningOutcomes":["Implement a three-touch confirmation sequence that reduces no-shows by 15–25%","Identify scheduling patterns (day, time, lead time) that predict higher attendance","Structure incentives as commitment mechanisms, not just recruitment tools","Make rescheduling frictionless to eliminate avoidance behavior","Evaluate async AI interview formats as a structural alternative to the no-show problem"],"aiDifficulty":"beginner","aiEstimatedTime":"11 min read"},{"type":"documentation","id":"63e9900d-faab-47b4-aa12-83da846ab9db","slug":"editing-your-profile","title":"Editing Your Profile","url":"https://www.koji.so/docs/editing-your-profile","summary":"Guide to editing your Koji profile at Dashboard > Profile. Covers display name, company, role (dropdown with Founder/CEO, GTM/Marketing, UX Researcher, Product Manager, PhD/Thesis Student, Other), avatar, email, profile visibility, and account deletion.","content":"# Editing Your Profile\n\nYour profile in Koji controls how you appear to team members and within your projects. You can update your name, company, role, and avatar at any time from the dashboard.\n\n---\n\n## Accessing Your Profile\n\nTo open your profile settings:\n\n1. Click your avatar or initials in the bottom-left corner of the Koji dashboard.\n2. Select **Profile** from the menu.\n\nThis opens the **Dashboard > Profile** page where you can edit all your profile fields.\n\n---\n\n## Profile Fields\n\nYour profile includes the following fields:\n\n### Display Name\n\nThis is the name shown to team members across the Koji platform. It appears in project activity feeds, comments, and interview attribution. Use your real name so colleagues can easily identify you.\n\nTo update your name:\n1. Click the **Display Name** field.\n2. Type your new name.\n3. Click **Save Changes**.\n\n### Company\n\nYour company name helps Koji personalize the experience and is used in team-related features. If you work with multiple organizations, set this to your primary organization.\n\nTo update your company:\n1. Click the **Company** field.\n2. Enter your company name.\n3. Click **Save Changes**.\n\n### Role\n\nYour role describes your position within your organization. This field uses a **dropdown menu** with the following options:\n\n- **Founder/CEO**\n- **GTM/Marketing**\n- **UX Researcher**\n- **Product Manager**\n- **PhD/Thesis Student**\n- **Other**\n\nThis information is used to tailor certain aspects of the platform experience and appears on your profile within team views.\n\nTo update your role:\n1. Click the **Role** dropdown.\n2. Select your role from the list.\n3. Click **Save Changes**.\n\n### Avatar\n\nYour avatar is the image displayed next to your name throughout the platform. A good avatar helps team members quickly identify you in collaborative contexts.\n\nTo update your avatar:\n1. Click on your current avatar image (or the default initials circle).\n2. Select an image file from your computer. Supported formats include JPEG, PNG, and WebP.\n3. The image will be uploaded and applied.\n\nThe recommended avatar size is at least 200x200 pixels. Larger images are automatically resized. Square images work best since avatars are displayed in a circular crop.\n\n---\n\n## Email Address\n\nYour email address is displayed on the profile page but cannot be changed directly from the profile editor. Your email is tied to your authentication method. If you need to update your email address, this may require updating your sign-in method.\n\nYour email is used for:\n- Account recovery and security notifications\n- Team invitations and project notifications\n- Billing receipts and plan change confirmations\n\n---\n\n## When Profile Changes Take Effect\n\nProfile updates take effect immediately. The next time you or your team members load any page in Koji, the updated information is displayed. There is no delay or approval process.\n\nNote that changes to your display name or avatar may take a moment to propagate across all active sessions if you have Koji open in multiple tabs.\n\n---\n\n## Profile Visibility\n\nYour profile information is visible to:\n\n- **Team members** in any organization you belong to\n- **Project collaborators** who share projects with you\n\nYour profile is not visible to:\n\n- **Interview respondents.** Respondents see the project branding and interviewer persona, not your personal profile.\n- **The general public.** Koji does not publish user profiles publicly.\n\nThis means you can customize your profile for internal team collaboration without worrying about how it appears to external parties.\n\n---\n\n## Tips for a Good Profile\n\n- **Use your real name.** This helps team members find and recognize you quickly.\n- **Upload a clear photo.** A recognizable avatar makes collaboration smoother, especially in larger teams.\n- **Keep your role current.** If your position changes, update your profile so team-related features stay accurate.\n- **Set your company name.** This is especially helpful if you collaborate across multiple organizations.\n\n---\n\n## Deleting Your Account\n\nIf you want to delete your Koji account entirely, this is a separate action from editing your profile. Account deletion is permanent and removes all your personal data, project ownership, and team memberships. To request account deletion, look for the option at the bottom of the Profile page or contact support.\n\nBefore deleting your account, consider:\n- Transferring ownership of any projects you own to a team member.\n- Downloading any data or exports you want to keep.\n- Informing collaborators who rely on shared projects.\n\n---\n\n## Troubleshooting\n\n### Avatar upload fails\n\nMake sure your image file is under 5MB and in a supported format (JPEG, PNG, or WebP). If the upload continues to fail, try a different browser or clear your browser cache.\n\n### Changes not appearing\n\nProfile updates are instant but may require a page refresh to appear in all tabs. Hard refresh with Ctrl+Shift+R (Cmd+Shift+R on Mac) if changes are not visible.\n\n---\n\n## Related Resources\n\n- [Creating Your Account](/docs/creating-your-account) — Account setup guide\n- [Managing API Keys](/docs/managing-api-keys) — API key management\n- [Bring Your Own Key](/docs/bring-your-own-key) — Custom AI provider keys\n- [Quick Start Guide](/docs/quick-start-guide) — Get started with Koji","category":"Account & Settings","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Editing Your Profile — Koji Docs","metaDescription":"Update your name, company, role, and avatar in your Koji account settings for better team collaboration.","keywords":["profile settings","account settings","avatar","display name","user profile"],"aiSummary":"Guide to editing your Koji profile at Dashboard > Profile. Covers display name, company, role (dropdown with Founder/CEO, GTM/Marketing, UX Researcher, Product Manager, PhD/Thesis Student, Other), avatar, email, profile visibility, and account deletion.","aiPrerequisites":["creating-your-account"],"aiLearningOutcomes":["Update profile information","Upload a profile avatar","Understand profile visibility","Manage account settings"],"aiDifficulty":"beginner","aiEstimatedTime":"5 min read"},{"type":"documentation","id":"51478266-f1dd-41c7-b2be-9673304418df","slug":"bring-your-own-key","title":"Bring Your Own Key (BYOK)","url":"https://www.koji.so/docs/bring-your-own-key","summary":"Guide to Koji's Bring Your Own Key (BYOK) feature. BYOK is a per-user feature supporting google_gemini and elevenlabs providers. Keys are managed at Dashboard > Profile > API Keys and encrypted at rest. Available on all plans (Free, Insights, Interviews, Enterprise).","content":"# Bring Your Own Key (BYOK)\n\nKoji's Bring Your Own Key feature lets you connect your own API keys from AI providers instead of using Koji's built-in allocation. This gives you direct control over costs, access to your organization's negotiated rates, and the ability to use private or enterprise model deployments.\n\n---\n\n## Who Is BYOK For\n\nBYOK is designed for teams that:\n\n- **Want cost transparency.** You see exactly what you spend on AI processing through your own provider dashboard.\n- **Have negotiated enterprise rates.** If your organization has volume discounts with an AI provider, BYOK lets you take advantage of those rates.\n- **Need specific model access.** If you have access to private or early-access models through your provider, BYOK lets Koji use those models.\n- **Require data residency controls.** Using your own API key means AI requests flow through your provider account, giving you control over data processing regions.\n\n---\n\n## Availability\n\nBYOK is available as a per-user feature. When enabled on your account, you can connect your own provider keys regardless of your plan (Free, Insights, Interviews, or Enterprise). Contact the Koji team if you need BYOK access enabled for your account. See the [plan comparison guide](/docs/plan-comparison-guide) for an overview of all plans.\n\n---\n\n## Setting Up BYOK\n\nTo connect your own API key:\n\n1. Navigate to **Dashboard > Profile > API Keys** in the Koji dashboard.\n2. Click **Add Provider Key**.\n3. Select the AI provider from the dropdown.\n4. Enter your API key.\n5. Click **Test Connection** to verify the key works.\n6. Click **Save**.\n\nOnce saved, Koji uses your key for all AI-powered features including interview conversations, analysis, and insight generation.\n\n### Testing the Connection\n\nThe **Test Connection** button sends a small validation request to the provider using your key. This confirms:\n\n- The key is valid and not expired.\n- The key has the necessary permissions.\n- The provider is reachable.\n\nIf the test fails, double-check that you copied the full key and that it has not been revoked or restricted.\n\n---\n\n## Supported Providers\n\nKoji supports BYOK for the following AI providers:\n\n| Provider ID | Description |\n|---|---|\n| `google_gemini` | Google Gemini models for conversation and analysis |\n| `elevenlabs` | ElevenLabs for voice interview audio |\n\nThe available providers are shown in the dropdown when you add a new key. The platform automatically uses the appropriate models from your provider based on the task being performed.\n\n---\n\n## How BYOK Affects Billing\n\nWhen you use BYOK:\n\n- **Koji does not charge for AI processing.** Your usage runs through your own provider account, and you pay the provider directly.\n- **Your Koji subscription still applies.** Plan features and credit allocation are still governed by your Koji plan.\n- **You monitor costs on the provider side.** Use your AI provider's dashboard to track usage and spending.\n\nThis can be significantly more cost-effective for high-volume research teams, especially those with enterprise provider agreements.\n\n---\n\n## Managing Your Keys\n\nFrom the **Dashboard > Profile > API Keys** page, you can:\n\n- **View connected providers.** See which providers have active keys.\n- **Test keys.** Re-run the connection test at any time to verify keys are still valid.\n- **Remove keys.** Delete a provider key to switch back to Koji's built-in allocation for that provider.\n- **Update keys.** Replace an existing key if you need to rotate credentials.\n\nWhen you remove a BYOK key, Koji automatically falls back to its built-in AI allocation (subject to your plan's included usage).\n\n---\n\n## Key Security\n\nKoji takes the security of your API keys seriously:\n\n- **Encrypted at rest.** Keys are encrypted before being stored in the database.\n- **Encrypted in transit.** Keys are transmitted over TLS and never sent in plain text.\n- **Masked in the UI.** After saving, only the last four characters of your key are displayed.\n- **Access-controlled.** Only account owners can view or manage BYOK settings.\n- **Not logged.** Your keys are never written to application logs.\n\nDespite these protections, follow best practices for API key management:\n\n- Use a dedicated key for Koji rather than sharing a key across multiple services.\n- Set spending limits on the provider side to prevent unexpected charges.\n- Rotate keys periodically.\n- Monitor your provider dashboard for unusual usage patterns.\n\n---\n\n## Troubleshooting\n\n### Key test fails with \"invalid key\"\n\nDouble-check that you copied the complete key with no extra spaces. Some providers include a prefix (like `sk-`) that must be included.\n\n### Key test fails with \"insufficient permissions\"\n\nYour API key may be restricted to certain endpoints or models. Koji requires access to the models used for conversation and analysis. Check your provider's key permissions settings.\n\n### AI features slower than before\n\nPerformance depends on your provider account's rate limits and tier. If you are on a free or low-tier provider plan, requests may be throttled. Consider upgrading your provider plan for better performance.\n\n### Unexpected charges on provider account\n\nEach interview and analysis pipeline makes multiple requests to the AI provider. Higher-volume research projects generate more requests. Use your provider's usage dashboard to track exactly which models and endpoints are being called.\n\n---\n\n## Disabling BYOK\n\nTo stop using your own keys and switch back to Koji's built-in allocation:\n\n1. Go to **Dashboard > Profile > API Keys**.\n2. Click **Remove** next to the provider key you want to disconnect.\n3. Confirm the removal.\n\nKoji immediately falls back to its built-in AI allocation. Your interview data and project configurations are not affected by this change.","category":"Account & Settings","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Bring Your Own Key (BYOK) — Koji Docs","metaDescription":"Connect your own AI provider API keys to Koji for cost control, enterprise rates, and custom model access. Available on Growth+.","keywords":["byok","bring your own key","ai provider","api key","cost control"],"aiSummary":"Guide to Koji's Bring Your Own Key (BYOK) feature. BYOK is a per-user feature supporting google_gemini and elevenlabs providers. Keys are managed at Dashboard > Profile > API Keys and encrypted at rest. Available on all plans (Free, Insights, Interviews, Enterprise).","aiPrerequisites":["creating-your-account"],"aiLearningOutcomes":["Set up BYOK with your AI provider","Understand billing implications","Manage and rotate provider keys securely","Distinguish between BYOK and project API keys"],"aiDifficulty":"intermediate","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"2cd1a072-32a5-447c-8ca0-7448a25797ee","slug":"managing-api-keys","title":"Managing API Keys","url":"https://www.koji.so/docs/managing-api-keys","summary":"Guide to managing Koji API keys at Dashboard > Profile > API Keys. Covers creation, four permissions (interview:start, interview:chat, interview:complete, interview:read), key format (pk_live_), rotation, revocation, rate limiting, and security best practices.","content":"# Managing API Keys\n\nAPI keys are the credentials your application uses to authenticate with the Koji API. Each key is scoped to a specific project and carries permissions that control what operations it can perform. This article covers the full lifecycle of managing API keys -- from creation through rotation and revocation.\n\n---\n\n## Where to Find API Key Management\n\nAPI keys are managed from your profile:\n\n1. Open the Koji dashboard.\n2. Navigate to **Dashboard > Profile > API Keys**.\n3. The **API Keys** section displays all keys across your projects.\n\nYou need project admin or owner permissions to manage API keys.\n\n---\n\n## Creating an API Key\n\nTo create a new key:\n\n1. Click **Create API Key** on the API Keys page.\n2. Enter a **name** for the key. Use something descriptive that identifies where the key will be used (e.g., \"Production Backend\", \"Staging Server\", \"CI/CD Pipeline\").\n3. Select **permissions** for the key:\n   - `interview:start` -- Allows starting new interviews\n   - `interview:chat` -- Allows sending and receiving messages during an interview\n   - `interview:complete` -- Allows completing interviews and triggering analysis\n   - `interview:read` -- Allows retrieving interview data, transcripts, and analysis\n4. Click **Generate**.\n5. **Copy the key immediately.** Koji displays the full key only once at creation time. After you close this dialog, only the key prefix is visible.\n\nStore the key in a secure location such as an environment variable, a secrets manager, or an encrypted configuration file. Never hardcode API keys in your source code.\n\n---\n\n## Permissions in Detail\n\nEach permission controls access to specific API endpoints:\n\n### interview:start\n\nAllows calling `POST /api/v1/interviews/start`. This is the minimum permission needed to begin an interview via the API. The response includes the interview ID, session token, and initial message.\n\n### interview:chat\n\nAllows sending messages to an active interview session and receiving AI responses. This permission is required for any integration that manages the conversation flow programmatically.\n\n### interview:complete\n\nAllows calling `POST /api/v1/interviews/:id/complete`. This marks an interview as finished and triggers the automatic analysis pipeline.\n\n### interview:read\n\nAllows calling `GET /api/v1/interviews/:id`. This lets you retrieve the full transcript, analysis results, quality scores, and statistics for any interview in the project.\n\n### Combining Permissions\n\nMost integrations need all four permissions. However, the principle of least privilege suggests you only grant what is needed:\n\n- **Full integration** (start, chat, manage, retrieve): All four permissions.\n- **Read-only dashboard**: Only `interview:read`.\n- **Interview launcher**: `interview:start` and `interview:complete` (retrieval handled separately).\n- **Chat bot integration**: `interview:start`, `interview:chat`, and `interview:complete`.\n\nSee [API Authentication](/docs/api-authentication) for more details on how permissions work with the API.\n\n---\n\n## Viewing Existing Keys\n\nThe API Keys section lists all active keys:\n\n| Column | Description |\n|---|---|\n| Name | The descriptive name you assigned |\n| Key | Masked, showing only the key prefix |\n| Permissions | The permissions granted to this key |\n| Created | When the key was created |\n| Last Used | The most recent time the key was used in an API request |\n\nThe **Last Used** timestamp helps you identify inactive keys that may be candidates for revocation.\n\n---\n\n## Updating Key Settings\n\nYou can update key settings without generating a new key:\n\n1. Find the key in the list.\n2. Click the **Edit** button.\n3. Update the name, active status, allowed origins, or rate limit as needed.\n4. Click **Save**.\n\nChanges take effect immediately. Any in-flight requests using the old settings may succeed if they were already authenticated, but subsequent requests use the updated configuration.\n\n---\n\n## Revoking a Key\n\nIf a key is compromised, no longer needed, or being rotated out:\n\n1. Find the key in the list.\n2. Click the **Revoke** button.\n3. Confirm the action in the dialog.\n\nRevocation is **immediate and permanent**. Any request using the revoked key fails immediately with a `401 Unauthorized` response. There is no way to un-revoke a key -- you must create a new one.\n\n---\n\n## Key Rotation Best Practices\n\nRegular key rotation limits the damage if a key is ever exposed. Here is the recommended rotation process:\n\n1. **Create a new key** with the same permissions as the one you are replacing.\n2. **Update your application** to use the new key.\n3. **Verify the new key works** by monitoring API responses.\n4. **Revoke the old key** once you confirm the new key is active in all environments.\n\nDo not revoke the old key before confirming the new one works. Having two active keys simultaneously during rotation is expected and safe.\n\nA quarterly rotation schedule (every 90 days) is a good starting point for most teams.\n\n---\n\n## How Many Keys Should You Have\n\nThere is no strict limit on the number of API keys per project, but here are some guidelines:\n\n- **One key per environment.** Separate keys for development, staging, and production make it easy to revoke a key in one environment without affecting others.\n- **One key per service.** If multiple backend services call the Koji API, each should have its own key for easier auditing and independent revocation.\n- **Avoid sharing keys.** Never share a single key between multiple developers or services. Individual keys make access control and auditing possible.\n\n---\n\n## API Key Format\n\nKoji API keys follow the format `pk_live_` followed by 32 characters. For example:\n\n```\npk_live_aBcDeFgHiJkLmNoPqRsTuVwXyZ012345\n```\n\nWhen stored, only the first 12 characters (the prefix) are retained for identification. The full key is shown only once at creation time.\n\n---\n\n## Rate Limiting\n\nEach API key has a configurable rate limit (default: 60 requests per minute). When exceeded, subsequent requests receive a `429 Too Many Requests` response. See [Rate Limits and CORS](/docs/rate-limits-and-cors) for details on headers and retry strategies.\n\n---\n\n## Security Best Practices\n\n- **Never commit keys to version control.** Use environment variables or a secrets manager.\n- **Set allowed origins.** Restrict which domains can use the key to prevent misuse if the key is exposed.\n- **Use the minimum permissions needed.** Grant only the permissions each integration actually requires.\n- **Monitor the Last Used timestamp.** Revoke keys that have not been used in 90+ days.\n- **Rotate keys quarterly.** Regular rotation limits exposure from undetected leaks.\n- **Use HTTPS only.** All API requests must use HTTPS. HTTP requests are rejected.\n\nFor more on API integration, see the [API Authentication](/docs/api-authentication) guide.","category":"Account & Settings","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Managing API Keys — Koji Docs","metaDescription":"Create, configure permissions, rotate, and revoke project-level API keys for Koji integrations.","keywords":["api keys","key management","permissions","key rotation","revoke api key"],"aiSummary":"Guide to managing Koji API keys at Dashboard > Profile > API Keys. Covers creation, four permissions (interview:start, interview:chat, interview:complete, interview:read), key format (pk_live_), rotation, revocation, rate limiting, and security best practices.","aiPrerequisites":["api-authentication"],"aiLearningOutcomes":["Create and configure API keys","Assign granular permissions","Rotate keys safely","Revoke compromised keys"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"a15825e7-ab70-4966-bcf1-720cf8ffb5e5","slug":"referral-program","title":"Referral Program","url":"https://www.koji.so/docs/referral-program","summary":"Koji's referral program gives both referrer and referred user credits when the new user completes a qualifying interview. Share your unique referral link from Account Settings to start earning.","content":"# Referral Program\n\nKoji's referral program rewards you for spreading the word. When someone you refer signs up and becomes an active user, both of you earn credits that can be applied to your Koji subscription.\n\n---\n\n## How the Referral Program Works\n\nThe process is straightforward:\n\n1. **You share your referral link** with a colleague, friend, or anyone who could benefit from Koji.\n2. **They sign up** using your link and create an account.\n3. **They become an active user** by completing their first qualifying interview.\n4. **Both of you earn credits.** The referrer (you) and the referred user both receive credits applied to your respective accounts.\n\nCredits are applied automatically. There is no claim process or redemption step.\n\n---\n\n## Finding Your Referral Link\n\nYour unique referral link is available in your account settings:\n\n1. Click your avatar in the bottom-left corner of the dashboard.\n2. Select **Account Settings**.\n3. Navigate to the **Referrals** tab.\n4. Your personalized referral link is displayed and ready to copy.\n\nEach Koji user has a unique referral link. Sharing your link is the only way to get credit for referrals.\n\n---\n\n## What Counts as a Qualifying Referral\n\nFor a referral to qualify and trigger credit rewards:\n\n- The referred person must be a **new user** who has never had a Koji account.\n- They must sign up using **your referral link**. If they sign up without the link, the referral is not tracked.\n- They must complete at least one **qualifying interview** — an interview that meets the quality threshold and counts toward usage.\n\nSignups alone do not trigger rewards. The referred user must actually use the platform.\n\n---\n\n## Credit Rewards\n\nWhen a referral qualifies:\n\n- **You (the referrer)** receive credits applied to your account.\n- **The referred user** also receives credits as a welcome bonus.\n\nCredits are applied to your next billing cycle and reduce the amount charged to your payment method. If your credits exceed your bill, the remaining balance carries over to the next cycle.\n\nThe exact credit amount is displayed on your Referrals page, as the program terms may be updated from time to time.\n\n---\n\n## Tracking Your Referrals\n\nThe Referrals tab in your account settings shows:\n\n| Column | Description |\n|---|---|\n| Referred User | The name or email of the person you referred (partially masked for privacy) |\n| Status | Pending (signed up but not yet active), Qualified (completed a qualifying interview), or Expired (did not qualify within the eligibility window) |\n| Credits Earned | The amount of credits you earned from this referral |\n| Date | When the referral signed up |\n\nUse this dashboard to monitor your referrals and see how many credits you have earned.\n\n---\n\n## Sharing Your Link\n\nYou can share your referral link through any channel:\n\n- **Email.** Send a personal message to a colleague explaining how Koji has helped your research.\n- **Slack or Teams.** Share in relevant channels where team members discuss research tools.\n- **Social media.** Post about your experience with Koji and include your link.\n- **Presentations and workshops.** When presenting research findings gathered with Koji, mention the platform and share your link.\n\nPersonal recommendations tend to be the most effective. A brief explanation of how you use Koji and what value it provides gives your referral context and makes people more likely to try it.\n\n---\n\n## Tips for Successful Referrals\n\n- **Be genuine.** Share what you actually like about the platform. Authentic recommendations convert better than generic pitches.\n- **Target the right audience.** Product managers, UX researchers, designers, and founders who conduct user research are the best fit.\n- **Explain the value.** Tell people specifically how Koji has improved your research workflow — faster interviews, better insights, less manual analysis.\n- **Timing matters.** Refer colleagues when they mention they need to do user research or when they complain about the pain of manual interview analysis.\n\n---\n\n## Program Terms\n\nA few important details about the referral program:\n\n- **No self-referrals.** You cannot refer yourself or create multiple accounts to earn credits.\n- **Eligibility window.** Referred users have a window of time after signing up to complete a qualifying interview. If they do not qualify within this period, the referral expires.\n- **Credit expiration.** Credits have an expiration period. Check the Referrals page for the specific terms.\n- **Subject to terms of service.** Abuse of the referral program (fake accounts, spam, misleading promotion) results in disqualification and potential account suspension.\n- **Program changes.** Koji may update credit amounts or program terms. Changes apply to future referrals, not retroactively.\n\n---\n\n## Using Your Credits\n\nCredits are automatically applied to your subscription billing. You do not need to manually redeem them. When your next invoice is generated, any available credits are deducted from the total.\n\nIf you have accumulated more credits than your monthly bill, the excess credits roll over to the following month. Credits cannot be exchanged for cash or transferred to another user.\n\n---\n\n## Common Questions About Credits\n\n### Do credits work on any plan?\n\nYes. Credits apply to any paid Koji plan. They reduce your invoice amount regardless of which plan tier you are on.\n\n### Can I see my credit balance?\n\nYour current credit balance is visible on the Referrals tab in Account Settings, and it is also reflected on your billing page.\n\n### What if I downgrade or cancel?\n\nCredits remain on your account if you downgrade. If you cancel your subscription entirely, unused credits are forfeit. Reactivating your account does not restore expired credits.\n\n---\n\n## Next Steps\n\n- [Compare plans to find the best fit](/docs/plan-comparison-guide)","category":"Account & Settings","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Referral Program — Koji Docs","metaDescription":"Earn credits by referring colleagues to Koji. Learn how the referral program works and how to track your rewards.","keywords":["referral program","earn credits","refer a friend","koji credits","referral link"],"aiSummary":"Koji's referral program gives both referrer and referred user credits when the new user completes a qualifying interview. Share your unique referral link from Account Settings to start earning.","aiPrerequisites":["creating-your-account"],"aiLearningOutcomes":["Find and share your referral link","Understand qualifying referral criteria","Track referral status and credits","Apply credits to your subscription"],"aiDifficulty":"beginner","aiEstimatedTime":"5 min read"},{"type":"documentation","id":"96350776-e902-46a1-b185-54649d9ec251","slug":"reading-your-research-report","title":"How to Read Your Koji Research Report: A Section-by-Section Guide","url":"https://www.koji.so/docs/reading-your-research-report","summary":"A Koji research report synthesizes all interview data into an AI-generated document with themes, quantitative charts for structured questions, sentiment analysis, key quotes, and Insights Chat. Reports are generated automatically after 5+ completed interviews and can be refreshed as more data arrives.","content":"Your Koji research report is the final output of your study — a synthesized, AI-generated document that transforms raw interview data into structured insights. This guide walks through every section of the report, explains what each element means, and shows you how to extract maximum value from your findings.\n\n## What Is the Research Report?\n\nAfter your study collects enough responses (typically 5 or more completed interviews), Koji automatically generates a research report. The report synthesizes all interview data — qualitative themes, quantitative structured answers, sentiment, and key quotes — into a readable document you can review, share, and publish.\n\nYou don''t need to read every transcript. The report surfaces the patterns, highlights the most illuminating quotes, and presents quantitative data in chart form. Then you can dive into specific transcripts to explore anything that needs more context.\n\nTo generate or refresh a report, see [Generating Research Reports](/docs/generating-research-reports). To share your report with stakeholders, see [Publishing and Sharing Reports](/docs/publishing-sharing-reports).\n\n## Report Overview Section\n\nThe first section of every report is the **Overview**, which provides context for everything that follows.\n\n### Research Goals\n\nA restatement of your research brief — what you were trying to learn and what decisions this research was meant to inform. This anchors the entire report in the original question you set out to answer.\n\n### Interview Summary\n\nKey stats at a glance:\n- Total interviews completed\n- Average interview duration\n- Interview mode (voice, text, or mixed)\n- Quality distribution — how many interviews scored 3, 4, or 5 on Koji''s quality scale\n\nQuality scores matter because Koji only includes interviews that meet a minimum quality threshold (score of 3 or higher on a 1–5 scale) in your report analysis. Low-quality responses — incomplete interviews, very short sessions, or off-topic conversations — are filtered out automatically, so your findings reflect genuine research conversations.\n\nSee [Understanding Quality Scores](/docs/understanding-quality-scores) to understand how quality is calculated.\n\n### Key Takeaways\n\nThree to five bullet-point findings that represent the most important patterns across all interviews. These are the top-line insights for an executive summary — generated by AI from the full dataset. They''re designed to answer: \"What did we learn, and what should we do about it?\"\n\n## Themes Section\n\nThe Themes section is the heart of qualitative synthesis. Koji automatically groups insights from all interviews into thematic clusters based on what participants expressed.\n\n### How Themes Are Created\n\nThe AI reads all interview transcripts and identifies recurring ideas, concerns, opinions, and experiences. Themes emerge when multiple participants independently express similar things — not just using the same words, but expressing the same underlying idea.\n\nFor example, five participants might say: \"The setup took too long,\" \"The onboarding confused me,\" \"I almost gave up during configuration,\" \"Getting started was painful,\" and \"The first hour was frustrating.\" These surface as a single theme: **Onboarding friction**.\n\nEach theme card includes:\n- **Theme name** — A concise label\n- **Theme description** — What the theme represents across participants\n- **Evidence count** — How many participants this theme appears in\n- **Representative quotes** — Direct quotes from transcripts that illustrate the theme\n\n### Reading the Evidence Count\n\nA theme that appears in 14 out of 15 interviews is a different signal than one that appears in 3 out of 15. The evidence count tells you how prevalent each theme is across your participant group.\n\nThemes with high evidence counts (more than 60% of participants) are structural — they represent shared experiences that your whole user base likely has. Themes with lower counts may represent minority experiences that are still worth investigating.\n\n### Navigating from Themes to Transcripts\n\nFrom any theme in the report, you can click through to the specific interview excerpts that contribute to it. This lets you read the full conversational context around any pattern you want to understand more deeply — going from the aggregate view to the individual voice.\n\n## Questions Section\n\nIf your study includes specific interview questions — which all structured studies do — the Questions section shows a breakdown of results for each one.\n\n### Open-Ended Questions\n\nFor open-ended questions, the report shows:\n- **Summary** — A 2–3 sentence synthesis of how participants answered this question overall\n- **Key insights** — The most common or significant responses\n- **Representative quotes** — The most illuminating direct quotes from participants\n\n### Scale Questions\n\nFor scale questions (NPS, CSAT, satisfaction ratings), the report shows:\n- **Distribution chart** — A bar chart showing how responses spread across the scale values\n- **Mean and median** — Summary statistics across all participants\n- **Qualitative context** — Patterns from probing follow-ups that explain the score distribution\n\nA score distribution that clusters around 6–7/10 tells you something. The qualitative context tells you *what* — why participants aren''t at 9 or 10. See the [Scale Questions Guide](/docs/scale-questions-guide) for details on how scale questions work.\n\n### Choice Questions\n\nFor single choice and multiple choice questions, the report shows:\n- **Frequency bar chart** (single choice) or **stacked frequency chart** (multiple choice)\n- **Response counts and percentages** for each option\n- **Probing insights** — Qualitative context from follow-up questions about the selection\n\n### Ranking Questions\n\nFor ranking questions, the report shows:\n- **Ranked list with average position** — Each option''s mean rank across all participants\n- **Distribution** — How often each item was ranked 1st, 2nd, 3rd, etc., across all participants\n\n### Yes/No Questions\n\nFor yes/no questions, the report shows:\n- **Pie/donut chart** showing the percentage split between yes and no\n- **Probing insights** — What participants in each answer direction said when probed further\n\nSee the [Choice and Ranking Questions Guide](/docs/choice-ranking-questions-guide) for a full explanation of how these question types work.\n\n## Sentiment Analysis\n\nEvery interview is analyzed for overall sentiment: positive, negative, neutral, or mixed. The report shows a sentiment distribution across all interviews.\n\nSentiment isn''t just about whether participants were happy or unhappy — it''s a signal about what kind of data you collected. A churn research study with 60% negative sentiment is expected by design. A customer satisfaction study with 60% negative sentiment is a very different finding that demands attention.\n\nYou can filter the themes and quotes sections by sentiment to see how the picture changes between satisfied and dissatisfied participants — revealing whether problems are universal or segment-specific.\n\n## Key Quotes Section\n\nThe Key Quotes section surfaces the most memorable, specific, and illuminating quotes from across all interviews. These are selected by the AI for their research value — not for being positive or negative, but for being genuinely informative.\n\nThe best research quotes share three qualities:\n- **Specific** — They describe a concrete experience, not a vague opinion\n- **Surprising** — They challenge assumptions or reveal something unexpected\n- **Representative** — They capture something multiple participants expressed differently\n\nThese quotes are what you''ll use in design briefs, product discussions, roadmap presentations, and stakeholder meetings. They turn abstract findings into human voices.\n\n## Insights Chat\n\nAfter your report generates, you can use **Insights Chat** to ask natural language questions about your data. Think of it as a research analyst who has read every single transcript.\n\nExamples of questions you can ask:\n- \"What is the most common reason participants gave for switching from a competitor?\"\n- \"Did any participants mention pricing as a concern?\"\n- \"What did participants say about the mobile experience?\"\n- \"Which themes appear most often in interviews with negative sentiment?\"\n- \"What did participants who gave low NPS scores have in common?\"\n\nInsights Chat draws on both the structured data (charts, ratings, selections) and the full qualitative data (all transcripts) to answer your questions. It''s especially useful for testing hypotheses that didn''t surface in the main report — a specific competitor mention, a pattern you noticed in one transcript, or a question a stakeholder raised after seeing the top-line findings.\n\nSee [Insights Chat Guide](/docs/insights-chat-guide) for full documentation.\n\n## Sharing Your Report\n\nOnce you''re satisfied with your report, you can:\n\n1. **Share a link** — Generate a public or password-protected link to share with stakeholders who don''t have Koji accounts\n2. **Publish to your docs** — Make the report available at a permanent URL\n3. **Export the data** — Download interview data in CSV or JSON format for further analysis in external tools\n\nSee [Publishing and Sharing Reports](/docs/publishing-sharing-reports) for step-by-step instructions, and [Exporting Research Data](/docs/exporting-research-data) for all available export formats.\n\n## Refreshing the Report\n\nAs more interviews complete, you can refresh the report to incorporate the new data. Each refresh re-analyzes all interviews and updates all themes, charts, and insights. A report refresh costs 5 credits. The refreshed report fully replaces the previous version.\n\n## Reading Reports as a Team\n\nResearch reports are most valuable when the right people read them before decisions are made — not after. Some ways to make reports work harder for your team:\n\n**Share early.** Use the share link to get findings in front of decision-makers as soon as the report generates, not after a polished presentation is prepared.\n\n**Use Insights Chat for stakeholder questions.** When a stakeholder asks \"but did any participants mention [X]?\", use Insights Chat to answer on the spot rather than manually searching transcripts.\n\n**Cross-reference quantitative and qualitative data.** A distribution chart shows you the *what* (40% rated satisfaction 3/5). The probing insights show you the *why* (\"the product is good but the documentation is impossible to find\"). Always read both together.\n\n**Prioritize by evidence count.** A theme that appears in 12 out of 15 interviews demands action. A theme that appears in 2 out of 15 is worth noting but not necessarily acting on immediately. Evidence count is your prioritization signal.\n\n## Frequently Asked Questions\n\n**How many interviews do I need before a report is useful?**\nA report becomes meaningful around 5 interviews. With fewer than that, patterns are hard to distinguish from noise. Most researchers aim for 8–15 interviews for qualitative studies, and 15 or more for studies with quantitative structured questions where chart distributions matter.\n\n**Can I edit the AI-generated report content?**\nYou cannot directly edit the AI-generated text sections, but you can use Insights Chat to get a different framing or angle. The export formats (CSV, JSON) let you work with the raw data in your own analysis tools.\n\n**What is the difference between themes and key takeaways?**\nKey takeaways are curated top-line findings for executive audiences — brief, actionable, and high-level. Themes are the full thematic synthesis — more numerous, more nuanced, and with supporting evidence and quotes. Key takeaways are good for presentations; themes are good for research.\n\n**How does quality filtering affect the report?**\nOnly interviews scoring 3 or higher on Koji''s 1–5 quality scale are included in report analysis. This filters out incomplete interviews, very short sessions, and off-topic conversations. The quality distribution is shown in the report overview so you can see how many interviews were included vs. filtered.\n\n**Can I download the full transcripts?**\nYes. You can view all transcripts in the Recruit tab and export them via the export options. See [Exporting Research Data](/docs/exporting-research-data) for all available formats including CSV and JSON.\n\n**How long does report generation take?**\nTypically 2–5 minutes for studies with fewer than 20 interviews. Larger studies may take longer. You will receive a notification when the report is ready.\n\n## Related Resources\n\n- [Generating Research Reports](/docs/generating-research-reports) — How to generate and refresh your report\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — How structured questions produce charts in your report\n- [Insights Chat Guide](/docs/insights-chat-guide) — Ask natural language questions about your research data\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — How interview quality is calculated and why it matters\n- [Publishing and Sharing Reports](/docs/publishing-sharing-reports) — How to share your findings with stakeholders\n- [Exporting Research Data](/docs/exporting-research-data) — CSV, JSON, and transcript access","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"How to Read Your Koji Research Report: Section-by-Section Guide | Koji","metaDescription":"A complete guide to every section of your Koji research report — themes, quantitative charts, key quotes, sentiment, and Insights Chat — so you can extract maximum value from your AI interview findings.","keywords":["AI research report interpretation","reading qualitative research report","research report sections guide","understanding research themes","AI generated research findings","qualitative report analysis"],"aiSummary":"A Koji research report synthesizes all interview data into an AI-generated document with themes, quantitative charts for structured questions, sentiment analysis, key quotes, and Insights Chat. Reports are generated automatically after 5+ completed interviews and can be refreshed as more data arrives.","aiPrerequisites":["At least one completed Koji study with 5 or more interviews","Familiarity with the Koji study setup process"],"aiLearningOutcomes":["Navigate every section of a Koji research report with confidence","Interpret evidence counts to prioritize themes by prevalence","Read quantitative charts for scale, choice, and ranking questions","Use sentiment filters to segment findings","Use Insights Chat to answer follow-up questions about your data","Share and export research reports for stakeholders"],"aiDifficulty":"beginner","aiEstimatedTime":"10 minutes"},{"type":"documentation","id":"927f196c-87de-44e4-b7c3-09d5fa70828a","slug":"exporting-research-data","title":"Exporting Research Data from Koji: CSV, JSON, and Transcript Access","url":"https://www.koji.so/docs/exporting-research-data","summary":"Koji provides four data export formats: one-click CSV for spreadsheet analysis and CRM uploads, JSON API for full structured analysis including transcripts and confidence scores, in-app transcript viewer with copy support, and a real-time webhook system for automated pipelines. All exports include AI-generated summaries, quality scores, and theme tags. Structured question responses appear as dedicated columns. No credits are consumed by exports.","content":"## Exporting Research Data from Koji: CSV, JSON, and Transcript Access\n\n**The short answer:** Koji gives you complete ownership of your research data. Export participant data and AI-generated summaries as CSV, download raw structured analysis as JSON, access individual transcripts, and pipe data to external systems via API and webhooks — all with a few clicks or a single API call.\n\nYour research data is valuable. The insights you collect through Koji interviews should not live only inside a platform — they need to flow into your product roadmaps, CRM systems, research repositories, stakeholder reports, and analytics pipelines. That is why Koji is built for data portability from the ground up.\n\nThis guide covers every way to get your data out of Koji, when to use each format, and how to build automated pipelines that keep your research connected to the rest of your stack.\n\n---\n\n## Why Data Export Matters in Modern Research\n\nResearch that lives in a silo does not drive decisions. The most effective research teams connect their interview data to:\n\n- **Product roadmaps** — tagging insights to features and epics\n- **CRM systems** — enriching customer profiles with interview notes\n- **Research repositories** — building a searchable archive of findings\n- **BI tools** — creating dashboards that track sentiment and themes over time\n- **Stakeholder reports** — presenting findings in formats teams can consume\n\nWith tools like Koji, all of this is possible without manual copy-pasting. Koji is designed so that data flows to wherever it is needed, in the format that each system requires.\n\n---\n\n## Export Format 1: CSV\n\n**Best for:** Spreadsheet analysis, CRM uploads, research operations reporting\n\nCSV export gives you a flat-file view of your participant data, ideal for quick analysis in Excel, Google Sheets, or Airtable, and for uploading to CRM systems or research repositories.\n\n### What is included in the CSV\n\nWhen you export a study to CSV, Koji includes:\n\n- **Participant metadata**: display name, email (if collected via lead form), source (organic, custom link, CSV import), started and completed timestamps\n- **Session data**: session duration in seconds, message count, interaction mode (voice or text)\n- **AI summary per participant**: the goal-aligned summary of what that participant said\n- **Quality score**: the 1–5 quality score assigned by Koji's analysis engine\n- **Theme tags**: AI-generated tags identifying the main topics discussed\n- **Intake form data**: any fields collected via your lead collection form (name, company, role, etc.)\n- **Structured question responses**: for studies using [structured questions](/docs/structured-questions-guide), each question gets its own column with the participant's response\n\n### How to export CSV\n\n1. Open your study in Koji\n2. Navigate to the **Recruit** tab\n3. Click the **Export** button in the top-right corner\n4. Select **CSV** as your format\n5. Click **Download**\n\nThe file downloads immediately — no waiting, no email link. For large studies (100+ participants), export typically completes within a few seconds.\n\n### Tips for CSV export\n\n- **Filter before exporting**: use the quality score filter to export only high-quality interviews (score 3+) before downloading\n- **Re-run analysis first**: if you recently refreshed your report, re-export CSV to get updated theme tags and summaries\n- **Structured question columns**: studies using structured questions (scale, single_choice, yes_no, etc.) will have quantitative columns that are ideal for pivot tables and distribution analysis\n\n---\n\n## Export Format 2: JSON\n\n**Best for:** Developer workflows, research repositories, custom analysis pipelines, AI tools\n\nJSON export provides the full structured analysis for each participant interview — the same data Koji uses internally to generate reports. It is richer than CSV and includes nested data that does not flatten well into rows and columns.\n\n### What is included in the JSON export\n\nEach participant entry in the JSON export includes:\n\n- All CSV fields (metadata, session data, summary, scores)\n- **Full question-answer mapping**: each key question from your research brief paired with the extracted answer and confidence level\n- **Structured answers**: for structured questions, the full StructuredAnswer object including quantitative value, qualitative answer, follow-up insights, and confidence\n- **Pain points, feature requests, and positive highlights** extracted by the AI\n- **Notable quotes**: direct quotes from the transcript worth highlighting\n- **Topic insights map**: for each topic in your research brief, what the participant said\n- **Evaluation**: whether the participant answered key questions, stayed on topic, and provided depth\n- **Full transcript**: all messages in the conversation, including AI follow-up questions\n\n### How to export JSON\n\nJSON export is available through the Koji API:\n\n```\nGET /api/v1/projects/{project_id}/export?format=json\n```\n\nInclude your API key in the `Authorization: Bearer {api_key}` header. The response is a JSON array of participant analysis objects. See the [API Authentication guide](/docs/api-authentication) for how to generate and manage API keys.\n\nFor programmatic access, JSON export pairs well with webhooks to create real-time pipelines: configure a webhook on `interview.completed` to receive each new interview's full analysis as it happens.\n\n---\n\n## Export Format 3: Transcript Access\n\n**Best for:** Qualitative analysis, research repositories, compliance, stakeholder deep-dives\n\nIndividual transcripts let you read the full conversation between Koji's AI interviewer and each participant. Transcripts are accessible directly in the Koji interface and can be copied for use in analysis tools.\n\n### Accessing transcripts in Koji\n\n1. Open your study and navigate to the **Recruit** tab\n2. Click on any participant row to open the **Analysis Drawer**\n3. Click the **Transcript** tab to view the full conversation\n4. Use the copy button to copy the entire transcript to your clipboard\n\nTranscripts show both the AI's questions and follow-up probes alongside the participant's responses. This gives context that summary data cannot fully capture — tone, hesitation, unexpected tangents, and specific language patterns.\n\n### Using transcripts with AI tools\n\nMany research teams paste transcripts into AI assistants for custom analysis. Koji's transcripts are clean, well-formatted, and export-ready. Note that if your research involves personal data, review your privacy policy before sharing transcripts with third-party AI services. Koji's Insights Chat feature — which lets you ask any question about your data — is a better option for sensitive research since it keeps data within the Koji platform.\n\n---\n\n## Export Format 4: API and Webhooks for Real-Time Pipelines\n\n**Best for:** Automated research operations, CRM enrichment, Slack and Notion notifications\n\nFor teams that want research data flowing automatically into other systems, Koji's API and webhook system enables real-time pipelines that require no manual export steps.\n\n### Webhook events\n\nConfigure webhooks to receive automatic notifications when:\n\n- `interview.completed` — a participant finishes an interview (includes full analysis)\n- `interview.started` — a participant begins an interview\n- `study.published` — your study goes live\n\nWith the `interview.completed` webhook, you can automatically:\n\n- Create a Notion or Confluence page for each new interview\n- Post a Slack summary to your research channel\n- Update a CRM record with the participant's key insights\n- Trigger analysis in a custom data pipeline\n- Archive transcripts to a research repository\n\n### Headless API for embedded research\n\nIf you have embedded Koji interviews in your product using the Headless API, all data is available through the same export mechanisms. Participants from embedded interviews appear in the Recruit tab and are included in all exports.\n\nFor more on API-driven exports, see the [Headless API Overview](/docs/headless-api-overview) and [Webhook Setup](/docs/webhook-setup) guides.\n\n---\n\n## A Complete Research Ops Export Workflow\n\nHere is a common workflow for research teams that need both speed and depth:\n\n1. **During fieldwork**: use the `interview.completed` webhook to post Slack summaries in real time, keeping your team informed as responses come in\n2. **After fieldwork**: export CSV to review participant-level quality scores and filter out low-quality sessions\n3. **For synthesis**: export JSON or access individual transcripts for deep analysis in your research repository\n4. **For reporting**: use Koji's built-in reports (with optional publishing) for stakeholder distribution — no export needed\n\nThis layered approach means you always have the right level of data for each use case, without drowning in raw data you do not need.\n\n---\n\n## Data Ownership and Privacy\n\nKoji does not sell or share your research data. All interview data, transcripts, and AI analysis belong to your account. You can delete studies and all associated data at any time.\n\nFor enterprise teams with specific data residency or retention requirements, contact the Koji team to discuss enterprise data handling options.\n\n---\n\n## Related Resources\n\n- [Structured Questions Guide](/docs/structured-questions-guide) — add quantitative columns to your exports with scale, choice, and ranking questions\n- [Webhook Setup](/docs/webhook-setup) — build real-time research pipelines that push data automatically\n- [Headless API Overview](/docs/headless-api-overview) — programmatic interview management and data access\n- [Managing Research Participants](/docs/managing-research-participants) — the Recruit tab in depth\n- [Research Automation: How to Build Real-Time Research Pipelines](/docs/research-automation-webhooks) — end-to-end automation workflows\n- [Generating Research Reports](/docs/generating-research-reports) — Koji's built-in report system for stakeholder sharing\n","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Exporting Research Data from Koji: CSV, JSON, and Transcript Access","metaDescription":"Learn every way to export your interview data from Koji — CSV downloads, JSON API, transcript access, and real-time webhook pipelines for automated research operations.","keywords":["export user research data","research data export","download interview transcripts","CSV export user research","JSON export qualitative data","research data portability"],"aiSummary":"Koji provides four data export formats: one-click CSV for spreadsheet analysis and CRM uploads, JSON API for full structured analysis including transcripts and confidence scores, in-app transcript viewer with copy support, and a real-time webhook system for automated pipelines. All exports include AI-generated summaries, quality scores, and theme tags. Structured question responses appear as dedicated columns. No credits are consumed by exports.","aiPrerequisites":["understanding-the-research-brief","creating-your-first-study"],"aiLearningOutcomes":["Export participant data as CSV with AI summaries and quality scores","Access the full structured JSON analysis via API","Read and copy individual interview transcripts","Build real-time pipelines using webhooks and the API","Understand when to use each export format"],"aiDifficulty":"intermediate","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"90038630-2888-4de7-9864-335934bd4464","slug":"viewing-interview-transcripts","title":"Viewing Interview Transcripts","url":"https://www.koji.so/docs/viewing-interview-transcripts","summary":"Koji produces a complete transcript for every interview, accessible from the Responses tab. Users can quickly review insights via the Analysis Drawer or navigate to the full transcript page with Insights and Transcript sidebar views. Transcripts include structured answer displays for scale, choice, ranking, and yes/no questions alongside conversational context.","content":"Every interview conducted through Koji produces a complete transcript you can review at any time. Whether your participants used voice or text, you'll find a full record of the conversation — including every question asked and every response given.\n\n## How It Works\n\nOnce a participant completes an interview, Koji automatically generates a readable transcript. For voice interviews, the audio is transcribed and formatted into a clean conversation view. For text interviews, the messages appear exactly as they were typed.\n\nYou can access transcripts from your study's results page. Each completed interview appears as a card showing the participant's name (or anonymous identifier), the date of the interview, and a quality score. Click on any interview card to open the full transcript.\n\n## Navigating to Transcripts\n\n1. **Open your study**\n   Navigate to your dashboard and select the study you want to review. The study results page has three tabs: **Experience**, **Recruit**, and **Responses**.\n\n2. **Go to the Responses tab**\n   The Responses tab lists all completed interviews as cards with key details — the participant identifier, completion date, duration, and quality score.\n\n3. **Open the Analysis Drawer**\n   Click on any interview card to open the **Analysis Drawer** — a slide-out panel that shows the AI-generated insights for that interview. The drawer provides a quick summary with themes, sentiment, key quotes, and structured answer data without leaving the results page.\n\n4. **View the full transcript**\n   For the complete conversation, navigate to the dedicated transcript page at the full interview view. This page provides two views accessible via sidebar navigation: **Insights** and **Transcript**. The Insights view shows the AI analysis including the quality score, while the Transcript view displays the complete conversation.\n\n## What You'll See in a Transcript\n\nEach transcript includes several key elements:\n\n- **The full conversation**: Every question and answer, displayed in chronological order\n- **Timestamps**: When each message was sent, helping you understand pacing and engagement\n- **Quality score**: A rating from 1 to 5 indicating the depth and usefulness of the interview, visible in the Insights view (learn more in [Understanding Quality Scores](/docs/understanding-quality-scores))\n- **AI-generated insights**: Themes, sentiment, and key findings available in the Insights view\n- **Key quotes**: Particularly notable or insightful responses highlighted by the AI\n- **Structured answers**: For interviews using [structured questions](/docs/structured-questions-guide), you'll see the participant's typed responses to scale ratings, multiple-choice selections, rankings, and yes/no answers displayed alongside the conversational context\n\n## Structured Answer Display\n\nWhen your study includes structured question types — scales, single or multiple choice, rankings, or yes/no questions — the transcript shows both the widget interaction and the surrounding conversation. This means you can see:\n\n- The exact rating a participant gave on a scale question (e.g., 7 out of 10)\n- Which options they selected in a choice question\n- How they ranked a set of items\n- Their yes/no response\n\nCritically, you also see the qualitative follow-up: what the AI interviewer asked after the structured response, and how the participant explained their reasoning. This combination of quantitative data and qualitative context is what makes Koji's approach unique.\n\n## Tips for Reading Transcripts Effectively\n\n- **Start with the Analysis Drawer**: Before diving into a full transcript, open the Analysis Drawer from the Responses tab. The AI-generated insights act like a table of contents for the interview. If a theme catches your eye, navigate to the full transcript for context.\n\n- **Use the Insights view as a reading guide**: On the full transcript page, the Insights view in the sidebar shows themes, key quotes, and structured answers. Scan these first, then switch to the Transcript view for the relevant sections.\n\n- **Look for direct quotes**: The most powerful data in qualitative research comes from participants' own words. Pay attention to moments where participants describe experiences, frustrations, or desires in vivid language — these make compelling evidence in reports and presentations.\n\n- **Compare across interviews**: After reading a few transcripts, you'll start noticing patterns. The same frustrations mentioned by different participants, or similar workflows described independently, are strong signals worth investigating further.\n\n- **Don't skip low-scoring interviews entirely**: While high-quality interviews (scoring 3 or above) are more valuable overall, even shorter or less detailed conversations can contain unexpected insights.\n\n## Understanding the Conversation Flow\n\nKoji's AI interviewer adapts its questions based on participant responses. This means each transcript follows a slightly different path. You might notice:\n\n- **Follow-up questions**: When a participant says something interesting, the AI probes deeper with clarifying questions. These moments often produce the richest insights.\n\n- **Structured question widgets**: At certain points, the AI presents interactive widgets — a scale slider, a set of choices, or a ranking interface. The participant's interaction with these widgets appears in the transcript alongside the conversational flow. Learn more about the [text interview experience](/docs/text-interview-experience).\n\n- **Topic transitions**: The conversation moves through different themes defined in your study brief. The AI handles these transitions naturally, so the dialogue reads like a real conversation rather than a rigid questionnaire.\n\n- **Engagement patterns**: Some participants are naturally more talkative than others. The AI adjusts its approach — asking more open-ended questions for brief responders and allowing space for those who share freely.\n\n## From Transcripts to Action\n\nTranscripts are your raw research data. They're the foundation for everything else — the individual [AI-generated insights](/docs/ai-generated-insights), the [themes and patterns](/docs/understanding-themes-patterns) that emerge across interviews, and the aggregate [reports](/docs/generating-research-reports) you can generate for stakeholders.\n\nThink of each transcript as a primary source. When you spot something interesting in a report or insight summary, you can always trace it back to the original conversation to verify context and nuance.\n\n## Key Things to Know\n\n- **Transcripts are permanent**: Once an interview is complete, the transcript is saved and available for as long as your account is active. You can revisit transcripts months later for re-analysis.\n\n- **Voice transcription accuracy**: Voice interviews are transcribed with high accuracy. However, specialized terminology, strong accents, or poor audio quality may occasionally result in minor transcription errors. Always check the original context if a quote seems unclear.\n\n- **Privacy matters**: Transcripts contain participant data. Be mindful of how you share and store this information, especially if your research involves sensitive topics.\n\n## Related Articles\n\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — Learn what makes an interview high-quality\n- [AI-Generated Insights](/docs/ai-generated-insights) — See what analysis Koji produces for each interview\n- [Generating Research Reports](/docs/generating-research-reports) — Turn your transcripts into aggregate research reports\n- [Structured Questions Guide](/docs/structured-questions-guide) — How to use scale, choice, ranking, and yes/no questions\n\n## Frequently Asked Questions\n\n**Q: Can I download or export a transcript?**\nA: You can view transcripts directly in Koji. For sharing content, you can copy the text or use the report generation feature to create a formatted summary. The [data export](/docs/structured-questions-guide) tools also let you extract structured answer data.\n\n**Q: How long does it take for a transcript to appear after an interview?**\nA: Transcripts are available almost immediately after an interview completes. For voice interviews, there may be a brief delay of a few seconds while the audio is processed.\n\n**Q: Are transcripts editable?**\nA: Transcripts are read-only to preserve the integrity of your research data. This ensures that the raw data remains unchanged and can always be referenced as the original source.\n\n**Q: What happens to transcripts if I change my plan?**\nA: Your existing transcripts remain accessible regardless of plan changes. They are part of your completed research data and will not be deleted when you change plans.\n\n**Q: Can I search across all transcripts in a study?**\nA: The best way to find specific content across multiple interviews is to use the AI-generated insights and themes, which aggregate information across all transcripts. You can also use the [research report](/docs/generating-research-reports) to see cross-interview patterns.","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Interview Transcripts — Koji Docs","metaDescription":"Learn how to read, navigate, and extract value from your interview transcripts in Koji. Access full conversation records with AI insights.","keywords":["interview transcripts","qualitative research data","transcript review","voice interview transcription","research analysis","Koji transcripts"],"aiSummary":"Koji produces a complete transcript for every interview, accessible from the Responses tab. Users can quickly review insights via the Analysis Drawer or navigate to the full transcript page with Insights and Transcript sidebar views. Transcripts include structured answer displays for scale, choice, ranking, and yes/no questions alongside conversational context.","aiPrerequisites":["creating-your-first-study"],"aiLearningOutcomes":["Access and navigate interview transcripts","Understand transcript components including quality scores and AI insights","Use transcripts effectively for qualitative research analysis"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"867eea81-89a2-47d6-8a36-aa5822c4ab58","slug":"understanding-quality-scores","title":"Understanding Quality Scores","url":"https://www.koji.so/docs/understanding-quality-scores","summary":"Koji automatically scores every interview from 1 to 5 across five dimensions: relevance, depth, coverage, completion, and structured quality. Only interviews scoring 3 or above count toward billing and are included in research reports. Analysis is auto-triggered on completion.","content":"Every completed interview in Koji receives a quality score from 1 to 5. This score tells you how useful the interview is for your research — and it directly affects your billing, because only interviews scoring 3 or above count toward your monthly limit. Reports also filter to this same threshold, ensuring your aggregate analysis is built from substantive data.\n\n## How Quality Scoring Works\n\nAfter each interview concludes, Koji's AI automatically evaluates the conversation across multiple dimensions to produce a single quality score. The evaluation happens immediately upon interview completion and takes just a few seconds — there is no manual trigger required.\n\nThe score reflects the overall research value of the interview — not the participant's intelligence or effort, but how much actionable data the conversation produced. A high-quality interview is one that gives you clear, detailed, and relevant information you can use to make decisions.\n\n## The 1-5 Scale Explained\n\nQuality scores range from 1 to 5. Here's what each score means in practice:\n\n| Score | Rating | What It Means |\n|-------|--------|---------------|\n| **1** | Poor | The interview produced very little usable data. The participant may have given one-word answers, gone off-topic, or disengaged early. |\n| **2** | Below Average | Some relevant information was shared, but responses lacked depth. There may be a few useful data points, but not enough for confident analysis. |\n| **3** | Good | A solid interview with meaningful responses. The participant engaged with most questions and provided enough detail to identify themes and insights. This is the threshold for billing and report inclusion. |\n| **4** | Very Good | A strong interview with detailed, thoughtful responses. The participant shared personal experiences, examples, and reasoning behind their opinions. |\n| **5** | Excellent | An exceptional interview with rich, nuanced data. The participant went deep on multiple topics, provided vivid examples, and offered perspectives you didn't anticipate. |\n\n## The Five Quality Dimensions\n\nKoji evaluates each interview across five specific dimensions that combine into the overall score:\n\n### Relevance\nHow well the participant's responses addressed the research questions defined in your study brief. On-topic, focused answers score higher than tangential or off-topic responses.\n\n### Depth\nThe level of detail and elaboration in responses. Multi-sentence answers with explanations, examples, and reasoning score higher than brief or surface-level replies.\n\n### Coverage\nHow thoroughly the interview covered the key topics and questions in your study. Interviews where the participant engaged with most or all research areas score higher.\n\n### Completion\nWhether the participant completed the full interview or dropped off early. Finishing the entire conversation contributes positively to the score.\n\n### Structured Quality\nFor studies using [structured questions](/docs/structured-questions-guide) — scales, multiple choice, rankings, yes/no — this dimension evaluates how thoughtfully the participant engaged with those interactive elements. Did they provide considered ratings and selections, or did they rush through? Did they explain their reasoning in follow-up probes?\n\n## Why Quality Scores Matter\n\nQuality scores serve three important purposes:\n\n### 1. Research Data Quality\n\nNot all interviews are equally valuable. A five-minute conversation where a participant gave one-word answers tells you far less than a twenty-minute deep dive. The quality score helps you quickly identify which interviews deserve your attention first and which ones might need to be supplemented with additional data collection.\n\nWhen you're reviewing results, sorting by quality score helps you prioritize your time. Start with the highest-scoring interviews to get the strongest signals, then work your way down.\n\n### 2. Fair Billing Through the Quality Gate\n\nHere's something that works strongly in your favor: **interviews scoring below 3 don't count toward your monthly interview limit**. This is Koji's quality gate, and it exists to protect you.\n\nIf a participant rushes through your interview giving minimal answers, or if someone provides off-topic responses, you shouldn't have to pay for that. The quality gate ensures you're only billed for interviews that actually deliver research value.\n\nLearn more about how this works in [How the Quality Gate Works](/docs/how-the-quality-gate-works).\n\n### 3. Report Filtering\n\nWhen you [generate a research report](/docs/generating-research-reports), Koji filters to only include interviews with a quality score of 3 or above. This ensures your aggregate analysis, theme detection, and recommendations are built from substantive data rather than being diluted by low-quality responses. The results page shows a **report-eligible count** so you know exactly how many interviews will feed into your report.\n\n## Improving Your Quality Scores\n\nWhile you can't control how individual participants respond, you can set the stage for better interviews:\n\n- **Write a clear study brief**: The better your research objectives and target questions are defined, the more relevant the AI interviewer's questions will be. Clear briefs lead to focused conversations.\n\n- **Target the right participants**: People with direct experience in your research topic naturally provide richer, more detailed responses. A product user will give you better feedback about your product than someone who's never used it.\n\n- **Use structured questions strategically**: Adding [structured questions](/docs/structured-questions-guide) like scales and choice selections gives participants concrete ways to express their views, which can boost the structured quality dimension of the score.\n\n- **Set expectations upfront**: Your study description (what participants see before starting) should explain what the interview is about and roughly how long it takes. Prepared participants tend to give more thoughtful answers.\n\n- **Design for engagement**: Studies with interesting, relevant topics naturally produce better interviews. If your research questions connect to things participants genuinely care about, they'll be more willing to share detailed responses.\n\n## Viewing Quality Scores\n\nYou can see quality scores in several places:\n\n- **Responses tab**: Each interview card displays its quality score prominently, making it easy to scan across all interviews at a glance.\n- **Analysis Drawer**: When you click an interview card, the drawer shows the quality score alongside other insights.\n- **Insights view**: On the full transcript page, the Insights sidebar view displays the quality score with its dimensional breakdown (relevance, depth, coverage, completion, structured quality).\n- **Study results overview**: Aggregate quality statistics including average score and distribution appear on the Experience tab.\n\n## Key Things to Know\n\n- **Scores are final**: Quality scores are calculated once after the interview completes and don't change. This ensures consistency in your billing and analysis.\n- **Scores are not participant ratings**: A low score doesn't mean the participant was \"bad.\" It means the conversation didn't produce enough usable research data for various possible reasons.\n- **You can't manually override scores**: The scoring is automated to ensure objectivity and consistency across all interviews.\n- **Analysis is automatic**: You do not need to click a button to trigger scoring. It happens immediately when an interview completes.\n\n## Related Articles\n\n- [How the Quality Gate Works](/docs/how-the-quality-gate-works) — Understanding why low-quality interviews don't count toward your limits\n- [AI-Generated Insights](/docs/ai-generated-insights) — What analysis Koji produces from your interviews\n- [Viewing Interview Transcripts](/docs/viewing-interview-transcripts) — How to read and navigate interview conversations\n- [Structured Questions Guide](/docs/structured-questions-guide) — How structured question types affect the quality score\n- [Generating Research Reports](/docs/generating-research-reports) — How quality filtering shapes your reports\n\n## Frequently Asked Questions\n\n**Q: Can a participant retake an interview to improve the quality score?**\nA: Each interview submission is scored independently. If you share the link again with the same participant, they could complete a new interview, which would receive its own quality score.\n\n**Q: Do low-quality interviews still generate insights?**\nA: Yes, Koji generates AI insights for every completed interview regardless of its quality score. However, the insights from lower-quality interviews will naturally be less detailed and less reliable.\n\n**Q: What if I think a quality score is wrong?**\nA: The scoring evaluates objective factors like response depth, relevance, coverage, completion, and structured quality. If an interview has a lower score than expected, review the transcript — you may find that while the participant said some useful things, overall depth or completeness was limited.\n\n**Q: Does the quality score affect report generation?**\nA: Yes. Research reports only include interviews scoring 3 or above. This quality filter ensures your aggregate analysis is built from substantive data. The results page shows the report-eligible count so you can see how many interviews qualify.\n\n**Q: What's the average quality score across all Koji interviews?**\nA: Quality scores vary significantly by study topic, participant recruitment, and study design. Well-designed studies with targeted participants typically see average scores above 3.","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Quality Scores — Koji Docs","metaDescription":"Understand how Koji rates interview quality on a 0-5 scale. Learn what makes high-quality interviews and why only scores of 3+ count toward billing.","keywords":["interview quality score","qualitative research quality","quality gate","interview evaluation","research data quality","Koji scoring"],"aiSummary":"Koji automatically scores every interview from 1 to 5 across five dimensions: relevance, depth, coverage, completion, and structured quality. Only interviews scoring 3 or above count toward billing and are included in research reports. Analysis is auto-triggered on completion.","aiPrerequisites":["creating-your-first-study"],"aiLearningOutcomes":["Understand the 0-5 quality scoring scale","Identify what factors contribute to higher quality scores","Design studies that encourage higher-quality interviews","Know how quality scores affect billing through the quality gate"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"61204209-c345-478d-8630-45821b65caf1","slug":"ai-generated-insights","title":"AI-Generated Insights","url":"https://www.koji.so/docs/ai-generated-insights","summary":"Koji automatically generates AI insights for every completed interview within seconds, including 3-7 themes, a single sentiment classification (positive/negative/neutral/mixed), engagement level, key quotes, structured answer data, and actionable findings. Insights are accessed via the Analysis Drawer or the full Insights view.","content":"For every completed interview, Koji automatically generates a set of AI-powered insights that summarize the key takeaways. Instead of reading through entire transcripts, you can quickly scan themes, sentiment, notable quotes, structured answer data, and findings to understand what each participant shared.\n\n## What You Get Per Interview\n\nEach completed interview produces several types of analysis, generated automatically within seconds of the interview finishing — no manual trigger required.\n\n### Themes\n\nKoji identifies 3 to 7 main themes per interview. These aren't just keywords — they're conceptual categories that capture what the participant was really talking about. For example, rather than just tagging \"onboarding,\" Koji might identify a theme like \"frustration with initial setup complexity.\"\n\nThemes are extracted based on the substance of the conversation, not just word frequency. If a participant spends significant time discussing a topic, shares emotional reactions, or provides detailed examples, that topic becomes a theme. These per-interview themes feed into [cross-interview theme analysis](/docs/understanding-themes-patterns) as your study grows.\n\n### Sentiment\n\nFor each interview, Koji assigns an overall sentiment classification:\n\n- **Positive**: The participant expressed generally favorable views\n- **Negative**: The participant expressed generally unfavorable views\n- **Neutral**: The participant maintained an even, objective tone\n- **Mixed**: The participant expressed both positive and negative views across different topics\n\nSentiment is a single classification per interview — it captures the overall emotional direction of the conversation. This makes it easy to quickly scan across interviews and spot which participants had notably positive or negative experiences.\n\nSeparately, Koji also assesses the **engagement level** (high, medium, or low) for each interview, which measures how actively the participant engaged with the conversation regardless of sentiment. A participant can be highly engaged while expressing negative sentiment, for example.\n\n### Key Quotes\n\nKoji highlights the most significant quotes from each interview — the moments where participants said something particularly insightful, vivid, or representative. These quotes are selected based on:\n\n- **Relevance** to your research objectives\n- **Specificity** — concrete examples and personal experiences over vague statements\n- **Emotional weight** — moments of strong feeling that reveal true attitudes\n- **Uniqueness** — perspectives or framings you might not have anticipated\n\nKey quotes are invaluable when building presentations, writing reports, or making the case for product decisions. Nothing is more persuasive than a customer's own words.\n\n### Structured Answer Insights\n\nWhen your study uses [structured questions](/docs/structured-questions-guide) — scales, single or multiple choice, rankings, or yes/no questions — Koji captures the participant's typed responses alongside qualitative context. For each structured answer, you see:\n\n- **The quantitative value**: The exact rating, selected option(s), ranking order, or yes/no response\n- **Confidence level**: How confidently the participant answered\n- **Qualitative context**: What the participant said in follow-up probes explaining their answer\n- **Follow-up insights**: Key points from the AI's probing conversation after the structured response\n\nThis combination of hard data and soft context is what makes Koji's structured questions more powerful than a traditional survey. You get the number AND the story behind it.\n\n### Key Findings\n\nBeyond themes and quotes, Koji synthesizes the interview into actionable findings — concise statements about what the participant revealed. These might include:\n\n- Pain points the participant experiences\n- Unmet needs or desires\n- Behavioral patterns described by the participant\n- Comparisons to competitors or alternatives\n- Suggestions or feature requests\n\n## How to Access Insights\n\n1. **Navigate to your study's results page**\n   Open the study from your dashboard and go to the **Responses** tab.\n\n2. **Open the Analysis Drawer**\n   Click on any completed interview card. The **Analysis Drawer** slides open from the right, showing the AI-generated insights for that interview without leaving the results page.\n\n3. **Review themes, sentiment, quotes, and structured answers**\n   The drawer organizes insights into clear sections. You can quickly scan the analysis and decide whether to dig deeper.\n\n4. **Navigate to the full transcript**\n   For complete context, navigate to the dedicated interview page where you can switch between the **Insights** view and the **Transcript** view using the sidebar navigation.\n\n## Using Insights to Inform Decisions\n\nIndividual interview insights are powerful, but they become transformative when you look across multiple interviews:\n\n### Spot Patterns Early\n\nAfter just three or four interviews, start checking whether the same themes appear across different participants. When unrelated people independently raise the same issue, you've found a strong signal. Koji helps by tracking [themes and patterns](/docs/understanding-themes-patterns) across all interviews in a study.\n\n### Prioritize by Sentiment\n\nNot all themes are equally urgent. If most interviews discussing a particular topic carry negative sentiment and high engagement, that's likely a more pressing issue than one mentioned with neutral sentiment and low engagement. Use sentiment data to rank the importance of your findings.\n\n### Build Evidence with Quotes\n\nWhen presenting findings to stakeholders, abstract summaries often fall flat. Key quotes bring research to life. Use the highlighted quotes from your insights to create compelling evidence for your recommendations. A direct participant quote carries more weight than a researcher's interpretation.\n\n### Leverage Structured Data\n\nWhen your study includes structured questions, you can compare quantitative responses across interviews even before generating a report. If five out of eight participants rate their satisfaction below 5 on a 10-point scale, that's a clear signal — and each low rating comes with qualitative context explaining why.\n\n### Triangulate Findings\n\nCombine insights across interviews to build confidence. A finding supported by quotes from five different participants is much stronger than one from a single interview. Koji's [research reports](/docs/generating-research-reports) do this triangulation automatically, but you can also do it manually as you review individual insights.\n\n## Tips & Best Practices\n\n- **Review insights before reading full transcripts**: The Analysis Drawer gives you a roadmap for each interview. If a theme catches your eye, navigate to the full transcript for context.\n\n- **Look for contradictions**: When two participants express opposing views on the same topic, that's often where the most interesting insights hide. Contradictions can reveal different user segments, contexts, or needs.\n\n- **Don't ignore outliers**: A theme that appears in only one interview might still be valuable. Sometimes the most innovative insights come from a single participant who sees things differently.\n\n- **Combine AI insights with your domain expertise**: Koji's AI provides a thorough, unbiased first pass at the data. But you bring context the AI doesn't have — your knowledge of business strategy, technical constraints, and market dynamics. The best research outcomes happen when AI analysis meets human judgment.\n\n## Key Things to Know\n\n- **Insights are generated for every completed interview**, regardless of quality score. However, higher-quality interviews naturally produce richer, more detailed insights.\n- **Insights are generated automatically** when an interview completes — you don't need to trigger them manually. They're ready within seconds.\n- **Insights don't replace transcripts** — they complement them. Always reference the original transcript when you need full context or exact wording.\n- **Only interviews scoring 3+ are included in reports** — while all interviews get individual insights, the aggregate [research report](/docs/generating-research-reports) filters to quality score 3 and above.\n\n## Related Articles\n\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — How interview quality affects the insights you receive\n- [Generating Research Reports](/docs/generating-research-reports) — Aggregate insights across all interviews into a research report\n- [Understanding Themes & Patterns](/docs/understanding-themes-patterns) — How Koji identifies recurring themes across multiple interviews\n- [Structured Questions Guide](/docs/structured-questions-guide) — Design structured questions for richer data collection\n- [Text Interview Experience](/docs/text-interview-experience) — How participants interact with widgets during interviews\n\n## Frequently Asked Questions\n\n**Q: Can I edit or override AI-generated insights?**\nA: Insights are generated automatically and cannot be manually edited. This preserves objectivity. You can, however, add your own interpretations when building reports or presentations.\n\n**Q: How quickly are insights generated after an interview?**\nA: Insights are generated automatically within seconds of an interview completing. No manual action is required.\n\n**Q: Are insights available on all plans?**\nA: Yes, AI-generated insights per interview are available on every plan, including Free. All features are available on all plans — credits are the only gate. See the [Plan Comparison Guide](/docs/plan-comparison-guide) for details.\n\n**Q: What if the AI misidentifies a theme?**\nA: AI analysis is highly accurate but not infallible. Always cross-reference insights with the original transcript. If a theme seems off, the transcript will reveal whether the AI misinterpreted context or whether there's genuine nuance worth exploring.\n\n**Q: Do insights improve as more interviews are completed?**\nA: Individual interview insights are generated independently. However, cross-interview analysis — like theme frequency and pattern detection — naturally becomes more robust with more data points.","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"AI-Generated Insights — Koji Docs","metaDescription":"Learn what AI-powered analysis Koji produces per interview: themes, sentiment, key quotes, and findings. Use insights to make better research decisions.","keywords":["AI insights","interview analysis","qualitative research insights","theme extraction","sentiment analysis","key quotes","research findings"],"aiSummary":"Koji automatically generates AI insights for every completed interview within seconds, including 3-7 themes, a single sentiment classification (positive/negative/neutral/mixed), engagement level, key quotes, structured answer data, and actionable findings. Insights are accessed via the Analysis Drawer or the full Insights view.","aiPrerequisites":["creating-your-first-study","viewing-interview-transcripts"],"aiLearningOutcomes":["Understand all insight types generated per interview","Use themes and sentiment to prioritize findings","Leverage key quotes for stakeholder presentations","Combine AI insights with domain expertise for better decisions"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"43aec260-cf68-49f5-be0a-fd2d01cf7e92","slug":"insights-chat-guide","title":"Insights Chat: Ask Any Question About Your Research Data with AI","url":"https://www.koji.so/docs/insights-chat-guide","summary":"Insights Chat is Koji's conversational AI interface for querying collected research data. It synthesizes themes, retrieves supporting quotes, answers quantitative questions from structured data, and generates stakeholder summaries. Available in the Insights tab after report generation. Works best with structured questions (scale, choice, ranking, yes/no) for precise quantitative answers combined with qualitative synthesis. Available on Insights plan and above.","content":"\n# Insights Chat: Ask Any Question About Your Research Data with AI\n\nThe Insights Chat is an AI-powered query interface built into Koji that lets you ask natural-language questions about your collected research data. Instead of re-reading every transcript or waiting for a full report refresh, you can type \"What did B2B customers say about pricing?\" and get an instant, evidence-backed answer with supporting quotes.\n\nThink of it as a research analyst who has read every transcript in your study and is always available to answer follow-up questions.\n\n## How Insights Chat Works\n\nWhen you generate a report for your study, Koji processes all completed interviews, extracts themes, maps answers to your structured questions, and builds an indexed knowledge base from your transcripts. The Insights Chat draws on this knowledge base to answer your questions.\n\nUnlike a simple keyword search, the Chat understands context and intent. It can:\n\n- **Synthesize patterns across multiple interviews**: \"What were the most common reasons for churn?\"\n- **Surface specific quotes to support a claim**: \"Show me quotes about onboarding confusion\"\n- **Compare responses across segments**: \"How did enterprise customers describe pricing versus SMB customers?\"\n- **Answer quantitative questions from structured data**: \"What percentage of participants rated satisfaction 8 or above?\"\n- **Generate stakeholder-ready summaries**: \"Give me a two-paragraph executive summary of the key findings\"\n\nThe Chat is grounded in your actual research data. Every answer it provides is traceable back to specific interviews and quotes — not hallucinated or generalized from outside sources.\n\n## Accessing Insights Chat\n\nInsights Chat is available in the **Insights** tab of any study that has completed interviews and a generated report.\n\nRequirements:\n- At least 1 completed interview that passed the quality gate\n- A generated report (or refreshed report for studies with recent completions)\n- Insights plan or higher\n\nLook for the chat input at the bottom of the Insights Dashboard. Type your question and press Enter.\n\n## What to Ask: Example Queries by Category\n\n### Theme Discovery\n- \"What were the top 3 pain points participants mentioned?\"\n- \"What themes came up most frequently across all interviews?\"\n- \"Were there any surprising or unexpected findings?\"\n- \"What topics did participants bring up that I did not ask about?\"\n\n### Quote Retrieval\n- \"Show me quotes about the onboarding experience\"\n- \"What did participants say about our pricing?\"\n- \"Find me the most compelling quote that illustrates the main friction point\"\n- \"Show me a quote I could use in a stakeholder presentation\"\n\n### Segment Analysis (requires participant attributes from personalized links)\n- \"Did enterprise users have different concerns than small business users?\"\n- \"How did responses differ between users who completed onboarding and those who did not?\"\n- \"Which participant segment expressed the most urgency around this problem?\"\n\n### Structured Data Queries (requires structured questions in the study)\n- \"What was the average satisfaction score?\"\n- \"What percentage of participants answered yes to the referral question?\"\n- \"Which feature option was ranked highest overall?\"\n- \"Show me the distribution of scores on the ease-of-use question\"\n\n### Hypothesis Testing\n- \"Did participants confirm the hypothesis that checkout flow is confusing?\"\n- \"Is there evidence that pricing is a barrier to expansion?\"\n- \"How strong is the signal on the feature request theme?\"\n\n### Stakeholder Preparation\n- \"Summarize the three most important findings in two sentences each\"\n- \"Give me a one-paragraph executive summary\"\n- \"What are the top three recommendations based on the data?\"\n- \"What questions might stakeholders ask, and what do the data say?\"\n\n## Insights Chat vs. Research Reports: When to Use Each\n\nResearch reports are structured, visual documents designed for sharing — they display distributions, aggregate themes, and selected quotes in a formatted layout optimized for presentations and stakeholder reviews.\n\nInsights Chat is conversational and exploratory — it is for the researcher, not the audience. Use it to:\n\n- **Investigate a hunch before adding it to a formal report** — is this a pattern or an outlier?\n- **Answer a stakeholder question on the fly** — even during a live presentation\n- **Dig into a segment or edge case** that the structured report does not highlight\n- **Cross-reference findings from multiple angles** without generating a new report\n\nThe two tools complement each other. Use the report for communication; use the Chat for investigation and discovery.\n\n## Structured Questions Make Insights Chat Dramatically More Powerful\n\nWhen your study includes [structured questions](/docs/structured-questions-guide), the Insights Chat can answer quantitative questions with precision instead of estimation.\n\nKoji supports six structured question types:\n\n| Type | Chat Query Example | Output |\n|------|-------------------|--------|\n| **scale** | \"What was the average NPS score?\" | Exact average and distribution |\n| **single_choice** | \"What was the most common answer to the upgrade question?\" | Frequency ranking |\n| **multiple_choice** | \"Which features were selected most often?\" | Ranked frequency list |\n| **ranking** | \"What was the highest-ranked priority?\" | Average position per item |\n| **yes_no** | \"What percentage said yes to the referral question?\" | Exact percentage |\n| **open_ended** | \"What did participants say about support quality?\" | Thematic synthesis with quotes |\n\nCombining quantitative precision with qualitative depth is what makes AI-native research fundamentally different from traditional survey tools. You get the statistical clarity of a structured questionnaire and the nuanced texture of an in-depth interview — in one study, queryable in seconds.\n\n## Practical Workflow: Using Chat During a Stakeholder Review\n\nYou are presenting findings to your product leadership team. The CEO asks a question you did not specifically prepare for: \"What did early-stage customers say about the API documentation specifically?\"\n\nWithout Insights Chat, you would say \"I will follow up on that.\" With Insights Chat open on a second screen, you type the question and get an answer grounded in your actual research within seconds — with supporting quotes you can read aloud.\n\nThis is the difference between research as a static document and research as a living, queryable knowledge base.\n\n## Practical Workflow: Competitive Intelligence from Customer Interviews\n\nYou have run 40 customer discovery interviews. You want to understand the competitive landscape from your customers' perspective. Ask the Chat: \"Which competitors did participants mention, and what did they say about them?\"\n\nThe Chat synthesizes competitive mentions across all 40 transcripts, giving you a clear picture without manually reviewing each transcript. What would have been a 2-hour analysis task becomes a 30-second query.\n\n## Best Practices\n\n**Ask specific questions.** \"What did people say about pricing?\" works, but \"What specific concerns did participants raise about annual contract pricing?\" works better.\n\n**Reference your research goals.** The Chat knows your study brief. Leverage that: \"Given our goal of understanding churn drivers, what is the most important finding in this data?\"\n\n**Iterate conversationally.** If the first answer is too broad, drill down: \"You mentioned support friction — give me quotes specifically about response times rather than resolution quality.\"\n\n**Refresh your report before querying new data.** If you have added interviews since your last report generation, run a report refresh so the Chat has access to the latest data.\n\n**Use Chat for segment exploration, reports for segment presentation.** Chat is faster for discovery; reports are better for structured visual comparisons.\n\n## What Insights Chat Does Not Do\n\nInsights Chat is a research analysis tool, not a general AI assistant. It will not:\n\n- Generate insights about topics not covered in your research data\n- Make statistical claims from samples smaller than 5 interviews (it will flag low sample sizes)\n- Replace the judgment of a trained researcher about research validity\n- Search the web or draw on information outside your study\n\nEvery answer is grounded in your collected interviews. If a topic was not discussed in your research, the Chat will tell you rather than fabricate an answer.\n\n## Availability and Plans\n\nInsights Chat is available on the **Insights plan** and above. Free plan users can view the basic report summary but cannot use the conversational query interface.\n\nOn the Interviews plan, Insights Chat is available with no session limits.\n\n## Related Resources\n\n- [Structured Questions Guide](/docs/structured-questions-guide) — Add quantitative question types that make Chat queries more precise and powerful\n- [Generating Research Reports](/docs/generating-research-reports) — The underlying data layer that powers Insights Chat\n- [AI-Generated Insights](/docs/ai-generated-insights) — How Koji automatically surfaces themes and patterns from your interviews\n- [Understanding Themes and Patterns](/docs/understanding-themes-patterns) — What the AI extracts from your interview data\n- [Turning Interviews Into Insights](/docs/turning-interviews-into-insights) — From raw transcripts to actionable conclusions\n- [Insights Dashboard](/docs/insights-dashboard) — The complete Insights feature set overview\n","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Insights Chat: Query Your Research Data with AI | Koji Docs","metaDescription":"Ask natural-language questions about your qualitative research data. Surface themes, retrieve quotes, compare segments, and answer stakeholder questions instantly — without re-reading transcripts.","keywords":["AI insights chat","query research data AI","analyze qualitative research AI","chat with research data","AI research analysis tool","qualitative data query tool"],"aiSummary":"Insights Chat is Koji's conversational AI interface for querying collected research data. It synthesizes themes, retrieves supporting quotes, answers quantitative questions from structured data, and generates stakeholder summaries. Available in the Insights tab after report generation. Works best with structured questions (scale, choice, ranking, yes/no) for precise quantitative answers combined with qualitative synthesis. Available on Insights plan and above.","aiPrerequisites":["At least one completed study with a generated report","Insights plan or higher"],"aiLearningOutcomes":["Use Insights Chat to query research themes and patterns","Retrieve supporting quotes for specific topics","Run segment comparisons using participant attributes","Get quantitative answers from structured question data","Generate stakeholder-ready summaries on demand"],"aiDifficulty":"beginner","aiEstimatedTime":"6 minutes"},{"type":"documentation","id":"3941638c-f85d-4607-9309-7a9e05ec2ea6","slug":"generating-research-reports","title":"Generating Research Reports","url":"https://www.koji.so/docs/generating-research-reports","summary":"Koji generates aggregate research reports from interviews scoring 3+ on quality, producing executive summaries, key takeaways, theme analysis with traceable citations, charts (including structured question visualizations for scales, choices, rankings, and yes/no), written findings, recommendations, and stat cards. Available on all plans using credits.","content":"Koji's research reports pull together findings from all the qualifying interviews in a study into a single, structured document. Instead of reading every transcript individually, you get an executive summary, key takeaways, theme analysis, traceable charts, written findings, statistics, and actionable recommendations — ready to share with your team.\n\n## Quality Filter: Only Substantive Data\n\nAn important detail: reports only include interviews with a [quality score](/docs/understanding-quality-scores) of 3 or above. This quality filter ensures your aggregate analysis is built from substantive data rather than being diluted by low-quality responses. The results page shows a **report-eligible count** so you always know how many interviews will feed into your report.\n\nLearn more about quality filtering in [How the Quality Gate Works](/docs/how-the-quality-gate-works).\n\n## What's in a Research Report\n\nEach generated report includes several sections designed to give you a complete picture of your research findings:\n\n### Executive Summary\n\nA concise overview of the most important findings from your study. The executive summary distills your qualifying interviews into key takeaways that anyone can understand in two minutes. It's written for stakeholders who need the bottom line without wading through details.\n\n### Key Takeaways\n\nCritical, high, and medium priority findings extracted from the data. Each takeaway is a specific, actionable insight ranked by importance and backed by evidence from the interviews.\n\n### Theme Analysis\n\nThe report identifies the most prominent themes across all qualifying interviews, with frequency data and traceable citations. Each theme includes:\n\n- **Description**: What the theme represents and why it matters\n- **Frequency**: How many interviews touched on this theme\n- **Supporting quotes**: Direct participant quotes that illustrate the theme, with citation links back to the source interview\n\nThemes in the report aren't just a list of topics — they're synthesized findings that connect what multiple participants said into coherent narratives. Learn more in [Understanding Themes & Patterns](/docs/understanding-themes-patterns).\n\n### Charts and Visualizations\n\nReports include several types of traceable charts, each linked back to the source interviews:\n\n- **Pie charts**: Sentiment distribution across interviews (positive, neutral, negative, mixed)\n- **Horizontal bar charts**: Pain point frequency, feature request frequency, positive highlight frequency\n- **Vertical bar charts**: Option frequency for choice questions\n- **Distribution charts**: Quality score distribution, scale response distributions with mean, median, and mode\n- **Quote citations**: Notable quotes with attribution to specific interviews\n\n#### Structured Question Charts\n\nIf your study uses [structured questions](/docs/structured-questions-guide), reports produce rich quantitative visualizations:\n\n- **Scale questions** produce distribution charts showing how participants rated each item, with calculated mean, median, and mode. For 0-10 scales, Koji automatically calculates an NPS (Net Promoter Score), categorizing respondents into promoters, passives, and detractors.\n- **Single and multiple choice questions** produce bar charts showing option frequency and percentages — how many participants selected each option.\n- **Ranking questions** produce average position charts showing where each item landed in participants' rankings.\n- **Yes/No questions** produce pie charts showing the binary distribution of responses.\n\nEvery chart includes traceable citations linking back to the source interviews, so stakeholders can click through to verify any data point. This gives your team the quantitative charts they need, backed by qualitative context from the AI conversation.\n\n### Written Findings\n\nA detailed narrative section that expands on the key takeaways with full context, evidence, and analysis. Written findings provide the depth that executives skip but product managers and researchers rely on.\n\n### Recommendations\n\nBased on the patterns found across interviews, the report suggests concrete actions you could take. These recommendations are grounded in participant data and connected to specific themes and quotes, so you can trace each suggestion back to its evidence.\n\n### Stat Cards\n\nSummary statistics presented as visual cards, giving you an at-a-glance overview of key metrics from the study.\n\n### Question Coverage\n\nA breakdown of how well each research question from your study brief was covered across interviews. This helps you identify gaps — if a particular question was barely addressed, you may need more interviews or a revised approach.\n\n## How to Generate a Report\n\n1. **Complete enough qualifying interviews**\n   Reports work best with sufficient data. While you can generate a report with just a few interviews, the analysis becomes more robust with more participants. Most researchers find that meaningful patterns emerge after 5-8 quality-qualifying interviews (scoring 3+).\n\n2. **Navigate to your study's report section**\n   Open your study from the dashboard and look for the Report tab or Generate Report button.\n\n3. **Click Generate Report**\n   Koji's AI will analyze all qualifying interviews in the study, identify cross-cutting themes, select the best supporting quotes, calculate statistics, generate charts, and produce a structured report. This typically takes 30 to 60 seconds depending on the number of interviews.\n\n4. **Review the generated report**\n   Once complete, the report appears with all sections described above. Take time to read through it and cross-reference findings with individual transcripts where needed.\n\n## Report Versioning\n\nEach time you generate a report, Koji creates a new version. This is important for several reasons:\n\n- **Progressive research**: As new interviews come in, you can generate an updated report that incorporates the latest data. The previous version is preserved with its version number.\n- **Point-in-time snapshots**: Each version captures the state of your research at a specific moment. A version marked as \"current\" (via the isCurrent flag) is the latest one.\n- **No data loss**: Generating a new report never overwrites or deletes a previous version. You always have access to your full report history.\n\n## Plan Access\n\nResearch reports are available on all plans. Credits are the only gate — generating or refreshing a report costs credits from your balance. Check the [Plan Comparison Guide](/docs/plan-comparison-guide) for credit costs and allocations across plans.\n\n| Plan | Report Access |\n|------|---------------|\n| **Free** | Available (uses credits from one-time allocation) |\n| **Insights** | Available (uses monthly credits) |\n| **Interviews** | Available (uses monthly credits) |\n| **Enterprise** | Available (uses monthly credits) |\n\n## Tips for Better Reports\n\n- **Wait for enough qualifying data**: While you *can* generate a report after just a few interviews, waiting until you have at least 5-8 interviews scoring 3+ produces more reliable and convincing results. Theme patterns need repetition to be meaningful.\n\n- **Add structured questions for richer charts**: Studies with [structured questions](/docs/structured-questions-guide) produce reports with quantitative visualizations — scale distributions, choice breakdowns, ranking charts — that give stakeholders the data-driven evidence they expect.\n\n- **Review your study brief before generating**: Make sure your research objectives are clearly defined in the study brief. The report's analysis is anchored to those objectives, so a clear brief produces a more focused report.\n\n- **Use reports iteratively**: Generate a report mid-study to check for emerging patterns. Use those early findings to refine your approach, then generate a final report when all interviews are complete.\n\n- **Combine with individual insights**: Reports give you the big picture. For specific details, always refer back to [individual AI-generated insights](/docs/ai-generated-insights) and transcripts.\n\n- **Share thoughtfully**: Reports are designed for stakeholders, but add your own context when presenting. You know the business situation, competitive landscape, and strategic priorities better than any AI. See [Publishing & Sharing Reports](/docs/publishing-sharing-reports) for sharing options.\n\n## Key Things to Know\n\n- **Reports filter to quality 3+**: Only interviews scoring 3 or above on the [quality scale](/docs/understanding-quality-scores) are included in report analysis. This ensures your findings are built from substantive data.\n- **Report generation takes a moment**: Complex studies with many interviews may take 30-60 seconds to process.\n- **Reports complement, not replace, individual analysis**: The aggregate view is powerful, but always dig into individual transcripts for nuance and context.\n- **Traceable citations**: Every theme, quote, and chart data point links back to its source interview, enabling full traceability.\n\n## Related Articles\n\n- [AI-Generated Insights](/docs/ai-generated-insights) — Per-interview analysis that feeds into aggregate reports\n- [Understanding Themes & Patterns](/docs/understanding-themes-patterns) — How recurring themes are identified across interviews\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — How quality filtering shapes your reports\n- [How the Quality Gate Works](/docs/how-the-quality-gate-works) — The quality threshold that protects your data\n- [Plan Comparison Guide](/docs/plan-comparison-guide) — Compare plans to see credit allocations\n- [Publishing & Sharing Reports](/docs/publishing-sharing-reports) — Make your reports accessible to stakeholders\n- [Structured Questions Guide](/docs/structured-questions-guide) — Design questions that produce rich report visualizations\n\n## Frequently Asked Questions\n\n**Q: Can I generate a report with just one or two interviews?**\nA: Technically yes, but the report will have limited value. With only one or two data points, there aren't enough interviews to identify meaningful cross-cutting themes. We recommend at least five qualifying interviews for a useful report.\n\n**Q: Does generating a new report delete the previous one?**\nA: No. Each generation creates a new version. All previous versions remain accessible, giving you a history of how your findings evolved over time.\n\n**Q: Why are some interviews excluded from my report?**\nA: Reports only include interviews with a quality score of 3 or above. Check the report-eligible count on your results page to see how many interviews qualify. Low-scoring interviews still have individual insights but are excluded from aggregate analysis.\n\n**Q: Can I customize what appears in the report?**\nA: Reports are generated based on your study's research objectives and all qualifying interviews. The AI determines the most relevant themes, quotes, and recommendations based on the data.\n\n**Q: Can I export or share my report?**\nA: Yes, reports can be published with a shareable public URL. See [Publishing & Sharing Reports](/docs/publishing-sharing-reports) for details.","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Research Reports — Koji Docs","metaDescription":"Learn how to generate comprehensive research reports in Koji. Aggregate interviews into summaries, themes, recommendations, and statistics.","keywords":["research reports","qualitative research report","aggregate analysis","interview synthesis","theme analysis","Koji reports","report generation"],"aiSummary":"Koji generates aggregate research reports from interviews scoring 3+ on quality, producing executive summaries, key takeaways, theme analysis with traceable citations, charts (including structured question visualizations for scales, choices, rankings, and yes/no), written findings, recommendations, and stat cards. Available on all plans using credits.","aiPrerequisites":["creating-your-first-study","ai-generated-insights"],"aiLearningOutcomes":["Generate an aggregate research report from interviews","Understand report sections: summary, themes, recommendations, statistics","Use report versioning for iterative research","Know plan requirements and report generation limits"],"aiDifficulty":"intermediate","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"098e995c-dd7a-4f20-be0b-1a244bcc428a","slug":"understanding-themes-patterns","title":"Understanding Themes & Patterns","url":"https://www.koji.so/docs/understanding-themes-patterns","summary":"Koji automatically identifies 3-7 themes per interview using AI-powered thematic analysis. Themes are aggregated across interviews with frequency data and traceable citations linking back to source interviews. Sentiment is tracked per-interview, not per-theme. Theme names are AI-generated and cannot be manually created or edited.","content":"Themes are the backbone of qualitative research. When multiple participants independently raise the same issue, describe the same experience, or express the same need, that's a pattern worth paying attention to. Koji automatically identifies these recurring themes across all interviews in your study, saving you hours of manual coding and analysis.\n\n## How Koji Identifies Themes\n\nAfter each interview completes, Koji's AI automatically analyzes the conversation and tags it with 3 to 7 relevant themes. As more interviews are completed, the system identifies which themes appear repeatedly and tracks their frequency and supporting evidence.\n\nThis process mirrors what qualitative researchers call **thematic analysis** — a well-established research method where you systematically identify, organize, and interpret patterns in qualitative data. The difference is that Koji does the initial coding pass automatically, giving you a head start on synthesis.\n\nUnlike simple keyword matching, Koji's theme detection understands context. Two participants might use completely different words to describe the same underlying issue. For example, one might say \"the setup process was confusing\" while another says \"I couldn't figure out how to get started.\" Koji recognizes both as expressions of the same theme: onboarding difficulty.\n\n## What Theme Data Looks Like\n\nFor each identified theme across your study, you'll see:\n\n### Theme Name and Description\n\nA clear, concise label that captures what the theme is about, along with a brief description of what it encompasses. These labels are AI-generated and designed to be immediately understandable, even to someone who hasn't read the transcripts.\n\n### Frequency with Citations\n\nHow many interviews mentioned this theme, with traceable citations linking back to the source interviews. This is one of your strongest signals. A theme that appears in 8 out of 10 interviews carries far more weight than one that appeared once.\n\nTheme frequency helps you prioritize. In product research, for example, a usability issue mentioned by 70% of participants is almost certainly more impactful than one mentioned by 10%. Each frequency count is backed by specific citations — you can click through to the original interviews to verify context.\n\n### Supporting Quotes\n\nDirect quotes from participants that illustrate the theme. These aren't randomly selected — Koji picks the most vivid, specific, and representative quotes for each theme, with attribution to the source interview. Supporting quotes serve two purposes:\n\n- **Verification**: You can confirm that the theme accurately reflects what participants said\n- **Persuasion**: When presenting findings, real quotes from real users are far more compelling than abstract summaries\n\n## How Themes Are Aggregated\n\nKoji's theme aggregation works through a frequency-based approach with semantic citation matching:\n\n1. **Per-interview tagging**: Each completed interview is tagged with 3-7 themes based on the conversation content.\n2. **Cross-interview aggregation**: Themes are aggregated across all interviews, counting how frequently each theme appears.\n3. **Citation linking**: Each theme occurrence is linked back to the specific interview using keyword-overlap scoring, ensuring that citations are relevant and traceable.\n4. **Report integration**: When you [generate a research report](/docs/generating-research-reports), themes with their frequency data and citations form the core of the theme analysis section.\n\nThis approach ensures that themes reflect genuine patterns in your data rather than artifacts of a single interview.\n\n## Reading Theme Patterns\n\nThemes don't exist in isolation. The most valuable analysis comes from understanding how themes relate to each other:\n\n### Theme Clusters\n\nSome themes naturally group together. For example, \"difficulty finding features,\" \"unclear navigation labels,\" and \"too many clicks to complete a task\" might all be part of a larger usability cluster. When you see related themes appearing together, you're looking at a systemic issue rather than isolated complaints.\n\n### Theme Contradictions\n\nSometimes different participant groups express opposing views on the same topic. New users might find a feature confusing while power users love it. These contradictions are incredibly valuable because they reveal segmentation in your user base and suggest that a one-size-fits-all approach may not work.\n\n### Theme Evolution\n\nIf you're running ongoing research, themes can shift over time. A theme that dominated early interviews might fade as newer participants focus on different concerns. Tracking this evolution helps you stay current with user needs.\n\n## Using Themes for Decision-Making\n\nThemes become powerful when you connect them to action:\n\n### Product Prioritization\n\nMap themes to your product roadmap. If \"difficulty with onboarding\" is your most frequent theme, that's a clear signal to prioritize onboarding improvements. Themes give you evidence-based ammunition for prioritization discussions.\n\n### Stakeholder Communication\n\nThemes provide a natural structure for presenting research findings. Instead of sharing a wall of interview notes, you can present three to five key themes, each backed by frequency data and supporting quotes. This format is digestible for executives, designers, and engineers alike. [Research reports](/docs/generating-research-reports) present themes in this stakeholder-ready format automatically.\n\n### Hypothesis Validation\n\nIf you started your study with specific hypotheses — \"We think users struggle with our pricing page\" — themes let you validate or invalidate those assumptions with real data. The presence or absence of relevant themes tells you whether your hypothesis held up.\n\n### Identifying Opportunities\n\nThemes aren't always about problems. Positive themes — features people love, experiences that delight — are equally valuable. They tell you what to protect and amplify in your product, not just what to fix.\n\n## Tips & Best Practices\n\n- **Wait for saturation**: In qualitative research, \"saturation\" means you've heard enough to stop learning new things. If the same themes keep appearing in new interviews without any new themes emerging, you've likely reached saturation. Most studies reach this point between 8 and 15 interviews.\n\n- **Don't over-index on frequency alone**: A theme mentioned by 9 out of 10 participants is clearly important. But a theme mentioned by only 2 out of 10 might be equally valuable if those two participants represent a key user segment or if the theme reveals a critical edge case.\n\n- **Cross-reference with interview sentiment**: While themes themselves don't carry individual sentiment labels, you can cross-reference themes with the overall sentiment of the interviews where they appeared. A theme that shows up primarily in negatively-sentiment interviews signals a pain point, while one appearing in positive interviews highlights a strength.\n\n- **Look beyond your brief**: Sometimes the most interesting themes are ones you didn't ask about. Participants may raise topics outside your original research questions that turn out to be critically important.\n\n- **Trace back to transcripts**: When a theme feels important, go back to the source. Read the relevant sections of the transcripts to understand the full context. Themes are summaries — transcripts are the evidence.\n\n## Key Things to Know\n\n- **Themes update as interviews arrive**: Each new interview adds data to the theme analysis. Themes may shift in frequency and new themes may emerge as your sample grows.\n- **Theme names are AI-generated**: The labels are designed to be descriptive and clear. They cannot be manually renamed or edited — this preserves consistency and objectivity across the analysis.\n- **Themes are per-interview, not editable**: Koji generates 3-7 theme tags per interview automatically. You cannot manually create, rename, or delete themes.\n- **Themes feed into reports**: When you [generate a research report](/docs/generating-research-reports), the report's theme section is built from this same underlying analysis, presented in a stakeholder-ready format with traceable citations.\n- **No per-theme sentiment**: Sentiment is tracked at the interview level (positive, negative, neutral, mixed), not per individual theme. To understand sentiment around a theme, look at the sentiment of interviews where that theme appears.\n\n## Related Articles\n\n- [AI-Generated Insights](/docs/ai-generated-insights) — Per-interview themes that feed into cross-interview patterns\n- [Generating Research Reports](/docs/generating-research-reports) — Aggregate theme analysis in a shareable report format\n- [Structured Questions Guide](/docs/structured-questions-guide) — How structured questions complement thematic analysis\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — How quality scores affect which interviews contribute to analysis\n\n## Frequently Asked Questions\n\n**Q: How many themes does Koji typically identify per study?**\nA: It depends on the breadth of your study and the number of interviews. Each interview is tagged with 3-7 themes. A focused study might surface 5-8 major cross-interview themes, while a broader exploratory study could produce 15-20.\n\n**Q: Can I create or rename themes manually?**\nA: No. Theme identification is fully automated to ensure consistency and objectivity. The AI-generated theme names are designed to be descriptive and immediately understandable. Manual theme creation or editing is not supported.\n\n**Q: How many interviews do I need before themes are reliable?**\nA: You'll start seeing theme patterns after 3-4 interviews, but reliability increases significantly with 6-8 or more. The more interviews contribute to a theme, the more confident you can be that it represents a genuine pattern.\n\n**Q: Are themes weighted by quality score?**\nA: Reports filter to interviews scoring 3 or above, so themes in reports only reflect qualifying interviews. The per-interview theme tags are generated for all completed interviews regardless of score.\n\n**Q: Can I compare themes across different studies?**\nA: Themes are generated per study. To compare themes across studies, you can review the reports from each study side by side and look for overlapping patterns.","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Themes & Patterns — Koji Docs","metaDescription":"Understand how Koji identifies recurring themes across interviews. Learn to read theme frequency, supporting quotes, and use patterns for decisions.","keywords":["research themes","thematic analysis","qualitative patterns","theme frequency","cross-interview analysis","research synthesis","Koji themes"],"aiSummary":"Koji automatically identifies 3-7 themes per interview using AI-powered thematic analysis. Themes are aggregated across interviews with frequency data and traceable citations linking back to source interviews. Sentiment is tracked per-interview, not per-theme. Theme names are AI-generated and cannot be manually created or edited.","aiPrerequisites":["ai-generated-insights","creating-your-first-study"],"aiLearningOutcomes":["Understand how Koji detects and labels themes across interviews","Read theme frequency, sentiment, and supporting quotes","Use theme patterns for product prioritization and stakeholder communication","Know when theme saturation has been reached"],"aiDifficulty":"intermediate","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"5794a4bf-60fe-4aba-bd9d-8d1a585c1610","slug":"publishing-sharing-reports","title":"Publishing & Sharing Reports","url":"https://www.koji.so/docs/publishing-sharing-reports","summary":"Koji lets you publish research reports with a shareable public URL and unique slug. Published reports include all sections — executive summary, key takeaways, theme analysis, traceable charts (including structured question visualizations), written findings, recommendations, and stat cards. Available on all plans using credits.","content":"Once you've generated a research report, the next step is getting it in front of the people who need to see it. Koji makes it straightforward to share your findings with stakeholders, team members, and anyone else who needs access to your research.\n\n## Why Sharing Matters\n\nResearch only creates value when it reaches the people making decisions. A brilliant report sitting unread in your account doesn't help anyone. The goal is to make your findings as accessible as possible to the right audience — product managers, designers, executives, engineers, or anyone else who can act on the insights.\n\n## Publishing Your Report\n\nKoji lets you publish reports with a shareable public URL. Publishing generates a unique slug — a URL-safe identifier — that makes the report accessible to anyone with the link.\n\n### How to Publish\n\n1. **Open your report**\n   Navigate to your study and open the generated report.\n\n2. **Click Publish**\n   Use the publish action to make the report publicly accessible. Koji generates a unique public URL with a shareable slug.\n\n3. **Copy the shareable link**\n   The generated link can be sent to anyone. Recipients can view the full report without needing a Koji account.\n\n4. **Share with stakeholders**\n   Share the link via email, Slack, your project management tool, or wherever your team communicates.\n\n### Unpublishing\n\nIf you need to revoke public access, you can unpublish the report at any time. This immediately removes public access — the link stops working. The slug is preserved internally, so if you republish later, access is restored.\n\n## What Stakeholders See in Shared Reports\n\nPublished reports include the full report content:\n\n- **Executive Summary** — key takeaways in under two minutes\n- **Key Takeaways** — prioritized findings with evidence\n- **Theme Analysis** — cross-interview patterns with frequency data and traceable citations\n- **Charts and Visualizations** — sentiment distribution, pain point frequency, quality score distribution, and structured question data\n- **Written Findings** — detailed narrative analysis\n- **Recommendations** — actionable next steps grounded in participant data\n- **Stat Cards** — summary statistics at a glance\n\n### Structured Data in Public Reports\n\nIf your study uses [structured questions](/docs/structured-questions-guide), shared reports include the quantitative visualizations:\n\n- **Scale distributions** with mean, median, and mode (including NPS calculations for 0-10 scales)\n- **Choice frequency bar charts** showing how participants answered single and multiple choice questions\n- **Ranking average position charts** showing item rankings\n- **Yes/No pie charts** showing binary response distributions\n\nThese charts give stakeholders the data-driven evidence they're accustomed to from traditional surveys, while the surrounding qualitative context from the AI conversation provides the depth and nuance that surveys miss.\n\n### Present to Stakeholders\n\nReports are structured for easy presentation. The sections naturally map to a presentation flow:\n\n1. **Start with the Executive Summary** — gives everyone the key takeaways in under two minutes\n2. **Highlight Key Takeaways** — prioritized findings that drive action\n3. **Walk through the top themes** — show the evidence behind the main findings\n4. **Show the charts** — quantitative data from structured questions and sentiment analysis\n5. **Highlight key quotes** — let participant voices make the case\n6. **Discuss recommendations** — connect findings to potential actions\n\nThis structure works whether you're presenting in a team meeting, an executive review, or an asynchronous document review.\n\n## Best Practices for Sharing Research\n\n### Know Your Audience\n\nDifferent stakeholders need different levels of detail:\n\n- **Executives** typically want the executive summary and top-line recommendations. Keep it brief and focus on business impact.\n- **Product managers** want themes, recommendations, charts, and supporting evidence to inform their roadmap.\n- **Designers** benefit from specific quotes and pain points that inform design decisions.\n- **Engineers** may want specific usability issues and workflow breakdowns that suggest technical solutions.\n\nWhen sharing a report link, consider adding a brief note explaining which sections are most relevant for each recipient.\n\n### Add Your Context\n\nKoji's reports provide the data and analysis, but you bring the context. When sharing, consider adding:\n\n- **Business context**: How these findings relate to your current strategy or priorities\n- **Previous research**: How these results compare to or build on earlier studies\n- **Recommended next steps**: Your professional judgment on what actions to take\n- **Scope limitations**: Any important caveats about the study's participants, sample size, or methodology\n\nYour interpretation layer turns a research report into a strategic document.\n\n### Time It Right\n\nShare research when decisions are being made — not weeks before or after. The best time to present research findings is:\n\n- Before sprint planning, when the team is deciding what to build next\n- During roadmap reviews, when priorities are being set\n- At the start of design sprints, when problem framing happens\n- After product launches, when evaluating success and identifying improvements\n\nResearch shared at the right moment has an outsized impact on decisions.\n\n## Report Versions and Sharing\n\nRemember that each time you generate a report, a new version is created (marked with a version number and an isCurrent flag). When sharing, be mindful of which version you're distributing:\n\n- **Share the latest version** when you want stakeholders to see the most complete picture\n- **Reference earlier versions** when you want to show how findings evolved over time\n- **Note the interview count** so recipients understand the data behind the analysis\n\n## Privacy Considerations\n\nWhen sharing reports, keep these privacy best practices in mind:\n\n- **Participant anonymity**: Reports use participant identifiers rather than real names by default. If your study collected names, be thoughtful about whether to include them in shared reports.\n- **Sensitive content**: If interviews touched on sensitive topics, consider whether all recipients need to see all details.\n- **External sharing**: If sharing outside your organization, ensure that the level of detail is appropriate and that no confidential participant information is exposed.\n\n## Plan Access\n\nReport publishing and sharing is available on all plans. Credits are the only gate — generating or refreshing a report costs credits from your balance. Check the [Plan Comparison Guide](/docs/plan-comparison-guide) for credit costs and allocations.\n\n## Key Things to Know\n\n- **Reports are available on all plans**: All features including report publishing are accessible on every plan (free, insights, interviews, enterprise). Credits are the only usage gate.\n- **Recipients don't need a Koji account**: Anyone with the published link can view the report.\n- **Reports are read-only for recipients**: Published reports cannot be edited by viewers, preserving the integrity of your findings.\n- **You can unpublish at any time**: Removing public access is instant and revocable.\n\n## Related Articles\n\n- [Generating Research Reports](/docs/generating-research-reports) — How to create the reports you'll be sharing\n- [Understanding Themes & Patterns](/docs/understanding-themes-patterns) — Theme analysis that powers your reports\n- [Plan Comparison Guide](/docs/plan-comparison-guide) — Compare plans for credit allocations\n- [Structured Questions Guide](/docs/structured-questions-guide) — Design questions that produce rich report visualizations\n\n## Frequently Asked Questions\n\n**Q: Can I control who sees my published report?**\nA: Published links provide access to anyone who has the link. Share it only with intended recipients. If you need to revoke access, you can unpublish the report to immediately remove public access.\n\n**Q: Can recipients download the report?**\nA: Recipients can view the report through the published link. The report is presented as a web page with all sections, charts, and visualizations rendered inline.\n\n**Q: Do published reports update when I generate a new version?**\nA: The published link reflects the current version of the report. When you generate a new version, the published report updates to show the latest data.\n\n**Q: Is there a limit to how many people I can share a report with?**\nA: There's no limit on the number of people who can view a published report link. Share it as widely as needed within your organization.\n\n**Q: Can I add comments or annotations to a published report?**\nA: Reports are shared as-is. For collaborative annotation, we recommend copying key findings into your team's existing collaboration tools (like Notion, Google Docs, or Confluence) and discussing there.","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Sharing Reports — Koji Docs","metaDescription":"Learn how to share research reports with stakeholders. Get tips for presenting findings, timing delivery, and making research drive decisions.","keywords":["share research reports","stakeholder communication","research presentation","qualitative research sharing","Koji report sharing","research collaboration"],"aiSummary":"Koji lets you publish research reports with a shareable public URL and unique slug. Published reports include all sections — executive summary, key takeaways, theme analysis, traceable charts (including structured question visualizations), written findings, recommendations, and stat cards. Available on all plans using credits.","aiPrerequisites":["generating-research-reports"],"aiLearningOutcomes":["Share reports with stakeholders via links","Present research findings effectively to different audiences","Apply best practices for timing and contextualization","Manage report versions when sharing"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"d90eb49e-2e06-47c7-929b-8d934e686542","slug":"insights-dashboard","title":"Insights Dashboard","url":"https://www.koji.so/docs/insights-dashboard","summary":"The study results page in Koji is organized into three tabs: Experience (metrics, quality distribution, themes), Recruit (participant management), and Responses (individual interviews). Quality scores range from 1-5, data requires page refresh, and structured question aggregations provide scale distributions, choice frequencies, ranking averages, and yes/no charts.","content":"The study results page gives you an overview of your study's progress and findings. Instead of reading through individual interviews, you can quickly see how many responses you've collected, how the quality distribution looks, what the completion rate is, and what themes are emerging — organized across three tabs.\n\n## Understanding the Results Page\n\nThere is no separate \"Insights Dashboard\" page in Koji. Instead, your study's results are presented directly within the study view, organized into three tabs: **Experience**, **Recruit**, and **Responses**. Together, these tabs give you a complete picture of your study's health and findings.\n\n## The Three Tabs\n\n### Experience Tab\n\nThe Experience tab provides the high-level metrics and analytics for your study:\n\n#### Interview Count and Progress\n\nThe most fundamental metrics: how many interviews are at each stage. You'll see:\n\n- **Completed interviews**: The number of finished conversations\n- **Partial interviews**: Participants who started but haven't finished\n- **Active interviews**: Conversations currently in progress\n- **Report-eligible interviews**: How many scored 3 or above on the [quality scale](/docs/understanding-quality-scores) — these are the interviews that will be included in your [research report](/docs/generating-research-reports)\n\nThis gives you an immediate sense of how far along your study is and whether you need to recruit more participants.\n\n#### Quality Score Distribution\n\nA visual breakdown of how your interviews scored on the 1-5 quality scale. This is displayed as a distribution chart showing how many interviews fell into each score range.\n\nA healthy distribution is skewed toward the higher end — most interviews scoring 3 or above. If you see too many low-scoring interviews, it might indicate:\n\n- Your study brief needs refinement\n- Your participant targeting could be more specific\n- The interview topic may not resonate with your audience\n\nSee [Understanding Quality Scores](/docs/understanding-quality-scores) for more on what drives quality.\n\n#### Average Duration\n\nHow long participants typically spend in interviews, displayed as average duration in seconds. This helps you understand engagement and whether your interview length is appropriate.\n\n#### Emerging Themes\n\nA high-level view of the themes identified so far across completed interviews. This is a preview of the deeper theme analysis — you can see which topics are most frequently mentioned and get a sense of the narrative forming in your data.\n\nFor detailed theme analysis, see [Understanding Themes & Patterns](/docs/understanding-themes-patterns).\n\n### Recruit Tab\n\nThe Recruit tab helps you manage participant recruitment. It shows your interview link, respondent tracking, and tools for importing and managing participants.\n\n### Responses Tab\n\nThe Responses tab lists all individual interviews as cards. Each card shows the participant identifier, completion date, duration, and quality score. Click any card to open the **Analysis Drawer** with AI-generated insights, or navigate to the full transcript page.\n\nThis is where you go to review individual interviews and their [AI-generated insights](/docs/ai-generated-insights).\n\n## Structured Question Aggregation\n\nIf your study uses [structured questions](/docs/structured-questions-guide), the results page and reports include aggregated quantitative data:\n\n### Scale Question Aggregations\n\nFor scale questions (e.g., satisfaction ratings, NPS scores), you'll see:\n\n- **Distribution charts** showing how participants rated each item\n- **Mean, median, and mode** calculations\n- **NPS calculation** for 0-10 scales, categorizing respondents into promoters, passives, and detractors\n\n### Choice Question Aggregations\n\nFor single and multiple choice questions, you'll see:\n\n- **Bar charts** showing option frequency — how many participants selected each option\n- **Percentages** for each option\n\n### Ranking Question Aggregations\n\nFor ranking questions, you'll see:\n\n- **Average position charts** showing where each item landed across participants' rankings\n\n### Binary Question Aggregations\n\nFor yes/no questions, you'll see:\n\n- **Pie charts** showing the distribution of yes vs. no responses\n\nAll aggregation data includes traceable citations linking back to source interviews, so you can verify any data point by clicking through to the original conversation.\n\n## How to Use the Results Page Effectively\n\n### Monitor Study Health\n\nCheck the results page while your study is active. It tells you whether things are going well or if adjustments are needed:\n\n- **Healthy study**: Steady stream of completed interviews, quality scores mostly 3+, reasonable completion rate\n- **Needs attention**: Lots of started-but-not-completed interviews, quality scores trending low, or very few responses\n\n### Know When to Generate a Report\n\nThe Experience tab helps you decide when you have enough data. Look for:\n\n- Enough report-eligible interviews (usually 5-8 minimum for meaningful patterns)\n- A quality distribution that gives you confidence in the data\n- Theme patterns that are starting to stabilize (the same themes appearing without many new ones emerging)\n\nWhen these conditions are met, it's a good time to [generate a research report](/docs/generating-research-reports).\n\n### Compare Across Studies\n\nOver time, you'll develop a sense of what \"good\" looks like for your research. The results page for each study gives you benchmarking data:\n\n- Is this study getting better completion rates than your last one?\n- Are quality scores higher when you target a different audience?\n- Do structured questions improve engagement and data quality?\n\nThese comparisons help you continuously improve your research practice.\n\n## Metrics Reference\n\nHere's a quick reference for the key metrics you'll encounter:\n\n| Metric | What It Tells You | Why It Matters |\n|--------|-------------------|----------------|\n| **Completed interviews** | How much data you have | Determines if you have enough for reliable analysis |\n| **Report-eligible** | Interviews scoring 3+ | These count toward billing and are included in reports |\n| **Partial interviews** | Participants who started but didn't finish | Indicator of interview design or engagement issues |\n| **Avg. quality score** | Mean score across interviews | Overall data quality benchmark |\n| **Avg. duration** | Mean interview length in seconds | Indicates engagement level |\n| **Theme count** | Distinct themes identified | Shows breadth of topics covered |\n\n## Tips & Best Practices\n\n- **Don't obsess over individual metrics**: The results page gives you a holistic view. A slightly lower completion rate might be fine if quality scores are high — it could mean your interview is thorough enough that only engaged participants finish.\n\n- **Use it for stakeholder updates**: The Experience tab provides ready-made talking points for research status updates. \"We have 12 completed interviews with an average quality score of 4.1 and five clear themes emerging\" is a much better update than \"research is going fine.\"\n\n- **Check before and after study design changes**: If you update your study brief or change your recruitment approach, the results page will show the impact. Compare metrics before and after the change to see if things improved.\n\n- **Refresh the page for latest data**: The results page shows data as of the last page load. Refresh the page to see the latest interviews and updated metrics.\n\n## Key Things to Know\n\n- **Refresh for updates**: New data appears when you refresh the page. The results page does not auto-update in real time.\n- **Available on all plans**: The results page with all analytics is accessible on every plan, including Free. Report generation is a separate action that uses credits.\n- **Historical data persists**: Results data remains available for as long as the study exists. You can come back months later and still see all your metrics.\n- **Quality scale is 1-5**: Not 0-5. Scores range from 1 (poor) to 5 (excellent). See [Understanding Quality Scores](/docs/understanding-quality-scores) for the full breakdown.\n\n## Related Articles\n\n- [Generating Research Reports](/docs/generating-research-reports) — Turn your results data into a comprehensive research report\n- [Viewing Interview Transcripts](/docs/viewing-interview-transcripts) — Dive into individual interviews from the Responses tab\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — Learn what the quality distribution means\n- [Understanding Themes & Patterns](/docs/understanding-themes-patterns) — How themes are aggregated across interviews\n- [Structured Questions Guide](/docs/structured-questions-guide) — Design questions that produce rich aggregation data\n\n## Frequently Asked Questions\n\n**Q: Can I export results data?**\nA: The results page is designed as a visual overview within Koji. For shareable data, use the report generation feature, which presents the same metrics in a stakeholder-friendly format. You can also use Koji's data export tools.\n\n**Q: How do I see the latest data?**\nA: Refresh the page to load the most recent interviews and updated metrics. The results page does not auto-update in real time.\n\n**Q: Can I see results data for past studies?**\nA: Yes, all results data persists for the lifetime of the study. You can revisit any study's results at any time to review historical metrics.\n\n**Q: Is the results page available on the Free plan?**\nA: Yes. The results page with all analytics is available on all plans. Report generation uses credits from your balance.\n\n**Q: What if my completion rate is very low?**\nA: A low completion rate usually signals an issue with interview design, length, or participant targeting. Review your study brief for clarity, consider shortening the interview, and ensure you're reaching the right audience.","category":"Reports & Analysis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Insights Dashboard — Koji Docs","metaDescription":"Navigate Koji's visual analytics dashboard. See interview counts, quality distributions, completion rates, and emerging themes at a glance.","keywords":["insights dashboard","research analytics","interview statistics","completion rate","quality distribution","study metrics","Koji dashboard"],"aiSummary":"The study results page in Koji is organized into three tabs: Experience (metrics, quality distribution, themes), Recruit (participant management), and Responses (individual interviews). Quality scores range from 1-5, data requires page refresh, and structured question aggregations provide scale distributions, choice frequencies, ranking averages, and yes/no charts.","aiPrerequisites":["creating-your-first-study"],"aiLearningOutcomes":["Navigate the insights dashboard and understand all visual metrics","Monitor study health using completion rates and quality distributions","Know when you have enough data to generate a reliable report","Use dashboard data for stakeholder updates"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"d435e63a-9be1-4701-8571-94c5a2c56583","slug":"content-analysis-guide","title":"Content Analysis: The Complete Guide to Analyzing Text and Interview Data","url":"https://www.koji.so/docs/content-analysis-guide","summary":"Content analysis is a research method that transforms qualitative text into coded categories and measurable patterns. This guide covers the three main types (conventional, directed, summative), the step-by-step process, inter-rater reliability standards, and how Koji automates the most labor-intensive parts at scale.","content":"\n# Content Analysis: The Complete Guide\n\n**Bottom line:** Content analysis is a systematic research method that transforms qualitative text data — interview transcripts, survey responses, social media posts, app reviews — into coded categories and measurable patterns. Unlike thematic analysis, which focuses on subjective meanings, content analysis can be both qualitative and quantitative, making it uniquely versatile for researchers who need to combine interpretive depth with statistical rigor.\n\n---\n\n## What Is Content Analysis?\n\nContent analysis is a research technique for making replicable and valid inferences from texts (or other meaningful material) to the contexts of their use. Developed formally in the mid-20th century — with Bernard Berelson's 1952 book *Content Analysis in Communication Research* providing the foundational definition — it has become one of the most cross-disciplinary methods in social science, used across healthcare, marketing, political science, UX research, psychology, and communication studies.\n\nAt its core, content analysis works by:\n1. Defining categories or codes before (deductive) or during (inductive) analysis\n2. Systematically applying those codes to a corpus of text\n3. Counting, comparing, and interpreting the coded material\n4. Drawing conclusions about patterns, frequencies, and meanings\n\n> \"Content analysis is a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use.\" — Klaus Krippendorff, *Content Analysis: An Introduction to Its Methodology* (4th ed., 2018)\n\nContent analysis is applied across more than 25 academic disciplines, from nursing research to political science to UX research — one of the most broadly adopted methods in the social sciences. Its defining advantage over pure thematic analysis: it produces countable, comparable outputs that can be tracked over time and validated by multiple coders.\n\n---\n\n## Content Analysis vs. Thematic Analysis: The Critical Difference\n\nResearchers frequently confuse content analysis and thematic analysis. Both involve coding qualitative text data, but they differ in fundamental ways:\n\n| Dimension | Content Analysis | Thematic Analysis |\n|---|---|---|\n| **Primary goal** | Count and categorize | Interpret meanings |\n| **Coding approach** | Often deductive (pre-defined codes) | Typically inductive (emergent codes) |\n| **Output** | Frequencies, categories, quantifiable patterns | Rich themes, narratives, interpretations |\n| **Best for** | Large datasets, hypothesis testing, trend tracking | Understanding experiences, exploratory research |\n| **Can be quantified?** | Yes — produces numerical summaries | Not typically |\n\n**When to choose content analysis:** You need to compare responses across a large dataset, test a specific hypothesis, track trend changes over time, or produce findings that require inter-rater reliability validation.\n\n**When to choose thematic analysis:** You are exploring lived experiences, building team empathy, or conducting early-stage discovery research where the key themes are genuinely unknown.\n\nFor most UX and product research, thematic analysis works well for single-study analysis. Content analysis shines when analyzing patterns across many sessions — exactly what Koji's automated analysis engine does at scale.\n\n---\n\n## The Three Types of Content Analysis\n\nHsieh and Shannon (2005), in their landmark paper in *Qualitative Health Research*, identified three distinct approaches to qualitative content analysis, each suited to different research goals:\n\n### 1. Conventional Content Analysis (Inductive)\n\nCodes and categories emerge directly from the data. Researchers immerse themselves in the text before developing a coding scheme — no pre-existing framework is imposed. Best for exploratory research where theory is limited or absent.\n\n**Use when:** You are analyzing open-ended survey responses or interview transcripts with no pre-existing hypothesis about what themes will appear.\n\n### 2. Directed Content Analysis (Deductive)\n\nAnalysis begins with a theory or hypothesis. Codes are defined in advance and applied to the data systematically. Best for validating or extending existing theory — or testing whether findings from prior research replicate in a new context.\n\n**Use when:** You are testing whether a known user problem pattern (e.g., \"navigation confusion\") appears in a new product area.\n\n### 3. Summative Content Analysis\n\nBegins with quantifying and comparing specific words or phrases, then moves to interpretation. Used to understand how language use signals underlying meaning and context.\n\n**Use when:** You are analyzing product reviews, support tickets, or NPS follow-up comments to identify dominant patterns by frequency.\n\n---\n\n## Step-by-Step: How to Conduct Content Analysis\n\n### Step 1: Define Your Research Question\n\nBefore touching the data, be precise. \"What are the most commonly cited reasons users abandon onboarding?\" is actionable. \"What do users think?\" is not. Your research question determines which type of content analysis to use and what your codes will look like.\n\n### Step 2: Select and Sample Your Data\n\nDecide what corpus you will analyze. This could be:\n- Interview transcripts from a Koji study\n- Open-ended responses from a survey\n- Social media comments\n- Customer support tickets\n- App store reviews\n- Employee feedback submissions\n\nEnsure your sample is representative of the population you want to understand. For most product research, 20-50 units of text is sufficient for conventional content analysis to reach saturation.\n\n### Step 3: Develop Your Coding Framework\n\n**For inductive (conventional) analysis:**\n- Read through a sample of your data without coding — just absorb\n- Note recurring ideas, concepts, and language patterns\n- Group similar ideas into initial codes\n- Refine codes into higher-level categories with clear boundaries\n\n**For deductive (directed) analysis:**\n- Start with existing frameworks (e.g., usability heuristics, JTBD dimensions)\n- Define operational definitions for each code before touching your data\n- Create a codebook that specifies inclusion criteria, exclusion criteria, and example quotes for every code\n\n### Step 4: Apply Codes to Your Data\n\nWork through your data systematically, applying codes to relevant passages. A single passage can receive multiple codes (unitization). Be consistent and refer to your codebook frequently — especially if multiple people are coding.\n\n### Step 5: Check Inter-Rater Reliability\n\nFor rigorous research, have a second coder independently code a subset (10-20%) of your data. Then calculate agreement using Cohen's Kappa or Krippendorff's Alpha.\n\n**Accepted reliability standards:**\n- Cohen's Kappa ≥ 0.70 = acceptable agreement\n- Cohen's Kappa ≥ 0.80 = strong agreement\n- Krippendorff's Alpha ≥ 0.80 = publishable standard\n\nLow agreement signals unclear code definitions — revise your codebook and re-code until reliability improves.\n\n### Step 6: Analyze and Quantify\n\nCount the frequency of each code. Calculate percentages. Look for patterns, co-occurrences, and notable absences. Ask: which codes appear together? Which findings are surprisingly rare given your prior assumptions?\n\n### Step 7: Interpret and Report\n\nFrequencies are not findings — they are raw material for interpretation. Move from counts to meaning: why do these patterns exist? What do they suggest about the underlying user experience? What should your team do differently based on this evidence?\n\n---\n\n## The Modern Approach: AI-Powered Content Analysis with Koji\n\nTraditional content analysis is powerful but prohibitively time-consuming for most product teams. Manually coding 30 interview transcripts takes an experienced researcher 40-80 hours. Two coders are required for reliability. Codebooks must be developed, documented, and trained. This is why content analysis often gets skipped entirely in fast-moving teams — and replaced with impressionistic \"themes\" derived from whoever attended the research sessions.\n\n**Koji changes this fundamentally.**\n\nWhen you run a study with Koji, the platform's AI automatically:\n- Extracts themes and categories from every interview transcript simultaneously\n- Groups responses by structured question answers (scale ratings, choice selections, ranking results)\n- Produces frequency distributions for quantitative question types (e.g., \"47% of participants rated onboarding difficulty as 4 or 5 out of 5\")\n- Identifies the most commonly cited pain points across all participants, with supporting quote evidence\n- Surfaces the statistical distribution of responses for scale and choice questions — ready to include in stakeholder presentations\n\nWhat used to require a two-person research team working for two days now takes minutes. And because Koji's six structured question types — open_ended, scale, single_choice, multiple_choice, ranking, and yes_no — capture structured data alongside qualitative conversation, you get the analytical depth of content analysis and the interpretive richness of thematic analysis in a single study.\n\n**Example workflow:**\n\n> Instead of manually coding 50 interview transcripts to count how many users mentioned \"slow loading\" as a pain point, set up a Koji study with a scale question (\"How would you rate the current load speed? 1-5\") and an open-ended follow-up (\"What is the most frustrating part of that experience?\"). Koji analyzes all 50 sessions and produces a report showing scale distributions, top themes, and representative quotes — ready to present to stakeholders the same day.\n\nTeams using AI-assisted research tools report 60% faster time-to-insight compared to manual analysis methods.\n\n---\n\n## Content Analysis in Practice: Common Use Cases\n\n### Product Research\nAnalyze interview transcripts to identify the top friction points in a user journey. Count how many participants cited each issue. Prioritize fixes by frequency and severity.\n\n### Voice of Customer Programs\nCode customer support tickets or NPS follow-up responses by category (product bugs, pricing concerns, feature requests). Track category frequencies month-over-month to spot emerging trends before they become crises.\n\n### Competitive Research\nAnalyze competitor reviews on G2, Capterra, or app stores. Code by theme (speed, reliability, support quality, UX). Compare frequency distributions across competitors to find white-space positioning opportunities.\n\n### Employee Research\nCode exit interview transcripts by departure reason. Track frequency of categories over rolling quarters to identify systemic issues before they compound.\n\n### Survey Research\nApply directed content analysis to open-ended survey responses. Pair with Koji's structured question types for full quantitative + qualitative coverage in a single instrument.\n\n---\n\n## Common Content Analysis Mistakes\n\n**1. Codes that overlap**\nIf \"poor UX\" and \"confusing interface\" are both codes, coders will constantly debate which to use. Keep codes mutually exclusive, or explicitly document when co-coding is expected.\n\n**2. Skipping the codebook**\nWithout written operational definitions, reliability suffers and your analysis cannot be reproduced. Every code needs a definition, inclusion criteria, exclusion criteria, and example quotes.\n\n**3. Ignoring negative instances**\nDo not only count what is present — notice what is absent. If no participant mentions a feature you considered important, that silence is a finding.\n\n**4. Treating frequency as importance**\nThe most frequently mentioned theme is not always the most consequential. A single extreme case can outweigh 20 mild mentions in terms of business impact. Frequency and severity are separate dimensions.\n\n**5. Over-coding**\nNot every sentence is a finding. Code for meaningful patterns, not every occurrence of a vaguely relevant word.\n\n---\n\n## Frequently Asked Questions\n\n**Is content analysis qualitative or quantitative?**\nContent analysis bridges both. You work with qualitative text data but produce quantifiable outputs — frequencies, percentages, cross-tabulations. This hybrid nature is what makes it uniquely useful for research that must satisfy both interpretive and statistical audiences.\n\n**How many coders do I need for content analysis?**\nFor rigorous, publishable research: two independent coders with reported inter-rater reliability. For internal product research where speed matters more than academic standards: one coder is defensible, and AI-powered tools like Koji remove this constraint entirely by applying consistent automated coding across all sessions.\n\n**How is content analysis different from discourse analysis?**\nContent analysis focuses on *what* is said — frequencies, patterns, categories of meaning. Discourse analysis focuses on *how* it is said — the language choices, power structures, and social context embedded in communication. They answer different questions from the same text.\n\n**What software tools support content analysis?**\nTraditional dedicated tools include ATLAS.ti, NVivo, and Dedoose. For AI-native content analysis at scale — particularly for interview data — Koji automatically applies content and thematic analysis across all sessions simultaneously, eliminating manual coding software entirely.\n\n**How do I ensure validity in content analysis?**\nEstablish face validity (do codes clearly represent the intended concept?), inter-rater reliability (do independent coders agree?), and construct validity (do findings align with theory and other evidence?). Document your entire analytical process: codebook, sampling rationale, and analytical decisions.\n\n**How many participants do I need?**\nDirected content analysis testing a specific hypothesis may require 30-50 text units for adequate statistical power. Exploratory conventional analysis typically reaches saturation at 15-25 interview transcripts. For large-scale pattern analysis (e.g., coding 1,000 app reviews), statistical sampling logic applies.\n\n---\n\n## Related Resources\n\n- [The Complete Guide to Thematic Analysis](/docs/thematic-analysis-guide) — When thematic analysis is a better fit\n- [How to Analyze Qualitative Data](/docs/how-to-analyze-qualitative-data) — From raw interviews to actionable insights\n- [Coding Qualitative Data: A Step-by-Step Guide](/docs/coding-qualitative-data) — Master the coding process\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — Combine structured and qualitative data in Koji\n- [Research Synthesis Guide](/docs/research-synthesis-guide) — Combine multiple studies into unified insights\n- [AI-Generated Insights](/docs/ai-generated-insights) — How Koji automates content analysis across all sessions\n\n---\n\n*Koji automates the most time-consuming parts of content analysis — coding, categorizing, and frequency counting — so your team spends time on interpretation and action, not spreadsheets.*\n","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Content Analysis: The Complete Guide (2026)","metaDescription":"Learn how to conduct content analysis step-by-step — from the three main approaches to inter-rater reliability and AI-powered automation. Includes comparison with thematic analysis and modern Koji workflows.","keywords":["content analysis","qualitative content analysis","content analysis vs thematic analysis","coding qualitative data","content analysis research method","content analysis guide"],"aiSummary":"Content analysis is a research method that transforms qualitative text into coded categories and measurable patterns. This guide covers the three main types (conventional, directed, summative), the step-by-step process, inter-rater reliability standards, and how Koji automates the most labor-intensive parts at scale.","aiPrerequisites":["Basic understanding of qualitative research","Familiarity with interview transcripts or survey data"],"aiLearningOutcomes":["Understand the difference between content analysis and thematic analysis","Apply the three content analysis approaches to your research data","Calculate and interpret inter-rater reliability using Cohen's Kappa","Use Koji to automate content analysis across large interview sets"],"aiDifficulty":"intermediate","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"62f9bcc2-946d-4b0a-989d-aac2554a14d5","slug":"user-research-report-template","title":"User Research Report Template: How to Present Findings That Drive Action","url":"https://www.koji.so/docs/user-research-report-template","summary":"A user research report is a decision document, not a documentation exercise. The best reports lead with an executive summary, present 3-5 prioritized findings with supporting quotes, show theme frequency across participants, and end with specific recommended actions. Koji automatically generates this full report structure after every completed study — including executive summary, theme analysis, citation-linked quotes, and recommendations — reducing analysis time from days to minutes.","content":"# User Research Report Template: How to Present Findings That Drive Action\n\n**The problem with most research reports:** They're too long, too dense, and structured around what the researcher found rather than what the stakeholder needs to decide.\n\nA great user research report doesn't document the research process. It presents findings in a way that makes the path forward obvious — and makes it impossible to ignore the evidence.\n\nThis guide gives you a complete template for research reports that drive action, plus an explanation of how Koji's AI-generated reports automatically produce this structure for every study.\n\n---\n\n## The Core Principle: Research Reports Are Decision Documents\n\nBefore you write a single word, ask yourself: *What decision does this report need to inform?*\n\n- Should we build Feature X or Feature Y?\n- Is our onboarding working for the target segment?\n- Why are customers churning after 60 days?\n- Does our new positioning resonate with enterprise buyers?\n\nEvery section of your report should make that decision easier. Evidence that doesn't connect to the decision is a distraction.\n\nIf you don't know what decision the report is informing, find out before you write. Research without a decision consumer isn't research — it's documentation.\n\n---\n\n## The User Research Report Template\n\n### Section 1: Executive Summary (1 page maximum)\n\nThe executive summary is the most important section of your report. Many stakeholders — especially executives and product leads — will read nothing else.\n\n**Structure:**\n1. **Research question** (1-2 sentences): What were you trying to learn?\n2. **Method** (1 sentence): How many interviews, over what period, with whom?\n3. **Top 3 findings** (3 bullet points): The most important things you learned, framed as clear statements\n4. **Recommended actions** (2-3 bullet points): What should happen next based on these findings?\n\n**Example:**\n\n> **Research question:** Why do users who complete onboarding fail to run their first study within 30 days?\n>\n> **Method:** 12 in-depth interviews with users who signed up in the past 60 days and have 0 completed studies.\n>\n> **Top findings:**\n> - 9 of 12 participants said they weren't sure what research question to start with — they needed a template or guided starting point.\n> - 7 of 12 described feeling anxious about \"getting it right\" before inviting participants.\n> - 4 of 12 mentioned they recruited their first participant immediately after setup but the participant never responded, and they didn't know how to follow up.\n>\n> **Recommended actions:**\n> - Add a \"quick start\" template library to the onboarding flow\n> - Add a checklist or quality review step before the study goes live to address perfectionism anxiety\n> - Add automated participant re-invitation after 48 hours of no response\n\nThis summary takes 60 seconds to read and gives a stakeholder everything they need to act.\n\n---\n\n### Section 2: Research Background (Half page)\n\n**What to include:**\n- **Research objective**: The full question the research was designed to answer\n- **Research method**: Type of interviews (moderated, AI-moderated, voice, text), duration, recruitment approach\n- **Participants**: Number of participants, key demographic/firmographic characteristics, how they were recruited\n- **Timeline**: When the research was conducted\n- **Researcher/team**: Who conducted the research and who to contact with questions\n\n**Keep it factual and brief.** Stakeholders don't need to evaluate your methodology — they need to trust it. A concise, confident description is more credible than a defensive one.\n\n---\n\n### Section 3: Key Findings\n\nThis is the heart of the report. Each finding should follow the same structure:\n\n**Finding structure:**\n1. **Headline** (one clear statement): \"Most users don't understand the difference between a study and a template\"\n2. **Evidence** (2-4 supporting quotes or observations with attribution)\n3. **Frequency** (how many participants this applied to)\n4. **Implication** (what this means for the decision at hand)\n\n**Example:**\n\n> **Finding: Users consistently overestimate how long setup takes — and abandon before they experience the value**\n>\n> \"I thought I had to design all the questions myself from scratch. I spent an hour on the first study and wasn't happy with it so I gave up.\" — P7, Product Manager, SaaS company\n>\n> \"I wasn't sure what format the questions should be in, so I kept second-guessing myself.\" — P3, Founder, B2B startup\n>\n> *8 of 12 participants described a version of this experience. None of the 4 who reported smooth setup had been given the same level of unguided onboarding — 3 of them were referred by a colleague who showed them the template library.*\n>\n> **Implication:** The template library is not discoverable in the current onboarding flow. This is likely the primary driver of 30-day activation failure.\n\n**How many findings to include:**\n- 3-5 for most reports\n- Never more than 7 (after that, stakeholders stop retaining them)\n- Rank by strategic importance, not by how interesting they are to you\n\n---\n\n### Section 4: Themes and Patterns\n\nFor studies with 10+ participants, a themes section helps stakeholders see the macro patterns rather than individual stories.\n\n**What to include:**\n- **Top 5-7 themes**: Named, defined, and quantified by frequency\n- **Theme frequency chart**: A simple bar chart showing how often each theme appeared\n- **Sentiment breakdown**: How did participants feel overall? What topics generated positive vs. negative sentiment?\n\n**Example theme table:**\n\n| Theme | Frequency | Sentiment | Key Insight |\n|-------|-----------|-----------|-------------|\n| Onboarding confusion | 9/12 | Negative | Setup expectations don't match reality |\n| Template discovery | 8/12 | Neutral | High value when found, rarely found organically |\n| First participant success | 6/12 | Mixed | Critical activation moment — often fails silently |\n| Reporting quality | 5/12 | Positive | Strong positive signal when users reach reports |\n| Pricing clarity | 4/12 | Negative | Credit model not well understood in trial period |\n\n---\n\n### Section 5: Participant Quotes (Highlights)\n\nA dedicated quotes section serves two purposes: it gives stakeholders the emotional truth behind the numbers, and it gives writers and product teams the language they need.\n\n**Selection criteria:**\n- Prioritize quotes that illustrate findings the data can't fully capture\n- Include quotes from multiple participants, not just one eloquent respondent\n- Include at least one challenging/uncomfortable quote — reports that only surface positive quotes aren't credible\n\n**Attribution format:** Use participant role + company type, not names: \"— Senior PM, B2B SaaS company\" or \"— P7, 30-day trial user.\" Real names require consent and add noise.\n\n---\n\n### Section 6: Recommendations\n\nThe recommendations section is where research creates business value. It should be direct, specific, and owned.\n\n**Each recommendation should include:**\n1. **Action statement**: \"Add X template library to onboarding flow, surfaced at step 2 of setup\"\n2. **Evidence link**: \"Addresses the onboarding confusion theme present in 9 of 12 interviews\"\n3. **Expected impact**: \"Should reduce 30-day activation time and decrease time-to-first-study\"\n4. **Priority**: High / Medium / Low\n5. **Owner suggestion**: Which team should take this on?\n\n**Avoid vague recommendations** like \"improve onboarding\" or \"make it easier for users.\" These feel like research findings, not decisions. Stakeholders need specific actions.\n\n---\n\n### Section 7: Appendix (Optional)\n\nFor reports that will be referenced over time, a brief appendix adds credibility and archival value:\n- Full participant list (role, segment, interview date)\n- Complete interview guide\n- Links to individual interview transcripts (if available)\n- Methodology notes\n\nKeep the appendix lean. Its job is to answer \"where did this come from?\" — not to document everything you did.\n\n---\n\n## How Koji Generates Research Reports Automatically\n\nBuilding this structure manually for every research project takes 4-8 hours. Koji's report generation does it automatically — and does it for every study, not just the ones you have time to analyze.\n\nHere's what Koji generates automatically after every completed study:\n\n**Executive summary**: AI-generated overview with key themes, participant count, and top findings — ready to share.\n\n**Key findings**: Each question in your study gets its own findings section with theme analysis, representative quotes, and frequency data. Every quote is linked back to the original interview transcript.\n\n**Theme analysis**: Automatically extracted across all interviews with frequency charts and sentiment breakdown.\n\n**Recommendations**: AI-generated action suggestions based on the pattern of findings, categorized by product, marketing, research, and general.\n\n**Individual interview analysis**: Quality scores, sentiment, and structured answer extraction for every interview — so you can filter by participant segment or quality threshold.\n\n**Shareable report**: Publish your report as a clean public link to share with any stakeholder — no login required.\n\nThe full report is available in minutes after your study is complete. For researchers who would otherwise spend a week on synthesis, Koji's automatic reports represent a 10x reduction in analysis time.\n\n---\n\n## Research Report Writing Tips\n\n**Write findings, not observations.** \"Users struggled with the export function\" is an observation. \"The export function's incompatibility with Excel is the primary friction point blocking enterprise adoption\" is a finding. Findings interpret what you observed.\n\n**Use present tense for findings.** \"Users expect templates to be available at setup\" — not \"users expected.\" Present tense creates urgency.\n\n**Show, don't tell.** \"Users were frustrated\" is weak. \"Seven users described feeling frustrated — using words like 'confused,' 'stuck,' and 'gave up' — specifically during the first study setup.\" is evidence.\n\n**Include the uncomfortable data.** The most valuable research reports contain findings that challenge current assumptions. If you sanitize the uncomfortable findings, you've removed the most valuable parts.\n\n**Design for skim-readers.** Use headers, bold text, bullet points, and visual hierarchy. Most stakeholders will skim before deciding whether to read. Make it easy to extract the key messages at a glance.\n\n---\n\n## Common Research Report Mistakes\n\n**The \"findings dump\"**: 20+ bullet points with no prioritization. Stakeholders can't retain it, can't act on it, and stop trusting research that produces it.\n\n**Burying the lede**: Starting with background and methodology before getting to findings. The most important things should be first.\n\n**Recommendations that sound like findings**: \"We should learn more about X\" is not a recommendation — it's a research proposal. Recommendations are actions, not investigations.\n\n**Passive voice throughout**: \"It was found that users were observed to have difficulty...\" is researcher-speak. Write like a human who wants to be understood.\n\n**Missing the \"so what\"**: Every finding needs an implication. If you can't say what the finding means for the decision at hand, rethink whether it belongs in the report.\n\n---\n\n## The Bottom Line\n\nA user research report that drives action has three characteristics: it's scannable in 2 minutes, it makes the evidence undeniable, and it makes the recommended actions obvious.\n\nThe template above is designed to hit all three. Executive summary first, key findings with quotes, themes with frequency data, specific recommendations with evidence links.\n\nIf the research process is taking time away from report writing, Koji's automatic synthesis is worth exploring. You design the study, collect responses via Koji's AI interviews, and Koji generates the full report structure — findings, themes, quotes, and recommendations — so you can spend your time acting on insights instead of producing them.\n\n---\n\n## Related Resources\n\n- [Presenting Research Findings](/docs/presenting-research-findings) — Report presentation guide\n- [How to Analyze Qualitative Data](/docs/how-to-analyze-qualitative-data) — Analysis methodology\n- [Generating Research Reports](/docs/generating-research-reports) — Koji report generation\n- [Writing Insight Statements](/docs/writing-insight-statements) — Craft actionable insights\n- [Research Repository Guide](/docs/research-repository-guide) — Organize research outputs\n\n*Explore [structured questions](/docs/structured-questions-guide) for building data-rich research reports.*","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"User Research Report Template: Present Findings That Drive Action | Koji","metaDescription":"A complete user research report template with proven structure — executive summary, key findings, themes, and recommendations. Plus how Koji auto-generates this structure for every study.","keywords":["user research report template","research report format","how to write user research report","presenting research findings","research report structure","qualitative research report template","UX research report","research findings template"],"aiSummary":"A user research report is a decision document, not a documentation exercise. The best reports lead with an executive summary, present 3-5 prioritized findings with supporting quotes, show theme frequency across participants, and end with specific recommended actions. Koji automatically generates this full report structure after every completed study — including executive summary, theme analysis, citation-linked quotes, and recommendations — reducing analysis time from days to minutes.","aiPrerequisites":["Completed at least one round of research interviews","Basic understanding of qualitative data"],"aiLearningOutcomes":["Structure a research report using the executive summary + findings + themes + recommendations format","Write findings that interpret observations rather than just documenting them","Present recommendations that are specific and actionable","Use Koji auto-generated reports to deliver analysis faster"],"aiDifficulty":"intermediate","aiEstimatedTime":"15 minutes"},{"type":"documentation","id":"8b9c56bb-112a-4f39-8218-71d687e063ca","slug":"ux-research-report-template","title":"How to Create Effective UX Research Reports (+ Free Template)","url":"https://www.koji.so/docs/ux-research-report-template","summary":"A UX research report is a structured document communicating findings, insights, and recommendations from a research study to stakeholders. It should include an executive summary, methodology, findings with supporting quotes, and actionable recommendations tied to each insight. AI-native platforms like Koji auto-generate research reports automatically after each interview, reducing reporting time from days to minutes.","content":"# How to Create Effective UX Research Reports (+ Free Template)\n\nA great UX research report does one thing: it moves people to act. It transforms raw qualitative data — hours of interviews, transcripts, and notes — into a clear narrative that tells stakeholders exactly what users need and what the team should do next. Yet more than **51% of UX researchers say they wish they had more time for analysis and socializing findings** (Dscout, 2024), meaning the reporting step is one of the most underinvested parts of the research process.\n\nThis guide gives you a complete, reusable UX research report template and shows you how modern AI-native research platforms like Koji can auto-generate these reports in minutes — not days.\n\n---\n\n## What Is a UX Research Report?\n\nA UX research report is a structured document that communicates the findings, insights, and recommendations from a research study to stakeholders. It bridges the gap between raw user data and product decisions.\n\nA strong report answers three questions:\n1. **What did we learn?** (Findings)\n2. **What does it mean?** (Insights)\n3. **What should we do?** (Recommendations)\n\nThe report format varies depending on your audience — a concise executive summary for C-suite, a detailed findings document for product and design teams — but the structure remains consistent.\n\n---\n\n## Why Most UX Research Reports Fail\n\nThe most common reason research reports fail to drive action is a broken chain between data, insight, and recommendation. Researchers often:\n\n- **Overload with data** — including every metric and observation rather than prioritizing what matters\n- **Bury the insights** — leading with methodology instead of the most important finding\n- **Skip actionable next steps** — leaving stakeholders with interesting data but no clear path forward\n- **Use research jargon** — making reports inaccessible to non-researchers on the product team\n- **Create static documents** — reports that get filed and forgotten rather than shared and iterated on\n\nThe fix is a clear structure that puts the most important finding first and connects every insight to a concrete recommendation.\n\n---\n\n## The 6-Section UX Research Report Template\n\nUse this structure for any research study — usability tests, user interviews, surveys, or diary studies.\n\n### Section 1: Executive Summary\n\nThe executive summary is the most important section. Write it last but place it first. Keep it to 2-3 paragraphs covering:\n\n- **The research question** — What were you trying to learn?\n- **Key findings** — The 3-5 most important insights (stated as clear, specific claims)\n- **Top recommendations** — What should the team do next?\n\nMost stakeholders will only read the executive summary. If your key finding is not here, it will not get actioned.\n\n**Template:**\n```\nWe conducted [X interviews / survey with X participants] to understand [research question].\n\nKey findings:\n1. [Most important finding — stated as a specific insight, not just an observation]\n2. [Second finding]\n3. [Third finding]\n\nWe recommend: [Top 2-3 actionable recommendations]\n```\n\n### Section 2: Research Background & Objectives\n\nProvide context for why the research was conducted:\n\n- **Business context** — What product decision or challenge prompted this research?\n- **Research goals** — What specific questions were you trying to answer?\n- **Hypotheses** — What did the team believe going into the research (to be validated or invalidated)?\n- **Scope** — What was explicitly out of scope?\n\nThis section helps stakeholders understand why certain topics were explored and others were not.\n\n### Section 3: Methodology\n\nDescribe how the research was conducted:\n\n- **Method chosen** — User interviews, usability testing, surveys, diary study, etc.\n- **Why this method** — Brief rationale for why this method best answered the research question\n- **Participants** — Number of participants, screener criteria, key characteristics\n- **Session details** — Duration, moderated vs. unmoderated, in-person vs. remote\n- **Analysis approach** — How you synthesized the data (thematic analysis, affinity mapping, etc.)\n\nKeep this section concise — 1 page maximum. Stakeholders need enough context to trust the methodology, not a dissertation.\n\n### Section 4: Findings & Insights (The Core)\n\nThis is the heart of the report. Structure findings in one of three ways depending on your study:\n\n**Option A: By Research Goal**\nList each research goal and present all evidence supporting or contradicting it. Best for evaluative studies (usability tests, concept validation).\n\n**Option B: By Theme**\nOrganize by the patterns that emerged most strongly across participants. Best for generative/discovery research.\n\n**Option C: By Affinity Category**\nGroup insights by the natural categories that emerged during synthesis. Best for large datasets with many participants.\n\n**For each insight, include:**\n- **The insight statement** — A clear, specific claim (e.g., \"Users cannot find the export function because it is nested 3 levels deep in settings\")\n- **Supporting evidence** — 2-3 direct quotes from participants\n- **Frequency** — How many participants experienced this (e.g., \"7 of 8 participants\")\n- **Severity** — Critical / Major / Minor\n- **Visual evidence** — Screenshots, video clips, annotated UI\n\n**Example insight format:**\n\n> **Finding:** Users frequently abandon the checkout flow when asked to create an account.\n>\n> *\"I just wanted to buy one thing — I do not want to sign up for another account.\"* — Participant 4\n>\n> *\"Why do I need an account? I am never going to come back.\"* — Participant 7\n>\n> **Frequency:** 6 of 8 participants | **Severity:** Critical\n\n### Section 5: Recommendations\n\nTranslate every key insight into a specific, actionable recommendation. The most effective recommendations include:\n\n- **What to do** — The specific change or action\n- **Why** — Tied directly to the insight\n- **Priority** — P0 (immediate), P1 (next sprint), P2 (backlog)\n- **Owner** — Who should take this action (design, engineering, product)\n\n**Template:**\n```\nRecommendation: Allow guest checkout without account creation.\nWhy: 75% of participants abandoned checkout when required to create an account.\nPriority: P0 — blocks conversion\nOwner: Product + Engineering\n```\n\n### Section 6: Appendix\n\nInclude supporting materials for researchers and designers who want to dig deeper:\n\n- Full participant demographics\n- Interview guide / discussion guide\n- Raw data tables or session recordings\n- Affinity map or synthesis artifacts\n- Methodology limitations and caveats\n\nThe appendix keeps the main report clean while providing depth for those who need it.\n\n---\n\n## UX Research Report Best Practices\n\n### Lead With the Most Important Finding\n\nStructure your report like a newspaper article — the most important information first. UX researchers often make the mistake of building to a conclusion. Instead, state the conclusion upfront and then support it with evidence.\n\nThis is called the Minto Pyramid Principle: start with the top-level insight, then support it with evidence below.\n\n### Use Direct Quotes Strategically\n\nDirect quotes from participants are the most persuasive evidence in a research report. They create empathy and overcome stakeholder skepticism in a way that statistics cannot. Nielsen Norman Group emphasizes that \"video evidence is a strong UX storytelling tool that helps you improve comprehension, build empathy, and overcome skepticism when communicating research findings to stakeholders.\"\n\nChoose quotes that are:\n- Specific (not vague or abstract)\n- Representative of a pattern (not cherry-picked outliers)\n- Human and relatable\n\n### Quantify What You Can\n\nEven in qualitative research, numbers add credibility. \"7 of 8 participants struggled with X\" is more compelling than \"most participants struggled with X.\" Always specify how many participants experienced each finding.\n\n### Make It Visual\n\nAnnotated screenshots, journey maps, and comparison charts reduce cognitive load and make reports scannable. Use a consistent visual hierarchy:\n- **Bold** for finding statements\n- Quoted text for participant quotes\n- Tables for prioritized recommendations\n\n### Tailor for Your Audience\n\nCreate different versions of the same report for different audiences:\n- **Executive audience:** 1-page summary with top 3 findings and recommendations\n- **Product team:** Full findings document with supporting evidence\n- **Design team:** Detailed findings with UI annotations and specific design recommendations\n\n---\n\n## Research Reporting Timeline Reality\n\nThe average research project takes **42 days from start to finish** (Dscout, 2024), with analysis and reporting consuming a significant portion. Specifically:\n- Discovery research averages 60 days\n- Evaluative research averages 28 days\n\nNearly **60% of researchers report that reduced project time negatively affects the rigor of their methodology** and their creative approach — which means reporting quality suffers when timelines compress.\n\nOrganizations that invest in research are increasingly seeing results: from 8% in 2025 to **22% in 2026**, companies now view research as essential to their core business strategy — nearly tripling in one year (Maze Future of User Research Report, 2026).\n\n---\n\n## How AI Is Transforming Research Reporting\n\nThe traditional research reporting process involves manual transcription, time-consuming affinity mapping, hours of synthesis, and then writing the report from scratch. In 2026, **nearly 69% of researchers now use AI in at least some of their projects** (Maze, 2026), and the results are significant:\n\n- **63% report faster research turnaround**\n- **60% experience better team efficiency**\n- **56% achieve more optimized workflows**\n\nAI-native research platforms are fundamentally changing what is possible.\n\n### Traditional Approach vs. Koji AI-Native Approach\n\n| Step | Traditional | With Koji AI |\n|------|-------------|-------------|\n| Transcription | 2-4 hours per interview | Automatic, real-time |\n| Thematic analysis | 1-2 days for 10 interviews | Minutes |\n| Report generation | 4-8 hours per study | Auto-generated after each interview |\n| Cross-study synthesis | Days to weeks | Real-time dashboard |\n| Sharing findings | Static PDF or slide deck | Live, shareable research portal |\n\n### How Koji Auto-Generates Research Reports\n\nKoji is an AI-native research platform that conducts interviews autonomously — via text or voice — and automatically generates structured research reports. Here is how the reporting workflow works:\n\n1. **Set up your study** — Define your research questions using any of Koji's 6 structured question types: open-ended, scale, single choice, multiple choice, ranking, or yes/no.\n2. **Collect responses** — Koji's AI interviewer conducts interviews with your participants, probing for depth on open-ended questions.\n3. **Auto-analysis** — After each interview, Koji scores response quality (1-5 scale) and extracts structured answers.\n4. **Report generation** — Koji aggregates all responses into a comprehensive research report with themes, quotes, distributions for quantitative questions, and actionable insights — all organized by your research questions.\n5. **Share and publish** — Publish your report as a shareable link or export to CSV/JSON for further analysis.\n\nThe result: research teams can run studies with dozens or hundreds of participants and have a full analysis in hours rather than weeks.\n\n---\n\n## Downloadable UX Research Report Template\n\nHere is a complete, copy-paste-ready template for your next research report:\n\n```\n# [Study Title] Research Report\nDate: [Date]\nResearcher(s): [Names]\nStudy Type: [User interviews / Usability test / Survey]\nParticipants: [N participants, key characteristics]\n\n---\n\nExecutive Summary\n[2-3 paragraphs: context, key findings, top recommendations]\n\nKey Findings:\n1. [Finding 1]\n2. [Finding 2]\n3. [Finding 3]\n\nRecommendations:\n1. [Recommendation 1]\n2. [Recommendation 2]\n\n---\n\nBackground & Objectives\nBusiness context: [Why this research was needed]\nResearch goals: [What we were trying to learn]\nHypothesis: [What we believed going in]\nOut of scope: [What we intentionally did not explore]\n\n---\n\nMethodology\nMethod: [Research method]\nParticipants: [N participants, screener criteria]\nSessions: [Duration, moderated/unmoderated, remote/in-person]\nAnalysis: [How we synthesized the data]\n\n---\n\nFindings\n\nFinding 1: [Specific insight statement]\nEvidence:\n- \"[Quote from participant]\" — P[#]\n- \"[Quote from participant]\" — P[#]\nFrequency: [X of N participants]\nSeverity: [Critical / Major / Minor]\n\n---\n\nRecommendations\n\nRecommendation | Rationale | Priority | Owner\n[Action] | [Tied to finding] | P0/P1/P2 | [Team]\n\n---\n\nAppendix\n- Participant demographics\n- Interview guide\n- Raw data / session recordings\n- Methodology limitations\n```\n\n---\n\n## Related Resources\n\n- [Thematic Analysis: How to Find Patterns in Qualitative Data](/docs/thematic-analysis-guide)\n- [How to Write a Research Brief](/docs/research-brief-template)\n- [Turning Interviews Into Insights: Koji's Analysis Engine](/docs/turning-interviews-into-insights)\n- [Generating Research Reports with Koji](/docs/generating-research-reports)\n- [Structured Questions Guide: Mixing Qualitative and Quantitative Research](/docs/structured-questions-guide)\n- [Publishing and Sharing Research Reports](/docs/publishing-sharing-reports)","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"UX Research Report Template: How to Write Reports That Drive Action (2026)","metaDescription":"Get a complete UX research report template plus best practices for writing reports that actually drive product decisions. Includes how Koji auto-generates research reports in minutes.","keywords":["ux research report template","user research report","how to write research report","research findings report","ux research findings presentation","research report structure"],"aiSummary":"A UX research report is a structured document communicating findings, insights, and recommendations from a research study to stakeholders. It should include an executive summary, methodology, findings with supporting quotes, and actionable recommendations tied to each insight. AI-native platforms like Koji auto-generate research reports automatically after each interview, reducing reporting time from days to minutes."},{"type":"documentation","id":"02e2861f-fd5a-4c5f-9b94-c233e5a0dfa1","slug":"ai-transcription-research-interviews","title":"AI Transcription for Research Interviews: Speed Up Analysis by 10x","url":"https://www.koji.so/docs/ai-transcription-research-interviews","summary":"AI transcription converts audio interviews to text in minutes instead of days, enabling research teams to run 10x more studies without proportional time increases. Modern AI achieves 90–95% word accuracy — sufficient for qualitative analysis and theme extraction. Koji goes beyond transcription to automate the full research pipeline: AI conducts the interview (voice or text), transcribes in real time, extracts structured answers for 6 question types, scores session quality (1–5), identifies themes, and generates aggregate reports. This compresses a traditional 60–120 researcher-hour study to 2–4 hours of researcher time. Verify specific quotes against recordings before publishing; AI theme extraction is a starting point that benefits from researcher interpretation.","content":"# AI Transcription for Research Interviews: Speed Up Analysis by 10x\n\nTranscription has historically been one of the most painful bottlenecks in qualitative research. A 45-minute interview produces roughly 6,000–8,000 words of transcript. Manual transcription takes 3–5 hours per session. Outsourced transcription takes 24–48 hours and costs $60–$150 per session. For a study with 15 participants, that's 45–75 hours of transcription time and potentially $2,250 in costs — before you've done a single minute of analysis.\n\nAI transcription changes this math completely. Modern AI systems produce research-ready transcripts in minutes, not days. But the real breakthrough isn't just speed — it's what happens *after* transcription: automatic analysis, theme extraction, quality scoring, and report generation that would previously require days of researcher time.\n\nThis guide covers how AI transcription works for research interviews, what to look for, and how platforms like Koji close the gap between conversation and insight by automating the entire pipeline.\n\n## Why Transcription Matters in Research\n\nTranscription isn't just a logistical step — it's the foundation of all qualitative analysis. Without a transcript:\n- You can't do thematic analysis across multiple sessions\n- You can't pull quotes for reports and presentations\n- You can't search for specific words, phrases, or topics\n- You can't share the raw data with stakeholders or other researchers\n- You can't train AI systems to extract structured insights\n\nThe quality of your transcript directly affects the quality of your analysis. A poor transcript — full of errors, missing speaker labels, or garbled speech — creates compounding problems downstream.\n\n## How AI Transcription Works\n\nModern AI transcription uses large language models (LLMs) trained on massive corpora of speech to convert audio into text. Key capabilities:\n\n### Automatic Speech Recognition (ASR)\nThe core capability: converting audio waveforms into words. Modern ASR systems achieve word error rates (WER) of 5–10% for clear speech in standard accents, compared to 1–3% for professional human transcribers. For research purposes, this accuracy level is generally sufficient — minor errors in filler words and conjunctions don't affect qualitative analysis.\n\n### Speaker Diarization\nAutomatic identification of who is speaking when. Good diarization produces transcripts labeled \"Participant:\" and \"Interviewer:\" so you can immediately filter to participant responses. This is critical for research — you're analyzing participant speech, not the moderator's questions.\n\n### Multilingual Transcription\nModern AI systems can transcribe across dozens of languages, enabling research programs that span global markets without the bottleneck of finding bilingual transcription services. Koji supports multilingual interviews natively — sessions can be conducted in the participant's language and analyzed in your working language.\n\n### Timestamped Output\nTime-coded transcripts let you jump directly to specific moments in the recording. This is valuable for research validation — when an insight seems surprising, you can quickly verify it against the original audio.\n\n## The Traditional Research Pipeline vs. AI-Automated\n\n### Traditional Pipeline\n1. Conduct interview (45–60 min)\n2. Send to transcription service (24–48 hour wait)\n3. Review and clean transcript (1–2 hours)\n4. Code and tag themes manually (2–4 hours per session)\n5. Synthesize across sessions (4–8 hours for 10 sessions)\n6. Write analysis and report (4–8 hours)\n\n**Total time per 10-session study: 60–120 hours of researcher time**\n\n### AI-Automated Pipeline (Koji)\n1. AI conducts interview — voice or text, no scheduling required\n2. Transcript generated automatically in real time\n3. AI analysis runs immediately after each session — themes, quality score, individual insights, structured data extraction\n4. Aggregate report generated after sufficient responses — patterns, quotes, theme frequency, structured data charts\n5. Researcher reviews, edits, and shares report\n\n**Total researcher time per 10-session study: 2–4 hours**\n\nThis is the 10x efficiency gain. The bottleneck shifts from mechanical processing to the highest-value work: interpreting surprising findings and making decisions.\n\n## What AI Analysis Does With Transcripts\n\nTranscription is just the first step. The real value of AI in research is what happens with the transcript:\n\n### Theme Extraction\nAI identifies recurring themes across interview transcripts without the researcher manually reading and coding each session. Themes are surfaced with supporting quotes and frequency counts — \"8 of 12 participants mentioned difficulty with onboarding in the first week.\"\n\n### Sentiment Analysis\nEmotional tone detection at the session level and topic level. Not just \"positive/negative\" but nuanced patterns — \"participants are enthusiastic about the core value proposition but anxious about implementation complexity.\"\n\n### Structured Data Extraction\nWhen participants answer quantitative questions (scale ratings, choice selections, yes/no responses), AI extracts and structures these values automatically. This means quantitative and qualitative data flow through the same pipeline — a participant who rates satisfaction at 3/10 and then explains why has both data points captured, linked, and aggregated.\n\nKoji's structured answer system links every quantitative response back to its qualitative context. A scale response of \"3/10\" automatically connects to the participant's explanation — giving you both the metric and the story behind it.\n\n### Quality Scoring\nNot all interviews produce equally valuable data. AI quality scoring evaluates each session against your research brief — did the participant answer the key questions? Did they provide substantive responses? Was the conversation on-topic?\n\nKoji's quality gate (score 3+ on a 1–5 scale) only counts a session as a completed interview if it meets a minimum quality threshold. This prevents low-effort, single-sentence sessions from distorting your research data — and ensures you're only paying credits for genuinely valuable interviews. Learn more in our [how the quality gate works](/docs/how-the-quality-gate-works) guide.\n\n### Automatic Highlights and Quotes\nAI identifies the most significant quotes per session and per theme, saving hours of manual highlight-pulling. These quotes are immediately usable in reports and stakeholder presentations.\n\n## AI Transcription Accuracy: What to Expect\n\nFor research purposes, AI transcription is accurate enough for analysis. Key factors that affect accuracy:\n\n### Factors That Improve Accuracy\n- **Clear audio** — quiet environment, good microphone\n- **Standard accent / native speaker** — most systems are trained predominantly on English native speakers\n- **Slower speech** — participants who speak deliberately are transcribed more accurately\n- **Domain vocabulary in training data** — general language is typically well-covered\n\n### Factors That Reduce Accuracy\n- **Background noise** — ambient sound interferes with speech recognition\n- **Heavy accents or dialects** — accuracy varies by accent; improving but not perfect\n- **Technical jargon** — specialized terminology may be misrecognized\n- **Cross-talk or interruptions** — overlapping speech is challenging for speaker diarization\n\n### When to Spot-Check\nFor research reports with attributed quotes, always verify the specific quotes you plan to use against the original recording. AI transcription is reliable enough for theme extraction and analysis, but important quotes in published research deserve human verification.\n\n## AI Transcription vs. Human Transcription for Research\n\n| | AI Transcription | Human Transcription |\n|---|---|---|\n| **Speed** | Minutes | 24–48 hours |\n| **Cost** | Included in AI research platforms | $60–$150/session |\n| **Accuracy** | 90–95% WER | 97–99% WER |\n| **Speaker labels** | Automatic | Manual or extra cost |\n| **Analysis integration** | Immediate | Requires separate step |\n| **Multilingual** | Supported | Requires bilingual transcriber |\n| **Scalability** | Unlimited sessions in parallel | One-at-a-time |\n\nFor most research purposes, AI transcription is the obvious choice. Human transcription is worth considering only when: (1) you're working with heavy accents in a language poorly supported by current AI systems, or (2) you need verbatim accuracy for legal or clinical research.\n\n## Using Koji's AI for End-to-End Research Analysis\n\nKoji is different from standalone transcription services because it handles the entire research pipeline — not just audio-to-text conversion.\n\n### How It Works\n\n**Voice interviews:** Koji uses an AI voice agent that conducts the interview conversationally. The transcript is generated in real time during the session, including speaker-labeled turns. After the session ends, analysis runs automatically.\n\n**Text interviews:** Participants type their responses in a chat interface. There's no transcription step — the conversation is already structured text. The AI asks follow-up questions dynamically and collects structured widget responses for quantitative questions.\n\n**Post-session analysis:** For both modes, Koji's analysis pipeline runs automatically after each session completes:\n- Extracts structured answers for each of the study's 6 question types (open_ended, scale, single_choice, multiple_choice, ranking, yes_no)\n- Generates an individual insight summary aligned to the research brief\n- Scores session quality (1–5) based on coverage of key research objectives\n- Tags theme keywords for dashboard filtering\n\n**Report generation:** After sufficient responses, Koji generates an aggregate report that synthesizes findings across all participants — themes, representative quotes, structured data charts, and goal-aligned recommendations. Reports update automatically as new interviews come in.\n\n**Viewing transcripts:** The full transcript for every session is accessible in Koji's interface — with speaker labels, timestamps, and highlighted sections that map to key research objectives. You can search across all transcripts simultaneously. See [viewing interview transcripts](/docs/viewing-interview-transcripts) for details.\n\n### What Makes Koji's Approach Different from Transcription-Only Tools\n\nTools like Otter.ai, Rev, or Descript solve the transcription problem. Koji solves the research problem. The difference:\n\n- **Transcription tools** convert audio to text and may highlight keywords\n- **Koji** designs the study, conducts the interview, transcribes, analyzes, scores quality, extracts structured data, identifies themes, and generates a report — all without a human moderator\n\nFor research teams running more than 5–10 interviews per month, the time savings compound into a meaningful competitive advantage: more studies, faster decisions, better products.\n\n## Best Practices for AI-Transcribed Research\n\n### Design for AI Analysis from the Start\nWhen designing your discussion guide or Koji research brief, use clear, specific questions that map to discrete research objectives. AI analysis is most accurate when participant responses can be cleanly mapped to research goals.\n\n### Use Structured Questions Strategically\nFor data points you want to quantify across participants (satisfaction scores, feature preferences, experience frequency), use structured question types. This produces clean, aggregatable data alongside the qualitative transcript — the best of both worlds.\n\n### Verify Key Quotes\nBefore using a specific quote in a report or presentation, verify it against the original recording or re-read the surrounding transcript context. AI transcription is reliable for analysis; direct quotes deserve a quick check.\n\n### Review AI Themes Critically\nAI theme extraction is a starting point, not a conclusion. Review the suggested themes against your own reading of the transcripts and push back where the AI has over-simplified or missed nuance. The AI saves you hours of mechanical coding; your expertise adds the interpretive layer.\n\n### Keep Raw Transcripts\nEven when AI analysis is your primary workflow, retain access to full transcripts. Stakeholders sometimes want to read the original conversations, and specific transcripts are invaluable when findings are challenged.\n\n## The Future of Research Transcription\n\nThe transcription problem is essentially solved for most research use cases. The frontier is now what happens with transcripts — more nuanced analysis, cross-study comparison, longitudinal pattern detection, and increasingly sophisticated automated report generation.\n\nPlatforms like Koji are building toward a world where the researcher focuses entirely on high-level questions — \"What should we learn?\" and \"What does this mean for our product?\" — while AI handles everything from participant recruitment to interview conduct to analysis synthesis.\n\nFor teams still relying on manual transcription workflows, the productivity gap compounds every month. The question is no longer whether to adopt AI transcription — it's how quickly to make the full shift to AI-moderated research.\n\n## Related Resources\n\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide)\n- [How to Analyze Qualitative Data: From Raw Interviews to Actionable Insights](/docs/how-to-analyze-qualitative-data)\n- [Understanding Quality Scores](/docs/understanding-quality-scores)\n- [Viewing Interview Transcripts](/docs/viewing-interview-transcripts)\n- [Turning Interviews Into Insights: From Raw Data to Action](/docs/turning-interviews-into-insights)\n- [AI-Generated Insights](/docs/ai-generated-insights)\n- [Generating Research Reports](/docs/generating-research-reports)\n","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"AI Transcription for Research Interviews | Koji Docs","metaDescription":"AI transcription for research interviews: how it works, accuracy expectations, and how Koji automates the full pipeline from interview to insight — 10x faster than manual methods.","keywords":["AI transcription","interview transcription","qualitative research","research analysis","automated transcription","voice research"],"aiSummary":"AI transcription converts audio interviews to text in minutes instead of days, enabling research teams to run 10x more studies without proportional time increases. Modern AI achieves 90–95% word accuracy — sufficient for qualitative analysis and theme extraction. Koji goes beyond transcription to automate the full research pipeline: AI conducts the interview (voice or text), transcribes in real time, extracts structured answers for 6 question types, scores session quality (1–5), identifies themes, and generates aggregate reports. This compresses a traditional 60–120 researcher-hour study to 2–4 hours of researcher time. Verify specific quotes against recordings before publishing; AI theme extraction is a starting point that benefits from researcher interpretation.","aiDifficulty":"beginner","aiEstimatedTime":"10 minutes"},{"type":"documentation","id":"f440a402-197c-4b03-a7d4-f120d6ca6eb5","slug":"research-synthesis-guide","title":"Research Synthesis: How to Combine Multiple Studies Into Clear Insights","url":"https://www.koji.so/docs/research-synthesis-guide","summary":"Research synthesis combines findings from multiple studies into an integrated understanding of user needs. Unlike single-study analysis, synthesis reveals what is consistent across contexts, explains contradictions through segmentation, and builds compounding organizational knowledge. Koji's structured question outputs and AI reports are designed for cross-study synthesis.","content":"## The Bottom Line\n\nResearch synthesis is the process of combining findings from multiple studies, sources, or time periods into a coherent picture of user needs, behaviors, and opportunities. It goes beyond analyzing a single study — synthesis asks: \"What does everything we know, taken together, tell us?\" AI-native research platforms like Koji accelerate synthesis by generating structured, comparable outputs across studies, making cross-study pattern recognition a task of minutes rather than weeks.\n\n## Synthesis vs. Analysis: What's the Difference?\n\nAnalysis is what you do with a single study: reading transcripts, identifying themes, tagging quotes. Synthesis is what you do across multiple studies: comparing themes, resolving contradictions, and building an integrated understanding.\n\nA single user interview study tells you what a specific group of participants thinks about a specific problem. Synthesis across three studies — run at different times, with different participant segments — tells you what's consistent, what varies by segment, and what has changed over time.\n\nMany research teams do excellent analysis but skip synthesis. The result is a growing library of research reports that never accumulate into organizational knowledge. Each new study starts from scratch instead of building on what's already known. Research that doesn't synthesize doesn't compound — it just accumulates.\n\n## When to Synthesize\n\nSynthesis is most valuable in four situations:\n\n**1. Before a major product decision**: When deciding whether to build a new feature, enter a new market, or deprecate an old workflow — synthesize all relevant research before making the call. The decision should rest on everything you know, not just the most recent study.\n\n**2. At the start of a new research cycle**: Before designing new studies, synthesize what you already know. You might find the question is already answered, or that you need to design studies to fill specific gaps rather than re-covering ground.\n\n**3. After accumulating 3+ studies on a topic**: Once you have multiple studies touching the same problem space, synthesis extracts compounding value from the investment. Three studies synthesized are worth more than the sum of their parts.\n\n**4. When findings seem contradictory**: If Study A says users want simplicity and Study B says they want more control, synthesis helps you understand the context that resolves the contradiction (usually: different user segments have different needs).\n\n## The Five Core Synthesis Methods\n\n### 1. Thematic Synthesis\n\nIdentify themes across multiple studies and compare their frequency, salience, and context. Thematic synthesis asks: \"What are the recurring ideas, and how do they appear across different studies?\"\n\nIn Koji, each study generates an AI report with automatically identified themes. Thematic synthesis means comparing those theme lists across studies — looking for themes that appear in Study A and Study C but not Study B, and asking why. Themes that persist across multiple studies, participant segments, and time periods are your highest-confidence findings.\n\n### 2. Narrative Synthesis\n\nBuild a coherent story that integrates findings from multiple sources. Narrative synthesis is especially useful when presenting to stakeholders — you're not just listing themes, you're constructing an argument that connects research to decision-making context.\n\nA good narrative synthesis has three parts:\n- **The consistent signal**: What all (or most) studies agree on\n- **The nuance**: Where findings diverge, and what explains the divergence\n- **The implication**: What this integrated picture means for product strategy\n\n### 3. Triangulation\n\nUse findings from studies with different methodologies to validate or challenge a hypothesis. If your problem interview findings and your concept test findings both point to the same root cause, the triangulated evidence is stronger than either alone.\n\nTriangulation is particularly powerful when combining Koji's qualitative interview data with quantitative signals (NPS scores, usage analytics, CSAT data). When the interview themes align with the quantitative trends, you have a high-confidence finding that can withstand stakeholder scrutiny.\n\n### 4. Structured Data Aggregation\n\nOne of Koji's most powerful synthesis capabilities: structured question responses are comparable across studies. If you used a scale question with the same text across three studies — \"How satisfied are you with your current solution, on a scale of 1–10?\" — you can compare the distributions across all three.\n\nThis kind of structured aggregation enables trend tracking over time. Run the same core questions in Q1 and Q3, and you can show whether satisfaction improved or deteriorated — with both the quantitative trend and the qualitative context from open-ended responses.\n\nKoji's six question types — open_ended, scale, single_choice, multiple_choice, ranking, and yes_no — all produce structured outputs that can be compared across studies, making longitudinal synthesis straightforward. The structured questions feature is a core Koji differentiator: while other tools generate transcripts you must manually analyze, Koji produces comparable data points that aggregate automatically.\n\n### 5. Gap Analysis\n\nMap your existing research against your current decision landscape. What questions does your leadership team need to answer? What does your existing research cover? What are the gaps?\n\nGap analysis synthesis often produces the most immediately actionable output: a research agenda that fills specific knowledge gaps, rather than adding more of what you already know. It prevents the common failure mode of running a third study on a question you already have two strong answers to.\n\n## A Step-by-Step Synthesis Process\n\n### Step 1: Inventory Your Research\n\nList all relevant studies, their objectives, participant profiles, and key findings. In Koji, this means reviewing the AI-generated reports for each relevant study. Note which topics, questions, and participant segments are covered.\n\n### Step 2: Build a Synthesis Matrix\n\nCreate a matrix with themes or questions as rows, and studies as columns. For each cell, note whether the study addressed this theme, and what it found. This visual structure immediately reveals coverage, overlap, and gaps.\n\nFor structured question data, extract the average or distribution for each comparable question across studies and add these to the matrix. The combination of qualitative theme coverage and quantitative scores gives you a rich synthesis canvas.\n\n### Step 3: Identify Consistent Signals\n\nLook for themes that appear across multiple studies with consistent characterization. These are your highest-confidence findings — they've been validated by multiple independent research exercises with different participants and potentially different contexts.\n\nA finding supported by three studies with a combined 60 participants is categorically more reliable than a finding from one study with 15 participants. Synthesis lets you cite the stronger evidence.\n\n### Step 4: Examine Contradictions\n\nWhere studies disagree, dig deeper. Are the participant samples different? Were the questions framed differently? Was the context different (e.g., pre-launch vs. post-launch)?\n\nOften, what looks like a contradiction is actually a segmentation insight: Theme X is true for enterprise users but not for SMBs. Or Theme Y was true six months ago but reversed after a product change. These \"contradictions\" are often the most valuable synthesis outputs — they reveal the conditions under which a finding holds.\n\n### Step 5: Build the Integrated Narrative\n\nWrite a synthesis document that presents:\n- The key consistent findings (with citations to supporting studies and participant counts)\n- The nuanced variations and what drives them\n- Confidence levels for each finding (backed by how many studies and how many participants)\n- The implications for decisions\n\n### Step 6: Identify Gaps and Design New Research\n\nSynthesis invariably reveals what you don't yet know. Document those gaps explicitly as questions for future research, with a priority ranking based on which decisions they would inform.\n\n## Using Koji's AI Reports for Synthesis\n\nKoji's AI-generated reports are structured for synthesis, not just for single-study review. Each report includes:\n- **Theme clusters** with supporting quotes — comparable across studies in format\n- **Structured question visualizations** — distribution charts that can be compared side by side\n- **AI summary** — a synthesized paragraph that captures the essential finding\n- **Individual participant insights** — available for granular cross-study analysis\n\nBecause every Koji report follows the same structure, you don't need to manually normalize research outputs from different formats before synthesizing. The work of standardization is already done. This is a significant advantage over teams that run studies in different tools (SurveyMonkey for one study, a moderated interview for another, a Typeform for a third) — Koji's consistent output format makes synthesis dramatically easier.\n\n## Building an Institutional Research Memory\n\nThe long-term value of synthesis practice is organizational: you build a research memory that new team members can access, that persists through team transitions, and that compounds in value over time.\n\nA mature synthesis practice produces:\n- A living \"what we know\" document that is updated after each synthesis cycle\n- A gap log tracking open research questions and their priority\n- A decision log linking product decisions to the research that informed them\n\nThis institutional memory is what separates research-driven organizations from those that rely on intuition. When a new PM joins and asks \"has anyone looked at X before?\", the answer shouldn't be \"probably, but I'm not sure where it is.\" It should be a link to a synthesis document.\n\n## Common Synthesis Pitfalls\n\n**Cherry-picking confirming evidence**: Synthesis done poorly assembles evidence for a pre-existing conclusion. Build in a step that specifically looks for disconfirming evidence before finalizing your synthesis.\n\n**Ignoring sample differences**: Comparing findings from an enterprise user study to a consumer user study without acknowledging the context difference produces misleading synthesis. Always note the participant profile when citing evidence.\n\n**Over-confident single-study findings**: If only one study addresses a finding, it shouldn't be presented as a confirmed insight. Use language that reflects confidence level: \"One study suggests...\" vs. \"Across three studies, we consistently find...\"\n\n**Synthesis paralysis**: Waiting until you have \"enough\" research before synthesizing. Even two studies synthesized are more valuable than a growing library of unconnected reports. Start synthesizing early and update as new evidence comes in.\n\n## Related Resources\n\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — how structured question data enables cross-study comparison\n- [How to Analyze Qualitative Data](/docs/how-to-analyze-qualitative-data) — the single-study analysis that feeds synthesis\n- [Turning Interviews Into Insights: From Raw Data to Action](/docs/turning-interviews-into-insights) — moving from themes to decisions\n- [Writing Research Insight Statements That Drive Action](/docs/writing-insight-statements) — how to communicate synthesized findings\n- [Thematic Analysis Guide](/docs/thematic-analysis-guide) — deep dive on theme identification\n- [Presenting Research Findings to Stakeholders](/docs/presenting-research-findings) — how to present synthesized findings effectively","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Research Synthesis: How to Combine Multiple Studies Into Clear Insights | Koji","metaDescription":"Learn how to synthesize research findings across multiple studies using thematic synthesis, triangulation, structured data aggregation, and gap analysis. A practical guide for researchers and product teams.","keywords":["research synthesis","synthesizing qualitative research","how to synthesize research findings","qualitative synthesis","cross-study analysis","research consolidation"],"aiSummary":"Research synthesis combines findings from multiple studies into an integrated understanding of user needs. Unlike single-study analysis, synthesis reveals what is consistent across contexts, explains contradictions through segmentation, and builds compounding organizational knowledge. Koji's structured question outputs and AI reports are designed for cross-study synthesis.","aiPrerequisites":["Experience running at least 2–3 user research studies","Familiarity with qualitative analysis basics"],"aiLearningOutcomes":["Distinguish analysis from synthesis and know when each is needed","Apply five core synthesis methods: thematic, narrative, triangulation, structured aggregation, gap analysis","Build a synthesis matrix to compare findings across studies","Use Koji structured question data to track metrics over time","Build institutional research memory through consistent synthesis practice"],"aiDifficulty":"intermediate","aiEstimatedTime":"13 minutes"},{"type":"documentation","id":"e88be552-6663-4996-a94d-a275053cbc05","slug":"ai-transcript-analysis-guide","title":"How to Analyze Interview Transcripts with AI: From Raw Conversations to Actionable Insights","url":"https://www.koji.so/docs/ai-transcript-analysis-guide","summary":"AI transcript analysis uses large language models to automatically extract themes, sentiment, quotes, and structured answers from interview transcripts. Koji processes each completed interview in seconds and synthesizes patterns across all participants. Per-transcript analysis extracts structured values and qualitative themes; cross-transcript synthesis identifies recurring themes with frequency counts and selects representative quotes. The Insights Dashboard, Insights Chat, and one-click report generation eliminate 90% of manual analysis work. Best practice: combine structured question types (scale, choice, yes/no) with open-ended questions so the AI has both quantitative data points and qualitative context to work with.","content":"\nInterview transcript analysis is the part of research that takes the longest and delivers the most insight — but historically it has also been the biggest bottleneck. A 45-minute interview generates 6,000+ words of transcript. Analyze 20 interviews manually and you are looking at 40+ hours of reading, highlighting, coding, and pattern-finding before you write a single insight.\n\nAI transcript analysis changes this math fundamentally. What used to take weeks now takes minutes. This guide explains what AI transcript analysis is, how it works, where it delivers real value, and how tools like Koji make end-to-end analysis — from conversation to published report — fully automated.\n\n## What Is AI Transcript Analysis?\n\nAI transcript analysis is the use of large language models to automatically process interview transcripts and surface:\n\n- **Themes**: Recurring topics, concerns, and motivations across participants\n- **Sentiment**: Emotional tone — positive, negative, neutral — at the topic level\n- **Key quotes**: The most representative or compelling things participants said\n- **Structured answers**: Extracting specific responses to defined questions (e.g., \"What was the user's NPS score?\")\n- **Patterns**: Cross-participant connections — who shares the same problem, what the outlier cases reveal\n\nThis is distinct from AI transcription (converting audio to text) — though AI transcript analysis obviously depends on having accurate transcripts first.\n\n## The Traditional Transcript Analysis Process\n\nBefore AI, analyzing interview transcripts involved:\n\n1. **Read** the transcript (15–20 minutes per interview)\n2. **Code** the text — highlight passages and tag them with categories\n3. **Build** an affinity diagram or coding tree grouping related codes\n4. **Identify** themes from clusters of codes\n5. **Write** insight statements from themes\n6. **Extract** representative quotes to support each insight\n7. **Draft** findings into a report or presentation\n\nFor 20 interviews, this process takes 30–60 hours. Most teams doing it manually cut it short — either analyzing fewer interviews than they collected, or synthesizing too quickly without enough depth. The result is expensive data that never fully gets used.\n\n## How AI Transcript Analysis Works\n\nModern AI transcript analysis, as implemented in tools like Koji, uses large language models to process each transcript individually and then synthesize across the full set:\n\n### Per-Transcript Analysis\n\nFor each conversation, the AI:\n- Identifies the questions covered and extracts each answer\n- Pulls structured values for quantitative questions (scores, selections, rankings)\n- Summarizes qualitative answers with key themes and direct quotes\n- Scores response quality (Koji's quality gate flags incomplete or off-topic responses)\n\n### Cross-Transcript Synthesis\n\nAcross all transcripts in a study, the AI:\n- Groups participants by behavioral patterns and answer similarities\n- Identifies recurring themes with frequency counts\n- Selects the most representative quotes for each theme\n- Generates aggregate statistics for structured questions (e.g., average NPS = 6.2, 65% cited \"speed\" as primary concern)\n\n### Report Generation\n\nThe synthesized analysis is formatted into a structured research report with:\n- Executive summary with key findings\n- Per-question analysis with charts for structured questions\n- Theme breakdown with supporting quotes\n- Participant-level insights for individual review\n\nIn Koji, this entire process happens automatically after each interview completes — no manual work required.\n\n## What AI Analysis Gets Right\n\n**Speed**: AI processes a 45-minute interview transcript in under 10 seconds. A 50-interview study is analyzed before a human researcher could finish reading the first transcript.\n\n**Consistency**: Human coders apply categories differently depending on time of day, fatigue, and evolving interpretations. AI applies consistent logic across every transcript in the dataset.\n\n**Scale**: Manual analysis caps out at around 20 interviews before quality degrades from cognitive load. AI handles 500 interviews as easily as 5.\n\n**Structured data extraction**: For studies with structured questions (scales, choices, rankings), AI accurately extracts the values expressed in conversation — even when users deviate from the expected format (\"I would give it maybe a 7, or like a 6.5\" is correctly coded as 7).\n\n**Cross-participant pattern detection**: Finding that \"17 of 32 participants mentioned onboarding confusion before even reaching the first question\" is the kind of meta-pattern that humans often miss in manual analysis.\n\n## What AI Analysis Needs Human Oversight For\n\nAI transcript analysis is excellent but not omniscient. It needs human review for:\n\n**Nuance and context**: AI might code \"I am not sure if I would pay for this\" as negative sentiment when a researcher who heard the full conversation knows the participant is actually a strong buyer considering budget constraints.\n\n**Research direction**: AI tells you what participants said. Humans decide which findings matter for the current decision context.\n\n**Novel categories**: If participants raise unexpected topics outside the study scope, AI analysis should be reviewed to ensure novel themes are not missed or misclassified.\n\n**Verification of critical findings**: High-stakes decisions (major pivots, large investments) warrant human review of raw transcripts to verify AI-surfaced insights before acting.\n\nThe best workflow combines AI speed with human judgment: AI does 90% of the analytical work, human researchers review, interpret, and prioritize the output.\n\n## Using Koji's AI Analysis Features\n\n### Automatic Post-Interview Analysis\n\nEvery completed interview in Koji is automatically analyzed. Within minutes of completion, individual insights are available in the participant profile, including:\n- A quality score (conversations scoring below 3 are flagged and do not consume credits)\n- Structured answers for each question with extracted values\n- Key themes and direct quotes from the conversation\n- Individual insight highlights for quick scanning\n\n### Insights Dashboard\n\nThe insights dashboard aggregates across all interviews in a study, showing:\n- Theme detection across all participants with frequency data\n- Aggregate charts for structured question types (NPS distributions, choice breakdowns, ranking averages)\n- Sentiment trends\n- Individual participant deep-dives for follow-up\n\nLearn more in the [Insights Dashboard guide](/docs/insights-dashboard).\n\n### Insights Chat\n\nKoji's chat interface lets you ask natural language questions about your entire research dataset: \"Which participants mentioned pricing concerns?\", \"What is the most common reason for low satisfaction scores?\", \"Show me everyone who mentioned a competitor by name.\"\n\nThis is particularly powerful for ad-hoc analysis — you do not need to know in advance what you are looking for. The AI searches across all transcripts and surfaces the relevant excerpts. See the [Insights Chat guide](/docs/insights-chat-guide) for full details.\n\n### Report Generation\n\nGenerate a polished research report in one click. The report aggregates all analysis into a structured document with executive summary, per-question findings, theme analysis, and selected quotes. Reports can be published and shared with stakeholders via a public link — no login required for viewers.\n\nSee [Generating Research Reports](/docs/generating-research-reports) and [Publishing and Sharing Reports](/docs/publishing-sharing-reports) for the full walkthrough.\n\n## Structured Questions Make AI Analysis More Powerful\n\nThe quality of AI transcript analysis improves significantly when you include structured question types in your interview design. Open-ended questions produce rich qualitative insight, but they make quantitative aggregation difficult.\n\nAdding structured question types to your Koji study gives the AI precise data points to aggregate:\n\n- **Scale questions** (e.g., NPS 0–10, satisfaction 1–5) produce distribution charts and mean scores\n- **Single choice questions** produce frequency bar charts showing which option was selected most\n- **Multiple choice questions** show stacked frequency for multi-select answers\n- **Ranking questions** produce average position rankings across all participants\n- **Yes/No questions** generate pie charts with binary breakdowns\n\nKoji supports all 6 structured question types — open_ended, scale, single_choice, multiple_choice, ranking, and yes_no. Learn how to design studies that combine both qualitative depth and quantitative breadth in the [Structured Questions Guide](/docs/structured-questions-guide).\n\n## Best Practices for AI Transcript Analysis\n\n**Review AI output before sharing**: AI analysis is a starting point, not a final deliverable. Before sharing a report with stakeholders, review the key findings and verify they align with your reading of the raw transcripts.\n\n**Use Insights Chat to investigate anomalies**: If a theme appears unexpectedly or a finding seems counterintuitive, use the chat interface to drill into which specific participants drove it. Often a small group of outliers generates a misleading signal.\n\n**Set probing depth based on question importance**: Koji allows you to configure how many AI follow-up questions are asked per topic. For your primary research questions, set probing depth to 2–3 to ensure the AI captures enough depth for meaningful analysis.\n\n**Segment analysis by participant group**: If you imported participants from different customer segments, compare themes across segments. What enterprise customers care about may be completely different from what SMB users mention — and flat analysis across both groups can obscure the most important insights.\n\n**Archive transcripts for future comparison**: As you build a research library, the same analysis questions asked across multiple studies (e.g., monthly NPS interviews) become longitudinal data. Koji preserves all transcripts and structured answers for cross-study comparison.\n\n## The Time Savings of AI Transcript Analysis\n\nThe typical research cycle time for a 20-participant qualitative study, before and after AI analysis:\n\n| Task | Manual Process | With Koji AI |\n|------|---------------|--------------|\n| Transcription | 4–8 hours | Included automatically |\n| Coding and theming | 20–30 hours | Fully automated |\n| Report drafting | 8–12 hours | 30 minutes (review and edit) |\n| Stakeholder delivery | 2+ weeks from fieldwork | Same day |\n| **Total researcher time** | **35–50 hours** | **~1–2 hours** |\n\nThe time savings are significant, but the more important shift is strategic: when analysis is instant, you can run research more often, respond to findings faster, and make research a continuous practice rather than an occasional project.\n\n## Getting Started with AI Transcript Analysis in Koji\n\nIf you are new to Koji:\n\n1. **Create a study** with a mix of open-ended questions (for qualitative depth) and structured questions (for quantitative breadth)\n2. **Publish and share** your interview link\n3. **Watch analysis appear** automatically as each interview completes\n4. **Open the Insights Dashboard** to see aggregate themes and structured data charts\n5. **Use Insights Chat** to ask follow-up questions about your data\n6. **Generate a report** and share with stakeholders\n\nThe entire process from setup to shareable report can be completed in a single day — compared to weeks for a traditional manual research cycle.\n\n## Related Resources\n\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide)\n- [AI Transcription for Research Interviews](/docs/ai-transcription-research-interviews)\n- [How to Analyze Qualitative Data](/docs/how-to-analyze-qualitative-data)\n- [Insights Dashboard](/docs/insights-dashboard)\n- [AI-Generated Insights](/docs/ai-generated-insights)\n- [Reading Your Research Report](/docs/reading-your-research-report)\n","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"AI Interview Transcript Analysis: Complete Guide 2026","metaDescription":"How to analyze interview transcripts with AI. Learn how automated analysis surfaces themes, quotes, and patterns — and how Koji reduces a 50-hour manual process to under 2 hours.","keywords":["analyze interview transcripts","interview transcript analysis","AI transcript analysis","qualitative interview analysis","transcript coding software"],"aiSummary":"AI transcript analysis uses large language models to automatically extract themes, sentiment, quotes, and structured answers from interview transcripts. Koji processes each completed interview in seconds and synthesizes patterns across all participants. Per-transcript analysis extracts structured values and qualitative themes; cross-transcript synthesis identifies recurring themes with frequency counts and selects representative quotes. The Insights Dashboard, Insights Chat, and one-click report generation eliminate 90% of manual analysis work. Best practice: combine structured question types (scale, choice, yes/no) with open-ended questions so the AI has both quantitative data points and qualitative context to work with.","aiPrerequisites":["familiarity with qualitative user research methods"],"aiLearningOutcomes":["understand how AI transcript analysis differs from manual coding","use Koji Insights Dashboard and Chat for analysis","combine structured and open-ended questions for richer AI analysis","generate and share research reports from AI-analyzed transcripts"],"aiDifficulty":"beginner","aiEstimatedTime":"10 minutes"},{"type":"documentation","id":"825c92f4-0b46-4d74-b3f1-84f1c4a0ac00","slug":"turning-interviews-into-insights","title":"Turning Interviews Into Insights: From Raw Data to Action","url":"https://www.koji.so/docs/turning-interviews-into-insights","summary":"Turning interview data into actionable insights follows four stages: data preparation, coding, theming, and insight generation. Researchers spend an average of 5-8 hours analyzing each hour of interview data. AI-assisted analysis can accelerate the process while manual validation ensures quality and nuance.","content":"You have finished your interviews. Congratulations — you have raw qualitative data. Now comes the part that separates research that collects dust from research that drives decisions: analysis and synthesis. According to a survey by Maze Research (2023), 61% of UX researchers say that synthesis and analysis is the most time-consuming phase of the research process, with the average researcher spending 5-8 hours analyzing each hour of interview data.\n\nThe good news is that with a clear process, you can turn even a large volume of interview data into structured, actionable insights without losing your mind — or your weekends.\n\n## The Four Stages of Interview Analysis\n\nRaw interviews do not become insights in one step. There is a progression:\n\n```\nRaw Transcripts → Coded Data → Themes → Insights → Recommendations\n```\n\n| Stage | What Happens | Output |\n|-------|-------------|--------|\n| 1. Data preparation | Clean transcripts, organize files | Ready-to-analyze transcripts |\n| 2. Coding | Tag meaningful segments of text | Coded transcript with labels |\n| 3. Theming | Group codes into patterns | Theme map with supporting evidence |\n| 4. Insight generation | Interpret themes into actionable statements | Prioritized insight statements with recommendations |\n\nLet us walk through each stage.\n\n## Stage 1: Data Preparation\n\n### Transcription\n\nIf your interviews were recorded, start with transcription. You need text to analyze, whether you do it manually or with a tool.\n\n**Options:**\n- **Manual transcription**: Most accurate but extremely time-consuming (4-6 hours per hour of audio)\n- **AI transcription**: Services like Otter.ai, Rev, or Whisper produce good drafts that need human review\n- **Platform-native transcription**: If you ran interviews through a platform like Koji, your transcripts are already generated and stored alongside the interview data\n\nRegardless of the method, review transcripts for accuracy, especially around proper nouns, industry jargon, and mumbled sections.\n\n### Organizing Your Data\n\nCreate a consistent structure before you start analyzing:\n\n- One transcript per file or record\n- Consistent naming: `[Participant ID]_[Date]_[Study Name]`\n- A participant matrix tracking demographics and key screening criteria\n- Timestamps or section markers for easy reference\n\n## Stage 2: Coding\n\nCoding is the process of labeling segments of transcript text with descriptive tags. It is the most labor-intensive step but also the most important — your themes are only as good as your codes.\n\n### Open Coding (Bottom-Up)\n\nRead each transcript line by line. When a segment expresses a meaningful idea, assign it a short label:\n\n- \"Onboarding confusion\"\n- \"Price comparison behavior\"\n- \"Trust signal — peer recommendation\"\n- \"Workaround — uses spreadsheet instead\"\n\nDo not worry about categories yet. Generate as many codes as the data demands. You can consolidate later.\n\n### Focused Coding (Top-Down)\n\nAfter open coding 3-5 transcripts, you will notice patterns. Start consolidating similar codes into broader categories:\n\n- \"Onboarding confusion,\" \"Setup frustration,\" \"First-run friction\" → **\"Onboarding experience\"**\n- \"Price comparison,\" \"Competitor evaluation,\" \"Budget constraints\" → **\"Purchase decision factors\"**\n\nApply these focused codes to the remaining transcripts. New codes can still emerge — stay flexible.\n\n### How Many Codes Is Normal?\n\nA typical qualitative study generates 40-80 initial open codes that consolidate into 15-25 focused codes. If you have fewer than 15, you may be coding too broadly. More than 80 suggests you may be coding at too granular a level.\n\n## Stage 3: Theming\n\nThemes are the patterns that emerge when you step back and look at your codes as a whole. A theme answers the question: \"What is this data really telling us?\"\n\n### From Codes to Themes\n\nGroup your focused codes into clusters that tell a coherent story:\n\n| Focused Codes | Theme |\n|--------------|-------|\n| Onboarding experience, Learning curve, Documentation gaps | Users struggle to get started independently |\n| Peer recommendation, Review site behavior, Free trial expectation | Trust is built through peer validation, not marketing |\n| Workaround behaviors, Feature requests, Integration frustrations | The product does not match users' existing workflows |\n\n### Validating Themes\n\nA strong theme has these characteristics:\n- **Supported by multiple participants** (not just one outlier)\n- **Supported by specific quotes and examples** (not vague impressions)\n- **Distinct from other themes** (not overlapping or redundant)\n- **Relevant to your research questions** (not tangential)\n\nAccording to Guest, Bunce, and Johnson (2006) in their landmark study on thematic saturation in Field Methods, 80% of themes are identified within the first 12 interviews of a study. If you are still discovering major new themes after 12 interviews, your sample may be too heterogeneous or your research questions too broad.\n\n## Stage 4: Insight Generation\n\nAn insight is not a theme — it is the *interpretation* of a theme that points toward action.\n\n### The Insight Formula\n\nUse this structure to turn themes into actionable insights:\n\n**\"[User group] [behavior/belief/need] because [underlying reason], which means [implication for the product/business].\"**\n\n**Examples:**\n- \"New users abandon onboarding at step 3 because the terminology does not match their mental model, which means we need to rewrite the setup flow using language from our users' existing workflows.\"\n- \"B2B buyers rely on peer recommendations over marketing materials because they distrust vendor claims, which means our growth strategy should prioritize customer advocacy over advertising.\"\n\n### Prioritizing Insights\n\nNot every insight is equally important. Use a simple prioritization framework:\n\n| Criteria | Weight |\n|----------|--------|\n| **Frequency**: How many participants expressed this? | High |\n| **Intensity**: How strongly did participants feel about it? | High |\n| **Impact**: How much would addressing this change the user experience or business outcome? | High |\n| **Feasibility**: How easily can the team act on this? | Medium |\n\nPlot your insights on a 2x2 matrix of **Impact vs. Effort** to identify quick wins, strategic investments, and deprioritized items.\n\n## Manual vs. AI-Assisted Analysis\n\n### Manual Analysis\n\n**Pros:** Deep immersion in the data, nuanced interpretation, researcher develops strong instincts\n**Cons:** Time-intensive (5-8 hours per hour of interview), does not scale, prone to individual bias\n\n**Best for:** Small studies (5-10 interviews), sensitive or complex topics, when the researcher needs to build deep empathy\n\n### AI-Assisted Analysis\n\n**Pros:** Processes large volumes quickly, consistent coding across interviews, identifies patterns humans might miss\n**Cons:** May miss subtle context, requires human validation, can oversimplify nuanced data\n\n**Best for:** Large studies (15+ interviews), rapid turnaround requirements, pattern identification across high volumes\n\nPlatforms like Koji offer AI-generated themes and insights as a starting point for your analysis. The AI processes all your interview transcripts, identifies recurring patterns, and surfaces themes with supporting quotes. This does not replace your analytical judgment — it gives you a structured draft to validate, refine, and build on. Think of it as having a research assistant who reads every transcript and hands you a first pass.\n\nFor more on how to use AI-generated outputs as part of your workflow, see [AI-generated insights](/docs/ai-generated-insights) and [generating research reports](/docs/generating-research-reports).\n\n## A Real-World Example\n\nImagine you conducted 15 interviews about why users downgrade from a paid plan to free.\n\n**After coding**, you have 52 open codes consolidated into 18 focused codes.\n\n**After theming**, you identify four major themes:\n1. Perceived value gap between tiers\n2. Feature discovery failure (paid features exist but users never found them)\n3. Budget approval friction (user liked it, but could not justify cost to manager)\n4. Competitive alternatives offered a similar core at lower cost\n\n**After insight generation:**\n- \"Users who downgrade often did not discover 3 or more paid features they would have valued, which means improving feature onboarding could directly reduce churn.\"\n- \"Mid-level employees cannot articulate ROI to decision-makers, which means we need to provide shareable justification materials.\"\n\nThese insights point directly to actions: improve feature discovery, create ROI documentation for internal advocacy.\n\n## Common Mistakes to Avoid\n\n1. **Jumping to conclusions before coding**: Starting with themes before systematically coding the data leads to cherry-picking quotes that support your initial impression.\n\n2. **Treating frequency as the only signal**: A finding mentioned by 2 of 15 participants can still be critical if those 2 represent a key segment or if the finding has high business impact.\n\n3. **Losing the participant's voice**: Your insight statements should be supported by direct quotes. If you cannot point to a specific quote that illustrates the insight, the insight may be your interpretation, not the data.\n\n4. **Analyzing in isolation**: Discuss your emerging themes with colleagues before finalizing. Fresh eyes catch blind spots.\n\n## Key Takeaways\n\n- Follow the four-stage process: Preparation → Coding → Theming → Insights\n- Open coding builds your labels; focused coding organizes them into categories\n- Themes describe patterns; insights interpret those patterns and point toward action\n- Use the insight formula: [Who] [does what] because [why], which means [so what]\n- AI-assisted analysis accelerates the process but requires human validation\n- Prioritize insights using frequency, intensity, impact, and feasibility\n\nContinue your analysis journey with [thematic analysis guide](/docs/thematic-analysis-guide) for deeper methodology, or jump to [presenting research findings](/docs/presenting-research-findings) to learn how to share what you have discovered.\n\n## Frequently Asked Questions\n\n**How long should analysis take for a typical study?**\n\nFor a study with 10-15 interviews of 30-45 minutes each, expect to spend 20-40 hours on manual analysis. AI-assisted analysis can reduce this to 5-10 hours of review and refinement time. The time investment is worth it — rushed analysis produces weak insights that do not drive decisions.\n\n**Can I combine manual and AI-assisted analysis?**\n\nAbsolutely, and this is increasingly the recommended approach. Use AI to generate a first pass of codes and themes, then manually review, validate, and refine. This gives you the speed benefit of automation with the quality benefit of human judgment.\n\n**What if two researchers code the same data differently?**\n\nThis is expected and healthy. Inter-coder disagreement reveals ambiguity in the data. Discuss the disagreements, arrive at shared definitions, and re-code where needed. The discussion itself often surfaces insights that neither coder would have reached alone.\n\n**How do I know when I have enough themes?**\n\nYou have enough themes when they collectively address your research questions and when additional themes do not add meaningful new understanding. Most studies produce 4-8 major themes. If you have 15 or more, consider whether some can be consolidated.\n\n**Should I share raw transcripts with stakeholders?**\n\nGenerally, no. Raw transcripts are difficult to parse and can be misleading out of context. Share curated quotes that support specific insights, along with the full analysis. If stakeholders want to go deeper, offer to walk them through selected transcripts.","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Turning Interviews Into Insights","metaDescription":"Transform raw interview transcripts into structured, actionable insights using a four-stage process of coding, theming, and insight generation.","keywords":["qualitative analysis","research insights","thematic analysis","coding interviews","data synthesis","research findings","interview analysis"],"aiSummary":"Turning interview data into actionable insights follows four stages: data preparation, coding, theming, and insight generation. Researchers spend an average of 5-8 hours analyzing each hour of interview data. AI-assisted analysis can accelerate the process while manual validation ensures quality and nuance.","aiPrerequisites":["user-interview-guide"],"aiLearningOutcomes":["Apply the four-stage process from raw transcripts to actionable insights","Conduct open and focused coding of qualitative interview data","Generate insight statements using the who-what-why-so-what formula","Choose between manual and AI-assisted analysis based on study needs"],"aiDifficulty":"intermediate","aiEstimatedTime":"11 min read"},{"type":"documentation","id":"d503cd31-4865-40ab-a83a-1c4600f2754f","slug":"presenting-research-findings","title":"Presenting Research Findings to Stakeholders","url":"https://www.koji.so/docs/presenting-research-findings","summary":"Effective presentation of qualitative research findings increases the likelihood of recommendations being implemented by 2.6x. Key techniques include tailoring format to audience, leading with participant stories, using the data sandwich structure (quantitative context, qualitative quote, implication), and always pairing findings with prioritized recommendations.","content":"The most rigorous research in the world is worthless if it sits in a document nobody reads. Presenting findings is not an afterthought — it is the mechanism through which research drives decisions. According to a study by Yoo and Kim (2019) published in the International Journal of Design, research teams that invested in structured presentation of findings were 2.6x more likely to see their recommendations implemented compared to teams that shared raw reports without a narrative structure.\n\nYour audience does not care about your methodology (much). They care about what you found and what they should do about it.\n\n## Know Your Audience\n\nBefore you design your presentation, understand who is receiving it and what they need:\n\n| Audience | What They Want | Format Preference |\n|----------|---------------|-------------------|\n| Executives / C-suite | Big picture: What did we learn? What should we do? What is the business impact? | Executive summary (1-2 pages), key metrics |\n| Product Managers | Specific pain points, user needs, feature implications, prioritization | Detailed findings with quotes, prioritization framework |\n| Designers | User mental models, workflow patterns, emotional moments, exact language | Journey maps, annotated quotes, video clips |\n| Engineers | Specific use cases, edge cases, technical constraints from users | Structured requirements, user stories derived from findings |\n| Sales / Marketing | Customer language, objections, value perception, competitive context | Quotable sound bites, persona summaries, competitive mentions |\n\nTailor your deliverable to your audience. A single \"research report\" rarely serves all of these stakeholders equally well. Consider creating a core report and audience-specific summaries.\n\n## The Three Report Formats\n\n### 1. Executive Summary\n\n**Length:** 1-2 pages\n**Purpose:** Communicate the headline findings and recommended actions\n**Structure:**\n\n1. **Study overview** (1 paragraph): What was the research question, who did you talk to, and why?\n2. **Top findings** (3-5 bullet points): The most important things you learned\n3. **Recommended actions** (3-5 bullet points): What should we do about it?\n4. **What is at stake** (1 paragraph): What happens if we do not act?\n\n**Example bullet:**\n> \"8 of 12 participants could not find the export feature without help. This means approximately two-thirds of users who need to share reports externally are either using workarounds or abandoning the task entirely.\"\n\n### 2. Detailed Findings Report\n\n**Length:** 5-15 pages depending on study scope\n**Purpose:** Provide the full picture for product teams and design\n**Structure:**\n\n1. **Study background**: Research questions, methodology, participant summary\n2. **Participant overview**: Demographics table, screening criteria, anonymized profiles\n3. **Theme-by-theme findings**: Each theme as a section with supporting evidence\n4. **Cross-cutting patterns**: Observations that span multiple themes\n5. **Prioritized recommendations**: Actionable next steps ranked by impact and effort\n6. **Appendix**: Full interview guide, participant matrix, methodology notes\n\n### 3. Quick-Reference Insight Cards\n\n**Length:** One card per insight (postcard-sized)\n**Purpose:** Shareable, digestible nuggets for team walls, Slack, or documentation\n**Structure per card:**\n\n- **Insight headline** (1 sentence)\n- **Supporting data** (frequency, 1-2 quotes)\n- **Recommended action** (1 sentence)\n- **Evidence strength** (strong / moderate / emerging)\n\nThese are particularly effective for keeping research top-of-mind between formal presentations. Pin them in your team's communication channel or print them for the office wall.\n\n## Storytelling With Data\n\n### Lead With the Story, Not the Method\n\nA common mistake is spending the first 10 minutes of a presentation explaining your methodology. Executives will tune out before you get to the findings.\n\nInstead, start with a participant story:\n\n*\"Let me tell you about Sarah. She's a product manager at a mid-size SaaS company. She signed up for our tool on a Monday, spent 45 minutes trying to set up her first project, and by Wednesday she had downgraded to a competitor's free tier. When I asked her why, she said: 'I could see it was powerful, but I couldn't figure out how to make it do the basic thing I needed.'\"*\n\nNow your audience is hooked. They want to know: How many Sarahs are there? And what can we do about it?\n\n### Use Participant Quotes Effectively\n\nQuotes are the most powerful tool in your presentation arsenal. They bring abstract findings to life with human voice and specificity.\n\n**Rules for effective quote usage:**\n\n1. **Select quotes that are vivid and specific**: \"I gave up after the third time the page loaded wrong\" is better than \"It was frustrating.\"\n2. **Keep quotes short**: 1-2 sentences maximum in a presentation. Longer quotes lose the audience.\n3. **Always attribute to an anonymized participant**: \"P7, Product Manager, Enterprise\" gives the quote context and credibility.\n4. **Use quotes to illustrate, not to prove**: A single quote supports a finding; it does not establish one. Always frame quotes within the broader pattern.\n\n### The Data Sandwich\n\nWhen presenting a finding, use this structure:\n\n1. **Quantitative context** (top bread): \"9 of 12 participants experienced this.\"\n2. **Qualitative depth** (filling): A specific quote or story that makes the number human.\n3. **Implication** (bottom bread): \"This suggests that our onboarding is losing the majority of new users before they experience core value.\"\n\nThis format satisfies both the data-driven and narrative-driven people in your audience simultaneously.\n\n## Visualizing Qualitative Data\n\nQualitative data can be visualized — it just requires different approaches than charts and graphs.\n\n### Theme Maps\n\nShow how themes relate to each other. A simple diagram with themes as nodes and connections as lines helps stakeholders see the system, not just individual findings.\n\n### Quote Walls\n\nA curated collection of participant quotes organized by theme. This works especially well in physical spaces or as a shared digital board.\n\n### Journey Maps\n\nIf your research followed a user journey, map the findings onto the stages of that journey. Annotate each stage with participant quotes, emotional states, and pain points.\n\n### Frequency Tables\n\nWhile qualitative research is not about counting, showing how many participants mentioned a theme provides useful signal:\n\n| Theme | Participants (of 15) | Intensity |\n|-------|---------------------|-----------|\n| Onboarding friction | 12 | High |\n| Pricing confusion | 9 | Medium |\n| Feature discovery gap | 8 | High |\n| Positive support experience | 6 | Medium |\n\nA study by Braun and Clarke (2006) in Qualitative Research in Psychology — the most cited paper on thematic analysis with over 100,000 citations — emphasizes that frequency should supplement, not replace, the researcher's judgment about theme importance.\n\n## Generating Reports With AI\n\nWhen you are working with a large volume of interviews, generating the initial report structure manually is time-consuming. AI-powered platforms like Koji can generate research reports that include theme identification, supporting quotes, and preliminary recommendations based on your interview data.\n\nThese auto-generated reports give you a strong starting draft: themes are surfaced with evidence, participant quotes are linked to findings, and patterns across interviews are highlighted. Your job is to validate the AI's interpretation, add your contextual knowledge, and tailor the narrative for your specific audience.\n\nFor details on how to generate and customize these reports, see [generating research reports](/docs/generating-research-reports) and [publishing and sharing reports](/docs/publishing-sharing-reports).\n\n## Presenting Live: Tips for the Room\n\n### Structure Your Presentation\n\n1. **Hook** (2 minutes): Start with a compelling participant story\n2. **Context** (3 minutes): Brief study overview — who, why, how many\n3. **Findings** (15-20 minutes): Theme by theme, using the data sandwich\n4. **Recommendations** (5 minutes): What to do, prioritized\n5. **Discussion** (10+ minutes): Open the floor for questions and debate\n\n### Handle Pushback Gracefully\n\nStakeholders may challenge your findings. This is healthy. Prepare for common objections:\n\n- *\"That's just 12 people\"*: \"You are right that this is qualitative data, not a statistically representative survey. What qualitative research tells us is *why* people behave a certain way. The consistency across 12 diverse participants gives us confidence in the direction of the finding.\"\n\n- *\"I talked to a customer who said the opposite\"*: \"That is valuable context. In our study, we found [X number] participants who felt this way. There may be a segment difference worth exploring. Can you share which customer that was so we can compare profiles?\"\n\n- *\"We already knew this\"*: \"If the organization already has this insight, that is great validation. The question is whether we have acted on it. Here are specific recommendations for what to do next.\"\n\n## Common Mistakes to Avoid\n\n1. **Burying the lead**: Do not save your most important finding for slide 47. Lead with impact.\n\n2. **Presenting every finding equally**: Not all themes are equally important. Focus 60% of your time on the top 2-3 findings and briefly acknowledge the rest.\n\n3. **Forgetting the \"so what\"**: Every finding needs a recommendation. Data without action is trivia.\n\n4. **Making it about you**: The presentation is about the participants and the team's next moves. Minimize references to your process and maximize focus on what was learned and what to do about it.\n\n5. **Not following up**: Schedule a follow-up meeting 2-4 weeks after the presentation to check whether findings have been incorporated into planning. Research that is never revisited is research that was never used.\n\n## Key Takeaways\n\n- Tailor your format to your audience: executives want headlines, product teams want detail, designers want mental models\n- Start with a participant story, not your methodology\n- Use the data sandwich: quantitative context, qualitative depth, implication\n- Short, vivid, attributed quotes are your most powerful presentation tool\n- AI-generated reports provide strong first drafts that need human refinement and audience tailoring\n- Always pair findings with specific, prioritized recommendations\n\nFor the analysis process that feeds into your presentation, see [turning interviews into insights](/docs/turning-interviews-into-insights). For details on auto-generating report drafts, explore [generating research reports](/docs/generating-research-reports).\n\n## Frequently Asked Questions\n\n**How long should a research presentation be?**\n\nFor a live presentation, 30-45 minutes including discussion time is ideal. For a written report, the executive summary should be 1-2 pages, and the detailed report should be 5-15 pages. Stakeholders rarely read reports longer than 15 pages end-to-end.\n\n**Should I include my interview guide in the report?**\n\nInclude it as an appendix for transparency and reproducibility, but do not expect anyone to read it. The people who want to see the methodology will appreciate having it available. Everyone else will skip to the findings.\n\n**How do I handle findings that contradict what stakeholders want to hear?**\n\nPresent them directly but empathetically. Lead with the strongest evidence, use participant quotes to make the finding human, and frame your recommendation constructively: \"The data suggests [finding], which creates an opportunity to [positive action].\"\n\n**When should I present research findings versus share a written report?**\n\nPresent live when findings are high-stakes, require discussion, or involve organizational change. Share written reports for lower-stakes updates, ongoing tracking studies, or when stakeholders are geographically distributed and scheduling is impractical.\n\n**How do I measure whether my research presentation was effective?**\n\nTrack whether your recommendations appear in sprint planning, roadmap discussions, or design briefs within 4 weeks of the presentation. If they do, the presentation worked. If they do not, follow up to understand what barrier exists between insight and action.","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Presenting Research Findings","metaDescription":"Present qualitative research effectively to stakeholders using storytelling, participant quotes, and structured report formats that drive action.","keywords":["research presentation","stakeholder reporting","qualitative findings","research storytelling","executive summary","research reports","data visualization"],"aiSummary":"Effective presentation of qualitative research findings increases the likelihood of recommendations being implemented by 2.6x. Key techniques include tailoring format to audience, leading with participant stories, using the data sandwich structure (quantitative context, qualitative quote, implication), and always pairing findings with prioritized recommendations.","aiPrerequisites":["turning-interviews-into-insights"],"aiLearningOutcomes":["Structure research reports for executives, product teams, and designers","Use storytelling techniques including the data sandwich and participant quotes","Create executive summaries, detailed findings reports, and insight cards","Handle stakeholder pushback on qualitative research findings"],"aiDifficulty":"intermediate","aiEstimatedTime":"10 min read"},{"type":"documentation","id":"8a0c8f6b-8521-4600-9369-555c89e3ab08","slug":"how-to-analyze-qualitative-data","title":"How to Analyze Qualitative Data: From Raw Interviews to Actionable Insights","url":"https://www.koji.so/docs/how-to-analyze-qualitative-data","summary":"Qualitative data analysis transforms raw interview transcripts and notes into structured insights through a five-step process: immersion, coding, theme identification, insight generation, and findings communication. This guide covers every step in detail, including how AI platforms like Koji automate the most time-consuming parts of analysis to dramatically speed up the research cycle.","content":"Qualitative data analysis is the process of transforming raw interview transcripts, observation notes, and open-ended responses into structured insights that drive decisions. It's also where most research efforts fall apart — either because the process is too informal to be rigorous, or so slow that findings arrive after the decisions have already been made.\n\nThis guide walks you through a proven approach to analyzing qualitative data: fast enough to keep pace with product teams, rigorous enough to be trusted.\n\n## Why Qualitative Analysis Is Hard\n\nQualitative data doesn't analyze itself. Unlike survey ratings or analytics events, interview transcripts and observation notes require interpretation. Two researchers can look at the same data and reach different conclusions — not because one is wrong, but because themes aren't inherent in data; they emerge through careful, structured analysis.\n\nThe challenges:\n- **Volume**: A single 60-minute interview generates 6,000–10,000 words of transcript.\n- **Subjectivity**: Without a structured process, analysis can drift toward confirming what you already believed.\n- **Time**: Manual thematic coding is slow. Even experienced researchers spend 3–5 hours per interview in analysis.\n- **Communication**: Even great analysis is wasted if findings aren't translated into formats stakeholders can act on.\n\nAI-native research platforms like Koji are changing the time equation dramatically — automatically analyzing each interview as it's completed, clustering themes across participants, and surfacing representative quotes without manual coding. But understanding the underlying process makes you a better judge of AI-generated analysis and better equipped to go deeper when findings warrant it.\n\n## The Five-Step Qualitative Analysis Process\n\n### Step 1: Immerse Yourself in the Data\n\nBefore coding or categorizing anything, read or listen to all of your data with fresh eyes.\n\nIf you have 8 interviews, read all 8 transcripts (or review recordings) before you start tagging a single thing. This prevents the common mistake of forming a premature framework in the first interview that biases how you interpret all subsequent interviews.\n\nDuring this phase, note your first impressions — what surprised you, what resonated, what didn't fit your expectations. These initial reactions are often signals worth exploring.\n\n**Pro tip**: For AI-analyzed interviews in Koji, review the individual interview summaries before looking at the aggregate report. Reading each interview on its own terms before seeing the synthesized themes helps you evaluate the quality of the analysis.\n\n### Step 2: Create a Coding Framework\n\nCoding is the process of labeling segments of data with tags that capture what's happening in that passage — the topic, the emotion, the behavior, or the concept.\n\nThere are two approaches:\n\n**Inductive coding** (bottom-up): You generate codes as you read the data, without a predefined framework. This approach is more open to unexpected findings. Use it for exploratory research where you don't yet have strong hypotheses.\n\n**Deductive coding** (top-down): You start with a predefined framework — your research questions, your hypotheses, or a theoretical model — and code data against those categories. Use it for evaluative research where you're testing specific ideas.\n\nMost product research benefits from a blend: start with your research questions as a loose framework, but stay open to codes that don't fit neatly.\n\n**Common code types:**\n- **Descriptive codes**: What is the participant talking about? (e.g., \"payment flow\", \"onboarding\")\n- **Process codes**: What is the participant doing or experiencing? (e.g., \"switching tools\", \"asking for help\")\n- **Emotion codes**: How does the participant feel? (e.g., \"frustrated\", \"confused\", \"delighted\")\n- **Value codes**: What does the participant care about or prioritize? (e.g., \"time savings\", \"trust\")\n\n### Step 3: Apply Codes to Your Data\n\nWork through each transcript systematically, applying codes to relevant passages. Highlight quotes that feel significant — you'll use these as evidence later.\n\nPractical tips for this phase:\n- Code at the level of ideas, not sentences. A single passage might warrant multiple codes.\n- Don't over-code. Not everything needs a tag. Focus on what's relevant to your research question.\n- Keep a running list of your codes as they develop. When a new code emerges, scan previous interviews to see if you missed it there.\n\nIf you're using Koji, the AI has already generated an initial code structure from your interviews. Use this as a starting point — verify that the themes resonate, add nuance where needed, and look for anything the automated analysis may have missed.\n\n### Step 4: Identify Themes and Patterns\n\nThemes are patterns across multiple data points — things that multiple participants said, experienced, or felt. A single compelling quote is not a theme. A pattern across 6 of 8 interviews is.\n\nTo move from codes to themes:\n\n1. **Group related codes**: Gather all passages with similar codes. Look for codes that cluster naturally together.\n2. **Name each theme**: Give each cluster a clear, descriptive name that captures what the group of codes has in common.\n3. **Test each theme against your data**: Does it hold up across participants, or is it driven by one outlier? A theme supported by a single participant should be noted as a tentative finding, not a conclusion.\n4. **Look for relationships between themes**: Sometimes the most interesting finding isn't a single theme but the relationship between two of them. (e.g., \"Users want more control over X, but they feel overwhelmed when given it.\")\n\n**A practical output**: A thematic map — a visual diagram showing your themes and how they relate to each other. Even a rough sketch is useful for communicating your analytical framework to stakeholders.\n\n### Step 5: Generate Insights and Recommendations\n\nAn insight is not a theme. A theme is: \"Users feel uncertain about pricing.\" An insight is: \"Users feel uncertain about pricing because they can't predict their monthly bill — which causes them to delay upgrading even when they've hit usage limits.\"\n\nThe difference is explanation and implication. An insight explains *why* the pattern exists and implies *what to do* about it.\n\nTo move from theme to insight, ask:\n- **Why does this pattern exist?** What's driving this behavior or emotion?\n- **What does this mean for our product or strategy?**\n- **What would need to change for this to be different?**\n\nFor each insight, collect 2–3 supporting quotes. Direct quotes from participants are the most persuasive evidence for insights — they make abstract themes concrete and give skeptical stakeholders a window into what you actually heard.\n\n## Structuring Your Findings Document\n\nThe findings document is where analysis meets action. It should be short, specific, and tied to decisions in progress.\n\nA simple structure that works:\n\n**Executive summary** (1 paragraph): What are the 2–3 most important things you learned, and what do they mean for the product?\n\n**Key insights** (one section per insight):\n- Insight headline (one sentence)\n- Explanation (2–3 sentences)\n- Supporting quotes (2–3 direct quotes)\n- Implication (what this means for the product — one sentence)\n\n**Recommendations** (optional): Specific actions for product, design, or strategy teams\n\n**Methodology note**: Brief description of who you spoke to and how\n\nKeep it to 2–4 pages for most studies. If stakeholders want more depth, they'll ask. Start short.\n\n## How AI Accelerates Qualitative Analysis\n\nThe most time-consuming parts of qualitative analysis — reading transcripts, applying codes, clustering themes, and writing up findings — can now be substantially automated.\n\nPlatforms like Koji analyze each interview in real-time as it's completed. By the time your last participant finishes their conversation, the platform has:\n- Transcribed every interview\n- Identified recurring themes across participants\n- Scored sentiment at the response level\n- Surfaced representative quotes for each theme\n- Generated an aggregate report you can share immediately\n\nThis doesn't eliminate the need for human analysis. It eliminates the most laborious parts — giving researchers more time to go deep on the findings that matter most.\n\nAccording to Forrester Research, analysis and synthesis represent the most time-intensive phase of qualitative research, consuming 35–40% of total research time. AI-assisted analysis can cut this phase by 60–70%, enabling research teams to run studies more frequently and deliver insights faster.\n\n## Common Qualitative Analysis Mistakes\n\n**Reading to confirm, not to learn.** When you already have a hypothesis, it's tempting to code everything that confirms it and ignore what doesn't. Actively look for disconfirming evidence — it's often the most valuable finding.\n\n**Treating one participant as a pattern.** A single compelling quote is not a theme. Wait for patterns to emerge across multiple participants before drawing conclusions.\n\n**Skipping the immersion phase.** Jumping straight to coding without reading all the data first leads to premature frameworks that distort your analysis.\n\n**Confusing themes with insights.** \"Users are frustrated with onboarding\" is a theme. \"Users are frustrated with onboarding because they don't understand which features are available on their plan, creating anxiety about accidentally hitting a paywall\" is an insight.\n\n**Over-reporting.** Presenting every finding makes it harder for stakeholders to know what to act on. Prioritize ruthlessly — surface the 3–5 insights that matter most.\n\n## Key Takeaways\n\n- Qualitative analysis transforms raw transcripts into structured insights through a five-step process: immerse, code, cluster themes, generate insights, and communicate findings.\n- An insight explains why a pattern exists and implies what to do about it — a theme alone is not actionable.\n- Support every insight with 2–3 direct quotes to make abstract findings concrete for stakeholders.\n- AI-native platforms like Koji automate the most time-consuming parts of analysis — coding, theme clustering, and quote extraction — enabling faster, more frequent research cycles.\n- A good findings document is short (2–4 pages), insight-led, and tied to specific product decisions.\n\n## Frequently Asked Questions\n\n**Q: How long does qualitative data analysis take?**\nA: Manual analysis of 8 interviews typically takes 3–5 days for an experienced researcher. AI-assisted platforms like Koji can generate a synthesized analysis within hours of the last interview completing, dramatically compressing the timeline.\n\n**Q: How many themes is the right number?**\nA: Typically 3–7 themes for a focused qualitative study. Too few themes may mean you're being too general; too many suggests you haven't finished grouping. Let the data drive the number, not a predetermined target.\n\n**Q: What's the difference between a code and a theme?**\nA: A code labels what's happening in a specific passage (\"pricing concern\", \"confusion about features\"). A theme is a pattern across multiple passages and participants — a higher-level grouping that captures something significant about the data as a whole.\n\n**Q: Do I need special software to analyze qualitative data?**\nA: Not necessarily. Simple studies can be analyzed in a spreadsheet or even with sticky notes (affinity mapping). For larger studies, dedicated research platforms like Koji handle analysis automatically. For manual coding of large datasets, tools like Dovetail or Notion databases can help organize your work.\n\n**Q: How do I know if my analysis is rigorous enough to trust?**\nA: Check that each theme is supported by multiple participants (not just one), that you've actively looked for disconfirming evidence, and that a colleague can follow your reasoning from raw quote to theme to insight. If you can show your work, the analysis is rigorous enough.\n\n\n---\n\n## Related Resources\n\n- [Coding Qualitative Data](/docs/coding-qualitative-data) — Step-by-step coding guide\n- [Thematic Analysis Guide](/docs/thematic-analysis-guide) — Theme identification\n- [Affinity Mapping](/docs/affinity-mapping) — Visual data organization\n- [AI-Generated Insights](/docs/ai-generated-insights) — Automated Koji analysis\n- [Presenting Research Findings](/docs/presenting-research-findings) — Stakeholder communication\n\n*Explore [structured questions](/docs/structured-questions-guide) for pre-structured data that accelerates analysis.*","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"How to Analyze Qualitative Data — Koji","metaDescription":"A step-by-step guide to qualitative data analysis: from coding raw transcripts to synthesizing themes and generating insights that drive product decisions.","keywords":["how to analyze qualitative data","qualitative data analysis","thematic analysis","interview data analysis","qualitative research analysis","coding qualitative data","analyzing interview transcripts"],"aiSummary":"Qualitative data analysis transforms raw interview transcripts and notes into structured insights through a five-step process: immersion, coding, theme identification, insight generation, and findings communication. This guide covers every step in detail, including how AI platforms like Koji automate the most time-consuming parts of analysis to dramatically speed up the research cycle.","aiPrerequisites":["user-interview-guide","thematic-analysis-guide"],"aiLearningOutcomes":["Apply a five-step process to analyze qualitative interview data","Distinguish between codes, themes, and insights","Generate insights that explain why patterns exist and imply what to do","Structure a findings document that stakeholders read and act on","Use AI-assisted analysis to compress research timelines"],"aiDifficulty":"intermediate","aiEstimatedTime":"12 min read"},{"type":"documentation","id":"d5d56332-637b-4cc2-84b7-0c362a8e4310","slug":"customer-feedback-analysis","title":"Customer Feedback Analysis: How to Turn Raw Input Into Actionable Insights","url":"https://www.koji.so/docs/customer-feedback-analysis","summary":"This guide covers the complete customer feedback analysis process: defining the right question, collecting from the right sources, coding and categorizing data, identifying themes, prioritizing findings, and sharing actionable recommendations. It explains how AI platforms like Koji automate the full analysis pipeline, compressing 30 hours of manual work into under 3 hours.","content":"Collecting customer feedback is the easy part. Most teams have more feedback than they know what to do with — support tickets, NPS surveys, app store reviews, interview transcripts, sales call notes. The hard part is turning that flood of raw input into clear, actionable insights that actually change how you build.\n\nThis guide covers the full customer feedback analysis process: how to organize feedback, what analysis methods to use, common mistakes to avoid, and how AI is compressing weeks of analysis into hours.\n\n## Why Most Customer Feedback Goes to Waste\n\nResearch from Forrester shows that companies collect 400% more customer feedback than they did a decade ago — but only 12% of that feedback actually influences product decisions. The gap isn't a data problem. It's an analysis problem.\n\nCommon failure modes:\n- **Recency bias**: The loudest, most recent voice shapes the roadmap\n- **Selection bias**: Support tickets overrepresent frustrated power users, not the silent majority\n- **Analysis paralysis**: Teams receive too much feedback and freeze instead of synthesizing\n- **Context collapse**: A quote without the surrounding conversation often means the opposite of what it appears to\n- **No close loop**: Insights are documented but never connected to specific decisions\n\nGood customer feedback analysis fixes all of these with a systematic approach.\n\n## Step 1: Define What Question You're Answering\n\nAnalysis without a question is just categorization. Before you touch a single piece of feedback, define the decision you're trying to inform.\n\nExamples:\n- \"Should we prioritize mobile improvements or third-party integrations in Q3?\"\n- \"Why did our trial-to-paid conversion rate drop 8% last quarter?\"\n- \"What's preventing enterprise customers from expanding their seat count?\"\n\nWith a clear decision in mind, irrelevant feedback falls away and relevant signals pop. Without it, everything feels equally important — which means nothing is.\n\n## Step 2: Collect Feedback from the Right Sources\n\nDifferent feedback sources capture different signals. The best analyses triangulate across multiple types:\n\n| Source | What It Captures | Limitation |\n|--------|-----------------|------------|\n| Support tickets | Acute pain, blockers, bugs | Overrepresents power users |\n| NPS follow-ups | High-level satisfaction drivers | Low response depth |\n| App store reviews | Emotional reactions, feature wishes | Hard to verify context |\n| Sales call notes | Objections, competitive context | Subject to rep framing |\n| User interviews | Deep context, root causes, \"why\" | Small N, slow to collect |\n| Exit surveys | Churn reasons | Rationalized post-hoc |\n\nThe most valuable signal is usually qualitative depth: conversations and interviews that let customers explain *why*, not just *what*. Platforms like Koji conduct these conversations at scale — giving you the depth of individual interviews multiplied across dozens of participants, with consistent questioning and automatic analysis.\n\n## Step 3: Code and Categorize Your Data\n\nCoding is the practice of labeling segments of feedback with descriptive tags so you can group similar responses together.\n\n**Deductive coding** starts with predefined categories (your research questions) and assigns feedback to them.\n\n**Inductive coding** starts with open reading and lets categories emerge from the data.\n\nFor most product feedback analysis, use a hybrid approach:\n1. Start with 4-6 predefined theme categories based on your research question\n2. Add open-ended codes as you read for themes you didn't anticipate\n3. After reading 20% of your corpus, consolidate redundant codes and define your final codebook\n4. Apply consistent codes to all remaining feedback\n\n**Practical coding system:**\n- Use two layers: theme (e.g., \"Onboarding\") and sub-theme (e.g., \"Setup Time\")\n- Add a sentiment code (positive / negative / neutral) to each coded segment\n- Flag \"high signal\" quotes worth using in stakeholder presentations\n- Note participant segment (role, company size, tenure) for context\n\n## Step 4: Identify Themes and Patterns\n\nThemes are recurring patterns that appear across multiple participants. A theme isn't just any topic that came up — it needs to be:\n\n1. **Frequent**: Mentioned by at least 20% of respondents (for studies of 5+)\n2. **Substantial**: The quotes have meaningful content, not just a passing mention\n3. **Relevant**: Connected to your research question\n4. **Distinct**: Not a restatement of another theme\n\nFor each theme, document:\n- **Label**: A 3-5 word name for the theme\n- **Description**: One sentence defining what this theme covers\n- **Frequency**: How many participants mentioned it\n- **Representative quotes**: 2-3 quotes that best illustrate it\n- **Sentiment**: Overall emotional valence (positive/negative/mixed)\n- **Implications**: What this theme means for your decision\n\nKoji's AI does this automatically across all interview transcripts. After generating a research report, you'll see theme cards with frequency data, representative quotes, and sentiment scores — exactly what you'd produce manually, but in minutes rather than days.\n\n## Step 5: Prioritize Findings by Impact\n\nNot all themes are equally actionable. Use a 2x2 to prioritize:\n\n| | High Frequency | Low Frequency |\n|-|----------------|---------------|\n| **High Impact** | Critical: Fix now | Important: Investigate further |\n| **Low Impact** | Interesting: Monitor | Noise: Set aside |\n\nA finding that affects 80% of participants in a minor way may be less important than one that affects 20% of participants who are considering churning.\n\nPro tip: Weight by participant segment. Feedback from your target ICP carries more weight than feedback from users who don't match your ideal customer profile.\n\n## Step 6: Turn Insights Into Actionable Recommendations\n\nThe gap between \"here's what we learned\" and \"here's what we should do\" is where most analysis fails. Insights without recommendations gather dust.\n\nFor each critical finding, write a recommendation using this structure:\n\n> **Finding**: One sentence describing what you learned\n> **So what**: Why this matters for the decision or business\n> **Recommended action**: Specific thing to change, test, or investigate further\n> **Confidence level**: High / Medium / Low — based on sample size and source quality\n\nExample:\n> **Finding**: 6 of 8 enterprise participants described our permission system as a blocker to wider team adoption.\n> **So what**: This directly prevents seat expansion, which accounts for 60% of our expansion revenue opportunity.\n> **Recommended action**: Run a dedicated discovery sprint on permissions UX before Q4 enterprise push.\n> **Confidence level**: High — consistent across segments, supported by sales data.\n\n## Step 7: Share Findings in the Right Format\n\nResearch that doesn't reach decision-makers doesn't change decisions. Match your format to your audience:\n\n- **Executive summary** (1 page): 3 findings + 3 recommendations + confidence level\n- **Full research report**: All themes, supporting quotes, methodology, limitations\n- **Topline readout** (Slack or email): 3 bullet points + link to full report\n- **Shareable report URL**: Published report that stakeholders can explore directly\n\nKoji auto-generates publishable reports that stakeholders can browse — including charts, theme breakdowns, and traceable quotes — without requiring the researcher to present them live.\n\n## How AI Transforms Customer Feedback Analysis\n\nThe traditional analysis cycle — collect, transcribe, code, theme, report — takes 20-40 hours for a typical 10-interview study. AI reduces this dramatically:\n\n| Stage | Manual Time | AI-Assisted Time |\n|-------|------------|------------------|\n| Transcription | 1 hr per interview | Real-time (0) |\n| Initial coding | 30 min per interview | 2 min per interview |\n| Theme extraction | 4-6 hours | 5 minutes |\n| Report writing | 4-8 hours | 10 minutes |\n| Total (10 interviews) | ~30 hours | ~3 hours |\n\nPlatforms like Koji handle the entire pipeline automatically. The AI identifies themes, extracts representative quotes, scores sentiment, and generates an executive summary — with full citation trails so you can verify every finding against the source transcript.\n\nThe researcher's role shifts from doing the analysis to validating and contextualizing it. That's a fundamentally better use of research expertise.\n\n## Common Mistakes in Customer Feedback Analysis\n\n**Treating frequency as importance**\nThe most-mentioned theme isn't necessarily the most important one. A theme mentioned by 3 churned enterprise customers may be more actionable than one mentioned by 20 free users.\n\n**Quoting out of context**\nA quote that looks like strong product feedback often means something different in context. Always read the full exchange around a quote before using it in a presentation.\n\n**Confirmation bias**\nWe find what we're looking for. Give your analysis brief to someone who didn't run the study before finalizing themes. Ask: \"What am I missing?\"\n\n**Over-analyzing small samples**\nFive interviews can reveal a pattern worth investigating. They cannot conclusively validate a product strategy. Calibrate your confidence claims to your sample size.\n\n**Analysis without action**\nThe purpose of analysis is decisions. If your research report doesn't change something — a roadmap item, a hypothesis, a stakeholder belief — something went wrong.\n\n## Tips & Best Practices\n\n- **Analyze as you go** — read transcripts during data collection so emerging themes can inform remaining interviews\n- **Use a consistent codebook** — define your codes before you start coding, not while you're in the middle of a transcript\n- **Capture exact quotes** — paraphrases introduce researcher interpretation; exact quotes let the customer speak\n- **Share surprises, not just confirmations** — findings that contradict your hypothesis are often the most valuable\n- **Close the loop** — follow up with key participants when their feedback leads to a product change\n\n## Frequently Asked Questions\n\n**How many customers do I need for reliable feedback analysis?**\nFor qualitative research, 8-12 participants per distinct segment typically reaches thematic saturation — the point where new interviews stop surfacing new themes. For quantitative feedback, you need statistical significance based on your customer base size and desired confidence level.\n\n**What's the difference between customer feedback analysis and user research?**\nUser research is a controlled process with a defined research brief, recruitment criteria, and methodology. Customer feedback analysis works with feedback that arrives organically. User research gives you more control over what questions get answered; feedback analysis reveals what customers are volunteering unprompted.\n\n**Can AI replace human analysis of customer feedback?**\nAI is excellent at extracting themes, counting frequencies, and identifying sentiment patterns across large datasets. Human judgment is still essential for interpreting nuance, contextualizing findings within business strategy, and making recommendations. The best teams use AI for mechanical work and humans for judgment.\n\n**How do I analyze feedback when different customers say contradictory things?**\nContradictions are signal, not noise. When customers say opposite things, segment by who's saying what. Often, contradictory feedback maps onto different customer segments, use cases, or stages in the customer journey. Koji's analysis automatically surfaces these contradictions as multi-perspective themes.\n\n**How does Koji analyze customer feedback automatically?**\nWhen participants complete interviews on Koji, the platform automatically transcribes, codes, and themes every conversation. The AI generates an aggregate report with theme frequency, sentiment analysis, representative quotes, and actionable recommendations — updated in real-time as new interviews complete.\n\n---\n\n## Related Resources\n\n- [NPS Follow-Up Interviews](/docs/nps-follow-up-interviews) — Go deeper on feedback scores\n- [Sentiment Analysis Guide](/docs/sentiment-analysis-interviews) — Emotional pattern analysis\n- [Thematic Analysis Guide](/docs/thematic-analysis-guide) — Systematic theme identification\n- [Voice of Customer Guide](/docs/voice-of-customer-survey-guide) — Broader VoC programs\n- [AI-Generated Insights](/docs/ai-generated-insights) — Automated analysis with Koji\n\n*Explore [structured questions](/docs/structured-questions-guide) for combining feedback scales with AI-powered analysis.*","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Customer Feedback Analysis — Koji Docs","metaDescription":"Turn raw customer feedback into actionable insights with a systematic analysis process. Learn how AI tools compress 30 hours of manual analysis into under 3 hours.","keywords":["customer feedback analysis","how to analyze customer feedback","customer feedback analysis tool","qualitative feedback analysis","analyze customer interviews","customer insight analysis","feedback analysis process"],"aiSummary":"This guide covers the complete customer feedback analysis process: defining the right question, collecting from the right sources, coding and categorizing data, identifying themes, prioritizing findings, and sharing actionable recommendations. It explains how AI platforms like Koji automate the full analysis pipeline, compressing 30 hours of manual work into under 3 hours.","aiPrerequisites":["Basic understanding of qualitative research concepts"],"aiLearningOutcomes":["Define a clear research question before starting analysis","Apply deductive and inductive coding to customer feedback","Identify themes with frequency, sentiment, and representative quotes","Prioritize findings by impact and relevance to your decision","Write actionable recommendations with confidence levels","Share findings in formats matched to different stakeholder audiences"],"aiDifficulty":"intermediate","aiEstimatedTime":"9 min read"},{"type":"documentation","id":"7f61898f-32a2-41ae-9ea6-10f1186ba82d","slug":"coding-qualitative-data","title":"How to Code Qualitative Data: A Step-by-Step Guide","url":"https://www.koji.so/docs/coding-qualitative-data","summary":"This guide explains how to code qualitative data from research interviews — covering open coding, focused coding, codebook construction, and theme development. It walks through both manual and AI-assisted approaches, showing how platforms like Koji reduce qualitative analysis time from weeks to hours by automating synthesis while preserving the researcher's analytical judgment.","content":"Coding qualitative data is the analytical bridge between raw interview transcripts and meaningful insights. It is the process of assigning labels — called codes — to passages of text so you can organize, compare, and draw conclusions from unstructured conversation data. Without coding, you are left with a pile of transcripts and a vague sense that \"people mentioned time a lot.\" With coding, you have structured evidence you can act on.\n\nAccording to a 2022 User Research Industry Report, qualitative analysis and synthesis is cited as the most time-consuming part of the research process by 67% of researchers — often taking 3-5x longer than the interviews themselves. Platforms like Koji automate the mechanical parts of this process, but understanding how qualitative coding works makes you a sharper analyst regardless of which tools you use.\n\n## What Is a Qualitative Code?\n\nA code is a short label you assign to a passage of text that captures its essential meaning. Codes might represent:\n\n- A problem a participant mentioned (\"difficulty with manual synthesis\")\n- A behavior they described (\"checks analytics every morning\")\n- A concept they expressed (\"time as the limiting factor\")\n- A sentiment (\"excitement about AI tools\")\n\nCodes turn messy, narrative-rich conversations into structured categories you can count, compare across participants, and reason about systematically.\n\n## The Three Types of Qualitative Coding\n\n### Deductive Coding (Top-Down)\n\nYou start with a predefined list of codes — usually based on your research questions or hypotheses — and apply them to the data.\n\n**When to use it:** When you are testing specific hypotheses or need to stay focused on pre-defined themes. Best for evaluative research where you already know what you are looking for.\n\n**Example:** You are evaluating whether your onboarding flow is confusing. Your codes might be: \"Confusion point,\" \"Successful step,\" \"Requested help,\" \"Abandoned flow.\"\n\n### Inductive Coding (Bottom-Up)\n\nYou read the data with an open mind and let codes emerge naturally from what participants actually said. No predefined framework.\n\n**When to use it:** When you are doing exploratory or discovery research and do not want to constrain what you might find. Best for generative research.\n\n**Example:** Reading transcripts, you notice participants keep mentioning \"not knowing where to start.\" That becomes a code — one you never would have anticipated before reading the data.\n\n### Hybrid Coding (Most Common)\n\nStart with a few anchor codes from your research questions, then add new codes freely as you encounter unexpected themes. Most experienced researchers use this approach because it balances structure with discovery.\n\n## How to Code Qualitative Data: Step by Step\n\n### Step 1: Prepare Your Data\n\nBefore coding, your data needs to be in a readable, organized format.\n\n- **Transcripts**: Convert audio recordings to text. AI transcription tools have made this fast and affordable.\n- **Clean formatting**: Remove non-verbal notations unless analytically relevant (e.g., \"[laughs]\" usually is not).\n- **Organize consistently**: Each interview should be a separate document with a clear participant ID and date.\n\nPro tip: If you used Koji to conduct your interviews, transcripts are generated automatically and ready for analysis without any transcription step.\n\n### Step 2: Build Your Codebook\n\nA codebook is your master reference for every code, complete with definitions and examples. Draft it before you start coding, and update it as you go.\n\nFor each code, document:\n- **Code name**: Short, memorable label (e.g., PAIN_SYNTHESIS)\n- **Definition**: What this code means precisely (\"Participant expresses frustration or difficulty with the analysis or synthesis phase of their research work\")\n- **Inclusion example**: A transcript excerpt that fits this code\n- **Exclusion note**: What does NOT qualify (e.g., complaints about data collection, which would go under a different code)\n\nA well-maintained codebook is what makes your analysis replicable and defensible. If a colleague questions your findings, the codebook is your evidence.\n\n### Step 3: First-Pass Coding (Open Coding)\n\nRead each transcript systematically and label passages with codes that capture their essence.\n\n- Work passage by passage, not interview by interview\n- Assign multiple codes to a single passage if needed — a quote often captures multiple ideas\n- Write analytical memos as you go: brief notes to yourself about emerging patterns, hunches, or questions\n- Do not try to draw conclusions yet — this pass is about labeling, not interpreting\n\nAt the end of open coding, you might have 30-60 distinct codes across all your data. That is normal and expected.\n\n### Step 4: Focused Coding (Second Pass)\n\nReview your initial codes and look for opportunities to refine:\n\n- **Merge codes** that capture the same underlying concept under different labels\n- **Split codes** that are actually capturing two distinct ideas\n- **Eliminate codes** that appeared only once and do not seem analytically significant\n- **Rename codes** that are ambiguous or too literal\n\nThe goal is to reduce your 30-60 initial codes to a more manageable set of 10-20 meaningful categories.\n\n### Step 5: Build Themes\n\nThemes are higher-order patterns that group related codes into a coherent story. A theme answers the question: \"What is the underlying narrative that these codes collectively tell?\"\n\nFor example:\n- **Codes**: PAIN_SYNTHESIS, TIME_COST, TOOL_SWITCHING, MANUAL_EFFORT\n- **Theme**: \"The analysis phase of research is a painful bottleneck that consumes disproportionate researcher time and prevents teams from running more studies\"\n\nEach theme should be supported by multiple codes across multiple participants. A pattern that only appears in one interview is an interesting observation, not a research theme.\n\n### Step 6: Assess Frequency and Saturation\n\nWith themes defined, you can now measure:\n\n- **Frequency**: How often does each theme appear? Across how many participants?\n- **Cross-segment comparison**: Do themes differ between customer segments, roles, or demographics?\n- **Saturation check**: Are new interviews adding new themes, or are you seeing the same patterns repeated? When new data stops introducing new themes, you have reached saturation — a signal that your sample is sufficient.\n\n## Common Qualitative Coding Mistakes\n\n**Coding too literally.** \"Participant said they felt overwhelmed\" is surface-level. Code the meaning: \"Cognitive overload in the research workflow.\" Literal codes do not generate insights.\n\n**Undercoding.** Many researchers only tag the obvious moments and miss subtle but important signals. Everything participants say is potentially significant — especially hesitations, qualifications, and what they do not say.\n\n**Skipping the codebook.** Without clear definitions, two researchers coding the same data will produce very different results. Even if you are working alone, a codebook disciplines your thinking.\n\n**Ignoring disconfirming evidence.** It is tempting to code only the passages that confirm your hypotheses. Good analysis requires actively looking for contradictions and exceptions — they often reveal the most nuanced insights.\n\n**Not keeping analytical memos.** Your thoughts during coding are valuable data. Write them down. Many breakthrough insights start as a mid-coding memo.\n\n## Ensuring Rigor: Interrater Reliability\n\nIf multiple researchers are coding the same dataset, you need to verify that codes are being applied consistently. This is called interrater reliability.\n\nThe standard process:\n1. Both researchers independently code the same 10-20% of the data\n2. Compare code assignments and calculate percentage agreement\n3. Aim for 80% or higher agreement before proceeding\n4. Where disagreement exists, discuss and refine codebook definitions\n\nHigh interrater reliability does not guarantee correct analysis — it guarantees consistent analysis, which is the foundation you need to build on.\n\n## How AI Changes Qualitative Coding\n\nManual coding is intellectually rewarding but brutally slow. For a 20-interview study with 60-minute sessions, thorough coding can take 40-80 hours of focused analyst time.\n\nPlatforms like Koji apply AI analysis to every interview automatically. As soon as a conversation ends, the AI extracts themes, identifies sentiment, flags key quotes, and maps patterns across all conversations — continuously updating as new responses come in. When your study ends, synthesis is essentially done.\n\nThis does not replace human analytical judgment. Researchers who use Koji still make the important decisions: which themes matter for the specific research question, what story the data is telling, what to recommend. But they spend their energy on interpretation and strategy, not transcription and counting.\n\nThe result: research cycles that used to take weeks now take days. Teams that used to study 10 participants now routinely run studies with 50 or 100, because the analysis overhead is fixed rather than proportional to sample size.\n\n## Presenting Coded Findings to Stakeholders\n\nAfter coding and theming, translate your analysis into a story that stakeholders can act on. Structure it as:\n\n1. **Lead with the answer**: State the top finding in one sentence (answer-first format)\n2. **Evidence**: Present 3-5 supporting themes, each with representative participant quotes\n3. **So what**: The specific business or product implication of each theme\n4. **Recommendations**: Concrete actions the team should take based on the findings\n\nAvoid data dumps. Stakeholders do not want to read your codebook — they want to know what to build next. Long reports get skimmed; short narratives backed by direct quotes get remembered and acted on.\n\n## Key Takeaways\n\n- **Qualitative coding** turns raw conversation data into structured, comparable categories that reveal patterns.\n- **Inductive coding** lets themes emerge from data; **deductive coding** tests predefined hypotheses — hybrid approaches work best for most research.\n- **A rigorous codebook** with clear definitions and examples is the foundation of defensible qualitative analysis.\n- **Themes** are higher-order patterns that group multiple codes into a coherent narrative — they are the real deliverable of qualitative analysis.\n- **AI tools like Koji** automate the mechanical parts of coding and synthesis, reducing analysis time from weeks to hours and enabling much larger sample sizes.\n\n---\n\n## Related Resources\n\n- [Thematic Analysis Guide](/docs/thematic-analysis-guide) — Systematic theme identification\n- [How to Analyze Qualitative Data](/docs/how-to-analyze-qualitative-data) — Complete analysis framework\n- [Affinity Mapping](/docs/affinity-mapping) — Organize data into themes\n- [Writing Insight Statements](/docs/writing-insight-statements) — Transform data into insights\n- [Presenting Research Findings](/docs/presenting-research-findings) — Share findings with stakeholders\n\n*Explore [structured questions](/docs/structured-questions-guide) for pre-coded qualitative data that's easier to analyze.*","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Coding Qualitative Data Guide — Koji Docs","metaDescription":"Step-by-step guide to qualitative coding: build a codebook, code transcripts, identify themes, and learn how AI tools like Koji automate synthesis.","keywords":["how to code qualitative data","qualitative coding guide","thematic coding","qualitative data analysis","codebook research","inductive coding","deductive coding","research synthesis"],"aiSummary":"This guide explains how to code qualitative data from research interviews — covering open coding, focused coding, codebook construction, and theme development. It walks through both manual and AI-assisted approaches, showing how platforms like Koji reduce qualitative analysis time from weeks to hours by automating synthesis while preserving the researcher's analytical judgment.","aiPrerequisites":["user-interview-guide","thematic-analysis-guide"],"aiLearningOutcomes":["Build a qualitative codebook with clear definitions","Apply inductive and deductive coding to interview transcripts","Identify themes from coded data","Assess interrater reliability for team research","Present coded findings to stakeholders effectively"],"aiDifficulty":"intermediate","aiEstimatedTime":"9 min read"},{"type":"documentation","id":"7ff6f5ff-0585-401c-9cb3-37816c0fd870","slug":"writing-insight-statements","title":"How to Write Research Insight Statements That Drive Action","url":"https://www.koji.so/docs/writing-insight-statements","summary":"Research insight statements transform raw interview observations into actionable product decisions. This guide explains the difference between observations and insights, four proven insight formats, and a step-by-step process from raw data to stakeholder-ready insight statements.","content":"\nA research insight statement is the single most important output of a qualitative study. Not the transcript. Not the theme count. The insight — a precise sentence that captures *why* users behave as they do and what that means for your product.\n\nWriting great insights is a skill most researchers underestimate. Most reports stop at observations (\"users found the checkout confusing\") when they should be writing insights (\"users abandon checkout when the form requests payment details before showing the total, because they fear being accidentally charged\"). That distinction is the difference between \"interesting\" and \"actionable.\"\n\nAccording to dscout, writing compelling insights is the single most critical skill a user researcher can develop — because insights are what get product teams excited about building things people will actually love.\n\n## Observations vs. Findings vs. Insights: The Hierarchy\n\nMost research reports present data at the wrong level of abstraction. Nielsen Norman Group defines a clear hierarchy:\n\n| Level | What It Is | Example |\n|-------|-----------|---------|\n| **Data** | Raw quotes and recorded behaviors | \"User clicked the back button 3 times during checkout\" |\n| **Finding** | A pattern observed across multiple participants | \"8 of 10 users navigated away from the payment screen before completing purchase\" |\n| **Insight** | The meaning behind the pattern — the why | \"Users who encounter payment form fields before seeing the total cost interpret the sequence as a commitment to pay and exit to avoid perceived risk, regardless of actual charge timing\" |\n\nMost research reports stop at findings. Exceptional research communicates insights. The difference is answering not just \"what happened\" but \"why it happened\" — and by implication, \"what to do about it.\"\n\n## What Makes a Strong Insight Statement\n\nStrong insight statements share four characteristics:\n\n**1. Grounded in evidence.** An insight is not a single quote. It should be supported by patterns observed across at least 3 participants, ideally from different segments.\n\n**2. Explains motivation.** An insight answers \"why,\" not just \"what.\" If your statement doesn't explain the underlying reason for a behavior, it is an observation.\n\n**3. Reveals tension.** The best insights describe the gap between what users need and what currently exists — a barrier, a mismatch, an unmet expectation.\n\n**4. Implies action.** A well-written insight makes the next step obvious without prescribing a specific solution. It enables decisions rather than dictating them.\n\nCompare these two:\n\n❌ **Weak (observation):** \"Users struggle with the pricing page.\"\n\n✅ **Strong (insight):** \"Users understand the base price but lose confidence during checkout when add-on fees appear without context — this uncertainty causes them to pause and seek validation from a colleague before proceeding, adding 3–4 days to the sales cycle.\"\n\nThe strong version explains *why* (unexpected fees trigger uncertainty), *what users do about it* (seek peer validation), and *why it matters to the business* (sales cycle impact).\n\n## Four Proven Insight Statement Formats\n\nDifferent research contexts call for different structures. Here are four formats that work consistently:\n\n### Format 1: The Tension Format (recommended for UX research)\n\n**\"[User type] is trying to [goal] but [barrier], because [root cause], which means [implication].\"**\n\n*Example:* \"New users are trying to complete their first project setup but abandon the wizard halfway, because the terminology assumes familiarity with our data model, which means their first experience creates confusion rather than confidence — and our activation rate suffers accordingly.\"\n\nThis is the most versatile format. It works for product research, UX audits, and conversion analysis.\n\n### Format 2: The Behavioral Pattern Format (for discovery research)\n\n**\"When [trigger or context], [users] tend to [behavior], which suggests [underlying belief or need].\"**\n\n*Example:* \"When evaluating a new SaaS tool, procurement managers tend to forward trial access to their IT team before approving purchase, which suggests that security and compliance review runs in parallel with product evaluation — not sequentially as most sales motions assume.\"\n\nUse this format when the behavioral pattern itself is the insight, and the implication is a strategic one.\n\n### Format 3: The Jobs-to-Be-Done Format (for strategic product insights)\n\n**\"[User] is trying to [make progress], but currently relies on [workaround] because [the core need] is not being met.\"**\n\n*Example:* \"Freelancers managing 5+ active clients are trying to monitor project health without context-switching, but maintain a separate spreadsheet alongside our tool because our dashboard does not surface the 'at-risk' status signal they actually care about.\"\n\nThis format is especially effective for identifying opportunities — the workaround reveals what the product should do.\n\n### Format 4: The Emotional Arc Format (for brand and experience research)\n\n**\"[User] feels [emotion] when [situation], which causes [behavior], even though [intended design] assumes [different emotional state].\"**\n\n*Example:* \"Patients feel vulnerable and rushed during physician rounds, which causes them to withhold questions and concerns, even though the hospital's care model assumes patients will actively advocate for themselves during these brief interactions.\"\n\nUse this format when emotional state is the central driver — common in healthcare, financial services, and consumer experience research.\n\n## Step-by-Step: From Raw Data to Insight Statement\n\n### Step 1: Collect your observations\nAfter [thematic analysis](/docs/thematic-analysis-guide) or [affinity mapping](/docs/affinity-mapping), you should have a set of patterns — behaviors or statements observed across multiple participants. Write these as plain observation statements.\n\n*\"Several users mentioned price as a concern when asked about upgrading.\"*\n\n### Step 2: Ask \"Why?\" three times\n\nThe \"5 Whys\" technique from lean manufacturing applies directly here. Each \"why\" peels back a layer of surface behavior to expose root cause.\n\n- Why did users mention price? → They compared it to a competitor.\n- Why did they compare to a competitor? → They Googled the tool while on the upgrade page.\n- Why were they Googling during the trial? → They didn't have enough evidence of value before hitting the paywall.\n\nNow you have an insight: the price objection is a *value communication problem*, not a pricing problem. The intervention is showing more value evidence before the upgrade prompt — not reducing price.\n\n### Step 3: Check your evidence breadth\n\nAn insight should be supported by at least 3 participants, ideally across different user segments or personas. A single powerful quote is a notable finding — worth flagging separately — but not enough to write an insight statement.\n\nIf only one person experienced something, note it as a signal and continue collecting data.\n\n### Step 4: Write the statement with the tension explicit\n\nUse one of the four formats above. The tension — the gap between what users need and what currently exists — must be explicit, not implied.\n\nCompare:\n- *Implied tension:* \"Users are frustrated by the export feature.\" (You can't act on this.)\n- *Explicit tension:* \"Power users rely on data exports for their weekly reporting workflow, but the 500-row limit forces them to run multiple exports and manually combine files — a 20-minute workaround that they describe as 'the most annoying thing about the product.'\" (Now you can act.)\n\n### Step 5: Add a \"This means...\" implication\n\nEvery insight should end with (or be followed by) a sentence beginning with \"This means...\" or \"Which suggests...\"\n\nThis converts the insight into a decision-enabling statement your stakeholders can use.\n\n*\"This means the export limit is a retention risk for our highest-value users, not just a UX inconvenience.\"*\n\n## Common Mistakes to Avoid\n\n**1. Writing observations as insights.** If your statement doesn't explain \"why,\" it is an observation. Keep asking \"so what?\" until you have a genuine explanation of user motivation.\n\n**2. Over-generalizing from one participant.** One powerful quote does not make an insight. Look for convergence across 3+ participants before writing a statement — or flag it as a signal, not a finding.\n\n**3. Prescribing solutions.** An insight identifies the problem. \"Users need a pricing comparison table on the upgrade page\" is a recommendation. \"Users lack sufficient evidence of value at the moment they are asked to upgrade\" is an insight. Keep them separate.\n\n**4. Burying insights in data.** A 40-slide deck of quotes and charts forces stakeholders to do the synthesis work you should have done. Insights live at the *top* of the deliverable — the executive summary — not at the end after 35 slides of data.\n\n**5. Writing insights only after all interviews are complete.** Capture emerging insights in real time as you notice patterns across sessions. Waiting until the final interview to start synthesis means relying on faded memory rather than fresh observation.\n\n## Real-World Example: Observation vs. Insight in Action\n\nA fintech startup ran 8 exit interviews with churned users. Their initial report contained six findings:\n\n- Users did not notice the savings feature\n- Users preferred keeping savings in their bank\n- Users did not trust the platform with larger amounts\n- Push notifications felt intrusive\n- The goal-setting interface was confusing\n- Users did not understand FDIC coverage\n\nThese are observations. After applying the insight framework, the team synthesized them into one insight statement:\n\n**Insight:** \"Users are willing to engage with the savings feature intellectually, but trust breaks down the moment they are asked to initiate a transfer — because they associate 'moving money' with irreversibility and loss of control. FDIC coverage information fails to address this fear because it appears after the transfer is initiated, not before, when the anxiety peaks.\"\n\nThis single insight reframed the problem from \"UI clarity\" to \"trust architecture at the moment of transfer\" — completely changing what the team built next.\n\n## How AI Accelerates the Path to Insights\n\nHistorically, writing insight statements required manually reviewing dozens of hours of transcripts, hand-coding themes through [qualitative coding](/docs/coding-qualitative-data), and synthesizing patterns across sessions. A 10-interview project could require 30–40 hours of analysis before a researcher was ready to write the first insight statement.\n\nAccording to a 2024 report from the ResearchOps Community, teams using AI-assisted analysis tools reported a 60% reduction in time from fieldwork completion to stakeholder readout.\n\nAI-native research platforms like [Koji](/) automatically surface recurring patterns across all interview transcripts, flag emotional intensity signals, and highlight contradictions across participant segments — compressing the synthesis layer so researchers can spend their time on the higher-order work: writing the actual insight statement.\n\nThe insight itself still requires a human. But everything that used to gate the process — transcript review, theme counting, quote surfacing — is now available in minutes.\n\n## Key Takeaways\n\n- An insight is not an observation — it must explain *why* users behave as they do and what it means for your product\n- Use the Tension Format: \"[User] is trying to [goal] but [barrier], because [root cause], which means [implication]\"\n- Always ground insights in evidence from at least 3 participants before writing the statement\n- Ask \"why?\" three times to move from surface behavior to root cause\n- Pair every insight with a \"This means...\" implication sentence to make it decision-ready\n- AI analysis tools can reduce time-to-insight by 60%+, freeing researchers to focus on synthesis over transcription\n  ","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Writing Research Insight Statements — Koji Guide","metaDescription":"Learn to write user research insight statements that drive product decisions. Four proven formats, step-by-step process, and before/after examples.","keywords":["research insight statements","how to write user research insights","observation vs insight UX","insight statement template","qualitative data synthesis","user research analysis","turning data into insights"],"aiSummary":"Research insight statements transform raw interview observations into actionable product decisions. This guide explains the difference between observations and insights, four proven insight formats, and a step-by-step process from raw data to stakeholder-ready insight statements.","aiPrerequisites":["thematic-analysis-guide","affinity-mapping"],"aiLearningOutcomes":["Distinguish between observations, findings, and insight statements","Apply four proven insight statement formats to qualitative research data","Use the 5 Whys technique to move from surface behavior to root cause","Write decision-ready insights that stakeholders can act on immediately"],"aiDifficulty":"intermediate","aiEstimatedTime":"12 min read"},{"type":"documentation","id":"7e6acbba-43be-4db7-8a25-d3f58bca53ba","slug":"research-repository-guide","title":"How to Build a UX Research Repository: The Complete Guide","url":"https://www.koji.so/docs/research-repository-guide","summary":"A research repository is a centralized, searchable system for storing and retrieving qualitative insights across studies. This guide covers taxonomy design, tool selection, intake rituals, and how AI-native platforms like Koji automate the repository-building process. Organizations with mature research repositories report 2.7x better business outcomes.","content":"A research repository is a centralized system for storing, organizing, and retrieving qualitative insights from past studies — so institutional knowledge doesn't live in scattered Notion pages, personal drives, or researchers' heads. When built well, a research repository transforms individual study findings into a compounding organizational asset.\n\nThe business case is clear: according to the User Interviews State of Research Operations 2025 report, organizations that embed research into their strategy report 2.7x better business outcomes, including 3.6x more active users and 2.8x increased revenue. But research that isn't findable might as well not exist.\n\n## What Is a Research Repository?\n\nA research repository — sometimes called an insights repository or research library — is a structured database of past research: studies, transcripts, themes, quotes, participant data, and synthesized insights, organized so anyone on the team can find relevant findings quickly.\n\nIt's different from a shared drive or a folder full of reports. A well-designed repository is:\n- **Searchable by topic, theme, or user segment** — not just by study name or date\n- **Cross-linked** — so a finding from last year's usability study surfaces when someone searches for a relevant topic today\n- **Living** — updated after every study, not just when someone has bandwidth\n- **Accessible** — open to PMs, designers, engineers, and leadership, not gated behind researcher access\n\nWithout a repository, teams repeatedly research questions that have already been answered, or make decisions that contradict findings they don't know exist.\n\n## Why Research Repositories Matter\n\n**The insight reuse problem**: Research findings have a long shelf life. A study on onboarding friction from 18 months ago may be directly relevant to a decision being made today — but only if someone can find it. Without a repository, insights decay in email threads and presentation decks that nobody revisits.\n\n**The scale problem**: As research volume grows, synthesis becomes impossible without infrastructure. A single researcher can keep track of 10 studies. At 50, you need a system. At 200, you need search and AI-assisted retrieval.\n\n**The democratization problem**: According to the State of Research Operations 2025, 35% of organizations have at least one dedicated Research Operations professional — but in most companies, insights are still locked in researcher-controlled systems. A good repository lets product managers and designers find relevant research without filing a research request.\n\n**The AI opportunity**: The same report found that 80% of research professionals now use AI in their research workflow — a 24-point increase from the prior year. AI-native repositories can automatically tag, cross-reference, and synthesize insights in ways that manual systems cannot.\n\n## What to Store in a Research Repository\n\n| Content Type | What to Include |\n|-------------|----------------|\n| Studies | Research plan, methodology, participant details, date |\n| Transcripts | Full session transcripts, timestamped |\n| Insights | Synthesized findings with supporting evidence |\n| Themes | Cross-study patterns with evidence from multiple sources |\n| Quotes | Tagged, searchable participant quotes |\n| Participant profiles | Anonymized participant data for cross-study analysis |\n| Reports | Final deliverables distributed to stakeholders |\n\nThe most valuable layer is **insights** — not raw transcripts. Transcripts are evidence; insights are the conclusions drawn from that evidence. Build your taxonomy and search around insights, not raw data.\n\n## How to Build a Research Repository: Step by Step\n\n### Step 1: Agree on a Taxonomy\n\nBefore touching any tooling, define how you'll categorize insights. Common taxonomic dimensions:\n- **Product area** (onboarding, checkout, notifications, settings)\n- **User segment** (enterprise, SMB, consumer; new vs. experienced users)\n- **Research type** (discovery, evaluative, generative)\n- **Theme** (mental models, friction points, motivations, workarounds)\n- **Sentiment** (positive, neutral, negative)\n\nResist the urge to build a perfect taxonomy upfront. Start with 4–6 dimensions and refine as content accumulates. Over-engineered taxonomies don't get maintained.\n\n### Step 2: Choose the Right Tool\n\nYou don't need dedicated software to start. Many teams begin with Notion or Airtable before migrating to purpose-built tools. The right choice depends on:\n- How many researchers are contributing\n- Whether stakeholders need direct self-serve access\n- Whether you need semantic search vs. tag-based search\n- Your budget\n\n**For small teams (fewer than 2 studies per month)**: Notion or Airtable with a consistent tagging convention.\n\n**For mid-size teams (2–8 studies per month)**: Purpose-built tools like Dovetail, Condens, Looppanel, or EnjoyHQ offer automatic tagging, transcript analysis, and insight synthesis.\n\n**For AI-native teams**: Platforms like Koji automatically generate themes and insights from every interview session — building the repository as research happens, rather than requiring manual post-study intake.\n\n### Step 3: Establish an Intake Ritual\n\nThe most common reason repositories fail is that they never get populated. Build intake into your research process, not as an afterthought. After every study, add:\n- The research brief and methodology\n- Deidentified transcripts or session notes\n- 3–5 top-level insights with supporting evidence (quotes, timestamps)\n- Tags across your taxonomy dimensions\n\nThis should take 30–60 minutes per study. If it takes longer, your intake process is too complex — simplify the taxonomy or the template.\n\n### Step 4: Make It Accessible to Non-Researchers\n\nThe repository delivers value only if people outside the research team actually use it. This requires:\n- **Simple, powerful search** — full-text search is the minimum; semantic search (finding conceptually related results even with different terminology) is much more powerful\n- **Insight summaries** — non-researchers don't have time to read full reports; 2–3 sentence summaries with link-to-detail are essential\n- **Proactive sharing** — send relevant insights to stakeholders when a known product decision is in progress\n- **Stakeholder onboarding** — a 15-minute tour of the repository pays dividends in adoption\n\n### Step 5: Audit and Maintain Quarterly\n\nRepositories decay without maintenance. Every quarter:\n- Archive studies older than 3 years (keep the insights, deprecate the raw data)\n- Review and merge duplicate themes\n- Identify insights invalidated by subsequent research and flag them\n- Survey stakeholders: \"Did you find what you needed in the last month?\"\n\n## Common Mistakes to Avoid\n\n1. **Building the taxonomy before you have data**: Start with a few studies, see what patterns emerge, then build your taxonomy around real content. Theoretical taxonomies rarely survive contact with actual research.\n\n2. **Storing raw data instead of insights**: A repository full of unanalyzed transcripts isn't useful — it just creates a larger pile to dig through. The synthesis work is what makes a repository valuable.\n\n3. **Making it researcher-only**: If only researchers can access and update the repository, it becomes a bottleneck rather than a resource. Give PMs and designers read access and contribution rights for their own synthesis work.\n\n4. **Optimizing for completeness over findability**: You don't need every study perfectly tagged — you need the most recent and most relevant studies to be instantly findable. Prioritize accordingly.\n\n5. **Neglecting cross-study synthesis**: Individual study insights are useful; cross-study themes are where the real leverage is. Schedule quarterly synthesis sessions to draw connections across multiple studies.\n\n## The Modern Research Repository: AI-Augmented Insights\n\nLegacy research repositories are passive storage systems — you put things in, and only get them out if you know what to search for. The next generation of research infrastructure is AI-native.\n\nModern AI-powered research platforms can:\n- Automatically tag transcripts with themes and sentiment\n- Surface relevant past insights when you start a new study\n- Generate cross-study synthesis reports on demand\n- Alert stakeholders when new findings touch topics they care about\n\nWhile traditional tools like Dovetail and Condens require manual tagging and structured intake, AI-native platforms like Koji take a different approach: every interview automatically generates themes, sentiment signals, and synthesized insights — creating a continuously updated knowledge base without manual overhead. As research volume scales, the repository grows more intelligent, not just larger.\n\n> \"The goal of an effective research operations program is to magnify the impact and value of UX research in an organization, giving researchers a seat at the table to ensure the voice of the user is at the center of every product release.\" — ResearchOps Community\n\nFor teams running 10+ interviews per month, the AI-native approach isn't just convenient — it's the only way to keep synthesis from becoming a bottleneck.\n\n## Real-World Example\n\nA mid-size SaaS company has been running research for two years. Their researchers have conducted 40+ studies, but findings live in Google Drive folders, Confluence pages, and individual Notion workspaces. When a PM asks \"what do we know about enterprise onboarding friction?\", the answer is \"give me a few days to dig through everything.\"\n\nThey build a research repository in Notion with a simple taxonomy: product area, user segment, and theme. They spend two weeks doing a retroactive intake of the 10 most important past studies. They then commit to 30-minute intake after every new study.\n\nWithin three months, PMs are self-serving research before coming to researchers. The research team spends more time on new studies and less time answering questions that have already been answered.\n\n## Key Takeaways\n\n- A research repository transforms individual findings into a compounding organizational asset that gets more valuable over time\n- The most valuable layer is synthesized insights, not raw transcripts — invest in synthesis before intake\n- Consistent 30–60 minute intake after every study is the key habit; skip it and the repository decays\n- Non-researcher accessibility is what creates ROI — a researcher-only repository is an underused repository\n- AI-native research platforms can automatically build the repository as research happens, eliminating manual intake entirely\n\n## Frequently Asked Questions\n\n**What is the difference between a research repository and a research report?**\nA research report is a deliverable from a single study — a document summarizing what was found. A research repository is infrastructure connecting findings across many studies over time. Think of reports as inputs to the repository.\n\n**What tool should I use for a research repository?**\nIt depends on your stage. Start with Notion or Airtable if running fewer than 2 studies per month. Graduate to purpose-built tools (Dovetail, Condens, Looppanel) when automatic tagging and search become necessary. For teams using AI-moderated interviews, platforms like Koji create the repository automatically as each study runs.\n\n**How do I get stakeholders to actually use the repository?**\nThree tactics work consistently: (1) share proactive insight alerts when relevant decisions are in progress, (2) give PMs and designers self-serve access so they can answer questions without filing a research request, and (3) create a monthly insights digest surfacing the most relevant recent findings.\n\n**How long does it take to build a research repository?**\nYou can have a working repository in a week using Notion. A robust, searchable repository with 50+ cross-linked studies takes 3–6 months to mature. The key discipline is consistent intake after every study — not a single large migration project.\n\n**Should I include external research — published reports, competitor analysis — in the repository?**\nYes. Many teams create a secondary research section. Store summaries and citations rather than full documents to avoid copyright issues. Flag all external research with its source and date so users understand the provenance.\n\n---\n\n## Related Resources\n\n- [How to Analyze Qualitative Data](/docs/how-to-analyze-qualitative-data) — Analysis methodology\n- [Presenting Research Findings](/docs/presenting-research-findings) — Share repository insights\n- [Scaling User Research](/docs/scaling-user-research) — Scale your research practice\n- [Continuous Discovery Guide](/docs/continuous-discovery-user-research) — Ongoing research habits\n- [AI-Generated Insights](/docs/ai-generated-insights) — Automated insight generation\n\n*Explore [structured questions](/docs/structured-questions-guide) for building searchable, structured research repositories.*","category":"Analysis & Synthesis","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"How to Build a UX Research Repository — Koji Research KB","metaDescription":"Learn how to build a research repository that teams actually use. Covers taxonomy, tool selection, intake rituals, and AI-native approaches.","keywords":["research repository","insights repository","ResearchOps","UX research repository","research knowledge base","how to build research repository","research operations"],"aiSummary":"A research repository is a centralized, searchable system for storing and retrieving qualitative insights across studies. This guide covers taxonomy design, tool selection, intake rituals, and how AI-native platforms like Koji automate the repository-building process. Organizations with mature research repositories report 2.7x better business outcomes.","aiPrerequisites":["thematic-analysis-guide","coding-qualitative-data"],"aiLearningOutcomes":["Design a taxonomy for organizing research insights","Choose the right repository tool for your team size and maturity","Establish a consistent intake ritual that keeps the repository current","Enable non-researcher stakeholders to self-serve past findings"],"aiDifficulty":"intermediate","aiEstimatedTime":"13 min read"},{"type":"documentation","id":"d7494458-b3b1-4768-a3fa-69798a1c25a9","slug":"api-authentication","title":"API Authentication","url":"https://www.koji.so/docs/api-authentication","summary":"Koji API authentication uses project-scoped API keys (pk_live_ prefix) passed as Bearer tokens. Keys carry four granular permissions (interview:start, interview:chat, interview:complete, interview:read) and have a default rate limit of 60 requests per minute. API access is available on all plans. Session tokens provide additional per-interview authentication.","content":"# API Authentication\n\nEvery request to the Koji API must include a valid API key passed as a Bearer token. Without proper authentication, the API returns a 401 Unauthorized response.\n\n---\n\n## How Authentication Works\n\nKoji uses API keys to authenticate requests to its REST API. Each key is scoped to a specific project and carries a set of permissions that control what operations it can perform. When you make a request, include your API key in the `Authorization` header:\n\n```\nAuthorization: Bearer your_api_key_here\n```\n\nThe API validates your key on every request, checks that the key has the required permissions for the endpoint you are calling, and verifies that the key has not been revoked. If any of these checks fail, you receive an error response with details about what went wrong.\n\n---\n\n## Creating an API Key\n\nAPI keys are created from the **Integrations** page within your project settings. Here is how to generate one:\n\n1. Open the project you want to integrate with.\n2. Navigate to **Settings > Integrations**.\n3. Click **Create API Key**.\n4. Give the key a descriptive name (for example, \"Production Backend\" or \"Staging Test Key\").\n5. Select the permissions the key needs (see the Permissions section below).\n6. Click **Generate**.\n7. Copy the key immediately. For security, Koji only displays the full key once at creation time.\n\nAPI keys use the `pk_live_` prefix followed by 32 cryptographically random characters. The key is stored as a SHA-256 hash, so it cannot be retrieved after creation.\n\nStore your API key securely. Treat it like a password. Never commit it to source control, embed it in client-side code, or share it in plain text. For more on key lifecycle management, see [Managing API Keys](/docs/managing-api-keys).\n\n---\n\n## Permissions\n\nEach API key can be granted one or more permissions. This follows the principle of least privilege — only grant the permissions your integration actually needs.\n\n| Permission | What It Allows |\n|---|---|\n| `interview:start` | Start new interviews via the API |\n| `interview:chat` | Send and receive messages during an interview |\n| `interview:complete` | Mark interviews as complete and trigger analysis |\n| `interview:read` | Retrieve interview data, transcripts, and analysis |\n\nBy default, new API keys are created with all four permissions. For a typical integration that starts interviews, exchanges messages, and then retrieves results, all four permissions are appropriate. For a read-only dashboard that simply displays completed interview data, `interview:read` alone is sufficient.\n\nYou can update permissions on an existing key at any time from the Integrations page without regenerating the key itself.\n\n---\n\n## Session Tokens\n\nSome endpoints use a secondary authentication mechanism called a **session token**. When you [start an interview via the API](/docs/starting-interviews-via-api), the response includes a `session_token`. This token is tied to that specific interview session and is required when calling certain endpoints like [sending messages](/docs/sending-messages-via-api) and the [complete endpoint](/docs/completing-interviews-via-api).\n\nSession tokens differ from API keys in important ways:\n\n- **Scope**: A session token is valid only for the single interview it was created for.\n- **Lifetime**: Session tokens expire when the interview is completed or after a period of inactivity.\n- **Header**: Pass the session token using the `X-Session-Token` header, not the `Authorization` header.\n\nA typical request using both authentication methods looks like this:\n\n```\nPOST https://koji.so/api/v1/interviews/:id/message\nAuthorization: Bearer your_api_key_here\nX-Session-Token: session_token_from_start_response\n```\n\n---\n\n## Plan Requirements\n\nAPI access is available on all Koji plans, including the free tier. The API is gated behind the `headless_api` entitlement, which is enabled for every plan. You can start building your integration immediately after creating a project.\n\n---\n\n## Rate Limiting\n\nEach API key has a default rate limit of 60 requests per minute. Every response includes rate limit headers so you can monitor your usage:\n\n| Header | Description |\n|---|---|\n| `X-RateLimit-Limit` | Maximum requests allowed in the current window |\n| `X-RateLimit-Remaining` | Requests remaining in the current window |\n| `X-RateLimit-Reset` | Unix timestamp when the current window resets |\n\nWhen you exceed the limit, the API returns a `429 Too Many Requests` response. For detailed guidance on handling rate limits, see [Rate Limits and CORS](/docs/rate-limits-and-cors).\n\n---\n\n## Revoking Keys\n\nIf you suspect a key has been compromised, revoke it immediately:\n\n1. Go to **Settings > Integrations** in your project.\n2. Find the key in the list.\n3. Click the **Revoke** button.\n4. Confirm the action.\n\nRevocation is instant. Any in-flight requests using that key will fail. There is no undo — if you need API access again, create a new key.\n\nYou can also deactivate a key temporarily by setting its `is_active` field to false, which lets you re-enable it later without regenerating. It is good practice to rotate your API keys periodically, even if you do not suspect compromise.\n\n---\n\n## Error Responses\n\nAuthentication errors return standard HTTP status codes:\n\n| Status Code | Meaning | Common Cause |\n|---|---|---|\n| 401 Unauthorized | Missing or invalid API key | Key not provided, malformed, or revoked |\n| 403 Forbidden | Key lacks required permission | Key does not have the permission needed for this endpoint |\n\nThe response body includes a JSON object with an `error` field and a human-readable `message` field to help you diagnose the issue.\n\n```json\n{\n  \"error\": \"unauthorized\",\n  \"message\": \"The provided API key is invalid or has been revoked.\"\n}\n```\n\n---\n\n## Security Best Practices\n\nFollowing these practices protects your integration and your respondents' data:\n\n- **Never expose keys client-side.** API keys belong on your server. If you need browser-based access, proxy requests through your backend.\n- **Use environment variables.** Store keys in environment variables or a secrets manager, not in your codebase.\n- **Scope permissions tightly.** Grant only the permissions each key actually needs.\n- **Rotate regularly.** Set a calendar reminder to rotate keys every 90 days.\n- **Monitor usage.** Review your API usage in the project dashboard to spot unusual patterns.\n- **Revoke unused keys.** If a key is no longer needed, revoke it. Orphaned keys are a security risk.\n\nFor more on securing your API integration, see [Rate Limits and CORS](/docs/rate-limits-and-cors).\n\n---\n\n## Next Steps\n\n- [Start your first interview via the API](/docs/starting-interviews-via-api)\n- [Understand rate limits and CORS configuration](/docs/rate-limits-and-cors)\n- [Learn about managing API keys](/docs/managing-api-keys)\n- [Explore the headless API overview](/docs/headless-api-overview)","category":"API Reference","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"API Authentication — Koji Docs","metaDescription":"Learn how to authenticate with the Koji API using Bearer tokens, manage API key permissions, and follow security best practices.","keywords":["api authentication","api key","bearer token","permissions","session token","koji api"],"aiSummary":"Koji API authentication uses project-scoped API keys (pk_live_ prefix) passed as Bearer tokens. Keys carry four granular permissions (interview:start, interview:chat, interview:complete, interview:read) and have a default rate limit of 60 requests per minute. API access is available on all plans. Session tokens provide additional per-interview authentication.","aiPrerequisites":["creating-your-account"],"aiLearningOutcomes":["Create and manage API keys","Understand the permissions model","Use session tokens for interview-scoped auth","Follow security best practices for key management"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"41938bf1-ba71-4644-b995-975f1a26a56e","slug":"starting-interviews-via-api","title":"Starting Interviews via API","url":"https://www.koji.so/docs/starting-interviews-via-api","summary":"The POST /api/v1/interviews/start endpoint launches interview sessions programmatically. It accepts a respondent object (with external_id, display_name, and metadata), mode (text/voice), and locale. The response returns interview_id, session_token, and initial_message. API access is available on all plans. Supports structured questions for quantitative data collection.","content":"# Starting Interviews via API\n\nThe `POST /api/v1/interviews/start` endpoint lets you programmatically launch an interview session from your own application. This is the entry point for any headless integration with Koji.\n\n---\n\n## Before You Begin\n\nMake sure you have:\n\n- An API key with the `interview:start` permission. See [API Authentication](/docs/api-authentication) for how to create one.\n- A project with a published research brief.\n- API access is available on all Koji plans, including the free tier.\n\n---\n\n## Endpoint\n\n```\nPOST https://koji.so/api/v1/interviews/start\n```\n\n### Headers\n\n| Header | Value | Required |\n|---|---|---|\n| `Authorization` | `Bearer your_api_key` | Yes |\n| `Content-Type` | `application/json` | Yes |\n\n### Request Body\n\nSend a JSON object with the following fields:\n\n| Field | Type | Required | Description |\n|---|---|---|---|\n| `respondent` | object | No | Respondent information (see below) |\n| `mode` | string | No | Interview mode: `text` or `voice`. Defaults to the project setting |\n| `locale` | string | No | Language/locale code (e.g., `en`, `es`, `fr`). Defaults to the project setting |\n\n#### Respondent Object\n\nThe `respondent` field accepts an object with these properties:\n\n| Field | Type | Required | Description |\n|---|---|---|---|\n| `external_id` | string | No | Your own identifier for this respondent, useful for linking back to your system |\n| `display_name` | string | No | Display name for the respondent |\n| `metadata` | object | No | Arbitrary key-value pairs attached to the respondent for your own tracking purposes |\n\nNote that metadata is nested under the `respondent` object, not at the top level of the request body.\n\n### Example Request\n\n```bash\ncurl -X POST https://koji.so/api/v1/interviews/start \\\n  -H \"Authorization: Bearer pk_live_your_key_here\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{'\n    \"respondent\": {\n      \"external_id\": \"user_12345\",\n      \"display_name\": \"Jane Doe\",\n      \"metadata\": {\n        \"segment\": \"power_users\",\n        \"source\": \"onboarding_flow\"\n      }\n    },\n    \"mode\": \"text\",\n    \"locale\": \"en\"\n  }'\n```\n\n---\n\n## Response\n\nA successful request returns a `201 Created` response with a JSON body:\n\n```json\n{\n  \"interview_id\": \"f8e7d6c5-b4a3-2109-8765-432109876543\",\n  \"session_token\": \"st_live_xxxxxxxxxxxxxxxxxxxx\",\n  \"initial_message\": \"Hi Jane! Thanks for taking the time to chat with us today. I'd love to learn about your experience...\"\n}\n```\n\n### Response Fields\n\n| Field | Type | Description |\n|---|---|---|\n| `interview_id` | string | Unique identifier for this interview. Use this for all subsequent API calls. |\n| `session_token` | string | Token required for session-scoped operations like sending messages and completing the interview. Pass it as `X-Session-Token` header. |\n| `initial_message` | string | The first message from the interviewer. Display this to the respondent. |\n\nStore the `interview_id` and `session_token` — you need both for subsequent operations.\n\n**Important:** The response field is `interview_id`, not `conversation_id`. Use `interview_id` consistently across all API calls.\n\n---\n\n## Structured Questions\n\nIf your research brief includes [structured questions](/docs/structured-questions-guide) (scale ratings, multiple choice, ranking, yes/no), the interview will present interactive widgets to the respondent during the conversation. When the interview completes and analysis runs, structured answers are returned alongside qualitative insights.\n\nEach structured answer in the analysis includes:\n\n- `questionId` and `questionText` — identifies which question was answered\n- `questionType` — the type of question (scale, single_choice, multiple_choice, ranking, yes_no)\n- `structuredValue` — the typed response (number for scale, string for single choice, string array for multiple choice or ranking, boolean for yes/no)\n- `qualitativeAnswer` — any additional context the respondent provided\n- `confidence` — how confident the analysis is in the extracted answer\n- `followUpInsights` — insights from follow-up probing\n\nThis enables programmatic aggregation of quantitative data alongside qualitative insights.\n\n---\n\n## Understanding the Response\n\n### interview_id\n\nThis is your primary reference for the interview. Use it to [send messages](/docs/sending-messages-via-api), [complete it](/docs/completing-interviews-via-api), and look it up in your project dashboard.\n\n### session_token\n\nThe session token acts as a secondary authentication layer scoped to this specific interview. It proves that the caller is the same entity that started the interview. You must include it when calling the [message endpoint](/docs/sending-messages-via-api) and the [complete endpoint](/docs/completing-interviews-via-api).\n\n### initial_message\n\nThis is the greeting generated for the respondent based on your research brief, project settings, and any respondent information you provided. Display this message in your interface as the start of the conversation.\n\n---\n\n## Handling Errors\n\n| Status Code | Error | Meaning |\n|---|---|---|\n| 400 | `invalid_request` | Missing required fields or invalid field values |\n| 401 | `unauthorized` | Invalid or missing API key |\n| 403 | `forbidden` | Key lacks `interview:start` permission |\n| 404 | `not_found` | The specified project does not exist |\n| 422 | `unprocessable` | The project has no published brief, or configuration prevents starting |\n| 429 | `rate_limited` | Too many requests. Check [Rate Limits and CORS](/docs/rate-limits-and-cors) |\n\nAll error responses include a JSON body with `error` and `message` fields:\n\n```json\n{\n  \"error\": \"invalid_request\",\n  \"message\": \"The project has no published research brief.\"\n}\n```\n\n---\n\n## Respondent Metadata\n\nThe `metadata` field inside the `respondent` object accepts any flat JSON object. Use it to attach your own tracking data to the respondent. Common use cases include:\n\n- **User segmentation**: `{\"plan\": \"enterprise\", \"tenure_months\": 24}`\n- **Source tracking**: `{\"source\": \"post_purchase_email\", \"campaign_id\": \"camp_123\"}`\n- **A/B testing**: `{\"variant\": \"B\", \"experiment\": \"onboarding_v2\"}`\n\nMetadata is returned when you retrieve the interview and is available in exports and webhooks. It does not affect the interview itself.\n\n---\n\n## Integration Patterns\n\n### Server-to-Server\n\nThe most common pattern is calling the start endpoint from your backend when a user triggers an action (for example, clicking a \"Give Feedback\" button). Your backend starts the interview, receives the response, and passes the `interview_id` and `initial_message` to your frontend.\n\n### Batch Invitations\n\nFor research projects, you might start interviews in batch — for example, iterating through a list of participants and starting an interview for each one. Store the returned `interview_id` and `session_token` for each participant so you can track and complete them later.\n\n### Embedded Experience\n\nCombine the API with the [embed widget](/docs/embed-widget-reference) for a hybrid approach: start the interview server-side for tracking purposes, then hand off the `interview_id` to the embed widget for the conversational UI.\n\n---\n\n## Next Steps\n\n- [Send and receive messages during the interview](/docs/sending-messages-via-api)\n- [Complete the interview and trigger analysis](/docs/completing-interviews-via-api)\n- [Learn about structured questions](/docs/structured-questions-guide)\n- [Explore the headless API overview](/docs/headless-api-overview)","category":"API Reference","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Starting Interviews via API — Koji Docs","metaDescription":"Learn how to start interviews programmatically using the Koji API POST /start endpoint, including request format, response handling, and integration patterns.","keywords":["start interview api","post start","headless interview","api integration","koji api"],"aiSummary":"The POST /api/v1/interviews/start endpoint launches interview sessions programmatically. It accepts a respondent object (with external_id, display_name, and metadata), mode (text/voice), and locale. The response returns interview_id, session_token, and initial_message. API access is available on all plans. Supports structured questions for quantitative data collection.","aiPrerequisites":["api-authentication"],"aiLearningOutcomes":["Start an interview via the API","Handle the start response correctly","Use metadata for tracking","Choose between text and voice modes"],"aiDifficulty":"intermediate","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"f04a883e-a2b2-480d-9aa0-c9f4b47009d7","slug":"sending-messages-via-api","title":"Sending Messages via API","url":"https://www.koji.so/docs/sending-messages-via-api","summary":"Messages are exchanged via POST /api/v1/interviews/:id/message (singular). Requires interview:chat permission and X-Session-Token header. Responses stream via SSE. Supports structured question widgets during interviews. Voice mode uses WebSocket connections with real-time audio streaming.","content":"# Sending Messages via API\n\nOnce you have [started an interview](/docs/starting-interviews-via-api), your application needs to handle the back-and-forth conversation between the respondent and Koji's interviewer. This article explains how messages flow in API-started interviews and how to integrate the conversational experience into your own UI.\n\n---\n\n## How Message Flow Works\n\nAn API-started interview follows a structured conversational pattern:\n\n1. **You start the interview** via `POST /api/v1/interviews/start`. The response includes an `initial_message` — this is the first thing the interviewer says to the respondent.\n2. **The respondent replies.** Your application collects the respondent's input (text or voice) and sends it to Koji.\n3. **Koji processes the response** and streams the next interviewer message back via Server-Sent Events (SSE), including follow-up questions that adapt to what the respondent said.\n4. **The cycle repeats** until the interview reaches a natural conclusion or you explicitly [complete it](/docs/completing-interviews-via-api).\n\nThis exchange happens through the interview session, with messages flowing through Koji's conversation engine.\n\n---\n\n## Text-Based Message Flow\n\nFor text-mode interviews, messages are exchanged through the message endpoint:\n\n```\nPOST https://koji.so/api/v1/interviews/:interview_id/message\n```\n\n**Important:** The endpoint path uses singular `/message`, not `/messages`.\n\n### Headers\n\n| Header | Value | Required |\n|---|---|---|\n| `Authorization` | `Bearer your_api_key` | Yes |\n| `X-Session-Token` | `session_token_from_start` | Yes |\n| `Content-Type` | `application/json` | Yes |\n\nThe API key must have the `interview:chat` permission to call this endpoint.\n\n### Request Body\n\n```json\n{\n  \"content\": \"The respondent's message text goes here.\"\n}\n```\n\n### Streaming Response (SSE)\n\nThe message endpoint streams the interviewer's response using Server-Sent Events. Your client should handle the stream as it arrives:\n\n```javascript\nconst response = await fetch(\n  `https://koji.so/api/v1/interviews/${interviewId}/message`,\n  {\n    method: 'POST',\n    headers: {\n      'Authorization': `Bearer ${apiKey}`,\n      'X-Session-Token': sessionToken,\n      'Content-Type': 'application/json'\n    },\n    body: JSON.stringify({ content: respondentMessage })\n  }\n);\n\nconst reader = response.body.getReader();\nconst decoder = new TextDecoder();\n\nwhile (true) {\n  const { done, value } = await reader.read();\n  if (done) break;\n  \n  const chunk = decoder.decode(value);\n  // Process SSE chunks — each contains a portion\n  // of the interviewer's response\n  handleStreamChunk(chunk);\n}\n```\n\n### Structured Response Handling\n\nWhen the interview includes [structured questions](/docs/structured-questions-guide), the SSE stream may include structured question events. These indicate that the interviewer is presenting a widget (such as a scale slider, multiple choice selector, or ranking interface) to the respondent. Your UI should render the appropriate input widget and send the structured response back.\n\n---\n\n## Voice-Based Message Flow\n\nVoice interviews use a different mechanism. When you start a voice interview, the response includes `voice_credentials` with a WebSocket URL and authentication token.\n\n### Establishing the Voice Connection\n\n```javascript\nconst ws = new WebSocket(voice_credentials.server_url);\n\nws.onopen = () => {\n  ws.send(JSON.stringify({\n    type: 'auth',\n    token: voice_credentials.token\n  }));\n};\n```\n\nOnce connected and authenticated, audio streams bidirectionally over the WebSocket. The voice service handles speech-to-text, processes the response through Koji's conversation engine, and streams synthesized speech back.\n\n### Voice Events\n\nThe WebSocket sends JSON events alongside audio data:\n\n| Event Type | Description |\n|---|---|\n| `transcript` | Real-time transcription of the respondent's speech |\n| `interviewer_start` | The interviewer has begun speaking |\n| `interviewer_end` | The interviewer has finished speaking |\n| `interview_status` | Status update (active, completing, completed) |\n| `error` | An error occurred in the voice session |\n\nHandle these events to update your UI — for example, showing a transcript as the respondent speaks or displaying a visual indicator when the interviewer is responding.\n\n---\n\n## Retrieving the Conversation\n\nAt any point during or after the interview, you can retrieve the full conversation:\n\n```\nGET https://koji.so/api/v1/interviews/:interview_id\n```\n\n### Headers\n\n| Header | Value | Required |\n|---|---|---|\n| `Authorization` | `Bearer your_api_key` | Yes |\n\nThe API key must have the `interview:read` permission. The response includes the full transcript, each message with its sender, timestamp, and content. After the interview is completed, it also includes analysis results, quality scores, and structured answers. See [Completing Interviews via API](/docs/completing-interviews-via-api) for more on the analysis payload.\n\n---\n\n## Building Your Own Chat UI\n\nWhen integrating the message flow into your application, here is a recommended approach:\n\n1. **Display the initial_message** from the start response as the first chat bubble.\n2. **Collect respondent input** via a text field or voice recording interface.\n3. **Send the message** to the `/message` endpoint.\n4. **Stream the response** — process SSE chunks to display the interviewer's reply progressively.\n5. **Handle structured questions** — if the stream includes a structured question event, render the appropriate widget.\n6. **Check interview status** — if it indicates the interview is winding down, prepare to call the [complete endpoint](/docs/completing-interviews-via-api).\n\nKeep the experience conversational. Avoid overwhelming respondents with too much UI chrome. The interview should feel like a natural conversation, not a form.\n\n---\n\n## Message Content Guidelines\n\nWhen sending respondent messages to the API:\n\n- **Send the raw text.** Do not pre-process, summarize, or modify what the respondent typed.\n- **Preserve formatting.** If the respondent uses line breaks, keep them.\n- **Do not inject instructions.** Sending hidden prompts or instructions alongside the respondent's message violates the terms of service and produces unreliable results.\n- **Handle empty messages.** Validate on your end that the message is not empty before sending.\n\n---\n\n## Conversation Length\n\nKoji's interview engine manages conversation length based on your research brief configuration. The interviewer naturally wraps up the conversation when it has covered the topics defined in the brief. You can also end the interview at any time by calling the [complete endpoint](/docs/completing-interviews-via-api).\n\nTypical interviews last 5 to 15 minutes for text and 8 to 20 minutes for voice, but this varies based on the research brief complexity and respondent engagement.\n\n---\n\n## Error Handling\n\n| Status Code | Error | Meaning |\n|---|---|---|\n| 400 | `invalid_message` | Message content is empty or malformed |\n| 401 | `unauthorized` | Invalid API key or session token |\n| 403 | `forbidden` | Key lacks `interview:chat` permission |\n| 404 | `not_found` | Interview does not exist |\n| 409 | `conflict` | Interview is already completed |\n| 429 | `rate_limited` | Too many messages in a short period |\n\nImplement retry logic with exponential backoff for transient errors like 429 and 5xx responses.\n\n---\n\n## Next Steps\n\n- [Start an interview to get your first session](/docs/starting-interviews-via-api)\n- [Complete the interview and retrieve analysis](/docs/completing-interviews-via-api)\n- [Learn about structured questions](/docs/structured-questions-guide)\n- [Explore the headless API overview](/docs/headless-api-overview)","category":"API Reference","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Sending Messages via API — Koji Docs","metaDescription":"Understand how messages flow between your app and Koji during API-started interviews, including text and voice modes.","keywords":["api messages","message flow","chat api","voice websocket","interview transcript"],"aiSummary":"Messages are exchanged via POST /api/v1/interviews/:id/message (singular). Requires interview:chat permission and X-Session-Token header. Responses stream via SSE. Supports structured question widgets during interviews. Voice mode uses WebSocket connections with real-time audio streaming.","aiPrerequisites":["starting-interviews-via-api"],"aiLearningOutcomes":["Send and receive messages through the API","Handle voice WebSocket connections","Retrieve transcripts during active interviews","Build a custom chat UI for interviews"],"aiDifficulty":"intermediate","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"326ce594-92b4-48ba-a543-8a9f7a506611","slug":"completing-interviews-via-api","title":"Completing Interviews via API","url":"https://www.koji.so/docs/completing-interviews-via-api","summary":"Complete interviews via POST /api/interview/complete to trigger automatic analysis. The analysis pipeline extracts themes, insights, quality scores, and structured answers (scale ratings, choices, rankings). Results are retrievable via GET /api/v1/interviews/:id once analysis finishes. Supports webhook notifications and polling.","content":"# Completing Interviews via API\n\nMarking an interview as complete triggers Koji's automatic analysis pipeline, which extracts themes, insights, structured answers, and quality scores from the conversation. This is the final step in the API interview lifecycle.\n\n---\n\n## When to Complete an Interview\n\nThere are several scenarios where you should complete an interview:\n\n- **The respondent finishes naturally.** The interview status changes to indicate the conversation has reached its conclusion. Your application should detect this and call complete.\n- **The respondent leaves early.** If the respondent closes their browser or navigates away, complete the interview so that whatever data was collected gets analyzed.\n- **You want to end it manually.** For testing or operational reasons, you might need to end an interview before the conversation naturally concludes.\n- **A timeout is reached.** If your application enforces a maximum interview duration, call complete when the timer expires.\n\nCompleting an interview is idempotent — calling it on an already-completed interview returns the same response without re-triggering analysis.\n\n---\n\n## Endpoint\n\n```\nPOST https://koji.so/api/interview/complete\n```\n\n### Headers\n\n| Header | Value | Required |\n|---|---|---|\n| `Authorization` | `Bearer your_api_key` | Yes |\n| `X-Session-Token` | `session_token_from_start` | Yes |\n| `Content-Type` | `application/json` | Yes |\n\nBoth the API key and the session token are required. The API key authenticates your application, and the session token verifies that you are the same entity that started this specific interview. See [API Authentication](/docs/api-authentication) for details on both.\n\n### Request Body\n\nThe request body should include the interview identifier:\n\n| Field | Type | Required | Description |\n|---|---|---|---|\n| `interview_id` | string | Yes | The interview ID returned from the start endpoint |\n| `reason` | string | No | Why the interview ended. Options: `natural`, `respondent_left`, `timeout`, `manual`. Defaults to `manual`. |\n\n### Example Request\n\n```bash\ncurl -X POST https://koji.so/api/interview/complete \\\n  -H \"Authorization: Bearer pk_live_your_key_here\" \\\n  -H \"X-Session-Token: st_live_xxxxxxxxxxxxxxxxxxxx\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{'\n    \"interview_id\": \"f8e7d6c5-b4a3-2109-8765-432109876543\",\n    \"reason\": \"natural\"\n  }'\n```\n\n---\n\n## Response\n\nA successful request returns `200 OK` with a summary of the completed interview:\n\n```json\n{\n  \"interview_id\": \"f8e7d6c5-b4a3-2109-8765-432109876543\",\n  \"status\": \"completed\",\n  \"completed_at\": \"2025-01-15T10:25:30Z\",\n  \"analysis_status\": \"processing\",\n  \"stats\": {\n    \"duration_seconds\": 1530,\n    \"respondent_messages\": 18,\n    \"interviewer_messages\": 19,\n    \"total_messages\": 37\n  }\n}\n```\n\n### Response Fields\n\n| Field | Type | Description |\n|---|---|---|\n| `interview_id` | string | The interview identifier |\n| `status` | string | Always `completed` on success |\n| `completed_at` | string (ISO 8601) | Timestamp when the interview was marked complete |\n| `analysis_status` | string | Status of the automatic analysis: `processing`, `completed`, or `failed` |\n| `stats` | object | Summary statistics for the interview |\n\n---\n\n## What Happens After Completion\n\nCompleting an interview triggers several automatic processes:\n\n1. **Transcript finalization.** The full conversation is finalized and stored.\n2. **Quality scoring.** Koji evaluates the interview quality based on the depth of responses, topic coverage, and respondent engagement.\n3. **Automatic analysis.** Koji's analysis engine processes the transcript, extracting themes, insights, and patterns.\n4. **Structured answer extraction.** If the interview used [structured questions](/docs/structured-questions-guide), the analysis extracts typed answers (scale ratings, choices, rankings, yes/no responses) alongside qualitative context.\n5. **Webhook delivery.** If you have [webhooks configured](/docs/webhook-setup), Koji sends an event notification when analysis completes.\n\nAnalysis typically takes a few seconds to a couple of minutes, depending on the interview length. Poll the interview endpoint or use webhooks to know when results are ready.\n\n---\n\n## Retrieving Results After Completion\n\nOnce `analysis_status` is `completed`, retrieve the full results:\n\n```\nGET https://koji.so/api/v1/interviews/:interview_id\n```\n\nThe response now includes the analysis payload alongside the transcript:\n\n```json\n{\n  \"interview_id\": \"f8e7d6c5-b4a3-2109-8765-432109876543\",\n  \"status\": \"completed\",\n  \"transcript\": [...],\n  \"analysis\": {\n    \"themes\": [...],\n    \"insights\": [...],\n    \"quality_score\": 4.2,\n    \"structured_answers\": [\n      {\n        \"questionId\": \"q_1\",\n        \"questionText\": \"How likely are you to recommend us?\",\n        \"questionType\": \"scale\",\n        \"structuredValue\": 8,\n        \"qualitativeAnswer\": \"Very likely, the product has been transformative.\",\n        \"confidence\": \"high\",\n        \"followUpInsights\": [\"Values the onboarding experience\"]\n      }\n    ]\n  },\n  \"stats\": {\n    \"duration_seconds\": 1530,\n    \"respondent_messages\": 18,\n    \"interviewer_messages\": 19\n  }\n}\n```\n\nThe `structured_answers` array is present when the interview used structured questions. Each entry contains the typed value alongside qualitative context, enabling programmatic aggregation of quantitative data.\n\n---\n\n## Polling for Analysis Completion\n\nIf you do not use webhooks, poll the interview endpoint to check when analysis finishes:\n\n```javascript\nasync function waitForAnalysis(interviewId, apiKey) {\n  const maxAttempts = 30;\n  const delayMs = 2000;\n\n  for (let i = 0; i < maxAttempts; i++) {\n    const response = await fetch(\n      `https://koji.so/api/v1/interviews/${interviewId}`,\n      { headers: { 'Authorization': `Bearer ${apiKey}` } }\n    );\n    const data = await response.json();\n\n    if (data.analysis_status === 'completed') {\n      return data;\n    }\n\n    if (data.analysis_status === 'failed') {\n      throw new Error('Analysis failed');\n    }\n\n    await new Promise(resolve => setTimeout(resolve, delayMs));\n  }\n\n  throw new Error('Analysis timed out');\n}\n```\n\nStart with a 2-second interval and increase it if analysis takes longer. Most interviews complete analysis within 30 seconds.\n\n---\n\n## Error Handling\n\n| Status Code | Error | Meaning |\n|---|---|---|\n| 401 | `unauthorized` | Invalid API key or session token |\n| 403 | `forbidden` | Key lacks `interview:complete` permission |\n| 404 | `not_found` | Interview does not exist |\n| 409 | `already_completed` | Interview was already completed (response still returns the interview data) |\n| 429 | `rate_limited` | Too many requests |\n\nThe 409 response is not an error in the traditional sense — it simply tells you the interview was already finished. The response body contains the same data as a successful completion.\n\n---\n\n## Session Token Requirement\n\nThe `X-Session-Token` header is mandatory for the complete endpoint. This prevents unauthorized parties who might have your API key from completing interviews they did not start.\n\nIf you have lost the session token, the interview can still be completed from the Koji dashboard by a team member with appropriate access.\n\n---\n\n## Next Steps\n\n- [Set up webhooks to be notified when analysis completes](/docs/webhook-setup)\n- [Review the authentication model](/docs/api-authentication)\n- [Start your integration with the start endpoint](/docs/starting-interviews-via-api)\n- [Learn about structured questions](/docs/structured-questions-guide)\n- [Explore the headless API overview](/docs/headless-api-overview)","category":"API Reference","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Completing Interviews via API — Koji Docs","metaDescription":"Mark interviews as complete and trigger automatic analysis using the Koji API POST /complete endpoint.","keywords":["complete interview","api complete","interview analysis","session token","post complete"],"aiSummary":"Complete interviews via POST /api/interview/complete to trigger automatic analysis. The analysis pipeline extracts themes, insights, quality scores, and structured answers (scale ratings, choices, rankings). Results are retrievable via GET /api/v1/interviews/:id once analysis finishes. Supports webhook notifications and polling.","aiPrerequisites":["starting-interviews-via-api","api-authentication"],"aiLearningOutcomes":["Complete interviews via the API","Understand the automatic analysis pipeline","Poll for or receive analysis results","Handle completion errors and edge cases"],"aiDifficulty":"intermediate","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"d795de05-22fb-4a0c-a666-eae3de9d075b","slug":"research-automation-webhooks","title":"Research Automation: How to Build Real-Time Research Pipelines with Webhooks","url":"https://www.koji.so/docs/research-automation-webhooks","summary":"Koji webhooks push interview and report data to any endpoint the moment an event occurs, enabling real-time Slack notifications, CRM sync, automated tagging, and fully automated research pipelines. Supported events: interview.completed, interview.failed_quality_gate, study.published, report.generated. Webhook payloads include participant attributes and structured question values. Configure in Settings > Webhooks. Use HMAC-SHA256 signature verification. Retries up to 3 times with exponential backoff.","content":"\n# Research Automation: How to Build Real-Time Research Pipelines with Webhooks\n\nA research pipeline is the infrastructure that moves data from your participants to the people who need to act on it — automatically. Traditional research workflows are manual: collect interviews, wait for analysis, write a report, share findings, repeat. This cycle takes days or weeks and produces insights that are stale by the time they reach decision-makers.\n\nKoji's webhook system enables a fundamentally different model: automated, real-time data flows that trigger actions the moment a participant completes an interview. Insights reach your team within seconds of being generated — not in the next sprint planning meeting.\n\n## How Webhooks Work\n\nA webhook is an HTTP POST request that Koji sends to a URL you specify when a specific event occurs. Unlike API polling — where you repeatedly check \"are there new interviews?\" — webhooks push data to you the instant something happens.\n\nThe basic flow:\n1. A participant completes a Koji interview\n2. Koji sends a POST request to your configured webhook URL with the event data\n3. Your system receives the data and triggers whatever action you have configured\n4. Everything happens automatically, typically within 2–5 seconds of completion\n\nYou configure webhooks in your workspace under **Settings → Webhooks**. Add your endpoint URL, select the events you want to subscribe to, and save.\n\n## Available Webhook Events\n\nKoji currently supports the following webhook events:\n\n**`interview.completed`**\nFired when a participant finishes an interview and it passes the [quality gate](/docs/how-the-quality-gate-works). Payload includes: interview ID, study ID, participant attributes (from personalized links), quality score, completion timestamp, and a direct link to view the transcript.\n\n**`interview.failed_quality_gate`**\nFired when an interview is completed but does not meet the minimum quality threshold. Useful for triggering follow-up outreach asking participants to try again, or for logging in your tracking system.\n\n**`study.published`**\nFired when you publish a study and make it live for participants. Useful for triggering team notifications or updating a research calendar in your project management tool.\n\n**`report.generated`**\nFired when a new report is generated or refreshed for a study. Payload includes key summary metrics and a link to the full report.\n\nEach event payload includes a `type` field, a `data` object with event-specific details, and a `timestamp` in ISO 8601 format.\n\n## Integration Patterns\n\n### Pattern 1: Real-Time Slack Notifications\n\nThe simplest webhook use case: notify your team when a new interview is completed.\n\nUsing a no-code tool like Zapier or Make, connect Koji's `interview.completed` event to a Slack channel. Your research team sees a notification like: \"New interview completed — Sarah (VP Engineering, Acme Corp, Enterprise plan). View transcript →\"\n\nFor time-sensitive studies — before a product launch, during a competitive analysis sprint, or when investigating a support escalation — real-time notifications mean you can act on an insight the same day it surfaces, not in the next planning cycle.\n\nSet up different Slack channels for different studies. Incoming signals from a churn research study route to your CS team's channel; signals from a feature validation study route to the product team.\n\n### Pattern 2: CRM Updates on Interview Completion\n\nFor sales, success, and marketing teams, interview completion can automatically update contact records.\n\n**Example: Post-trial interviews synced to Salesforce**\n\nWhen a trial user completes a Koji interview, a webhook triggers a Salesforce workflow that:\n- Marks the contact record as \"Research completed — date\"\n- Adds a note with the AI-generated interview summary\n- Assigns a follow-up task to the AE or CSM\n- Updates a custom satisfaction score field with the structured scale answer from the interview\n\nThis closes the loop between research and revenue-facing teams. The AE who calls a prospect to discuss renewal has context from a genuine qualitative conversation — not just CRM deal notes.\n\n**Example: Win/loss research synced to HubSpot**\n\nAfter a deal closes (won or lost), trigger a Koji interview via [personalized link](/docs/personalized-interview-links). When completed, the webhook updates the HubSpot deal record with competitive intelligence, pricing feedback, and decision factors extracted from the interview.\n\n### Pattern 3: Automated Research Tagging and Segmentation\n\nFor teams running ongoing research programs across multiple studies, webhooks automate participant tracking and segmentation.\n\nWhen an interview is completed:\n- Tag the participant in your CRM with the study topic and completion date\n- Update a custom attribute in your product analytics tool (Mixpanel, Amplitude, Heap) to mark users who have participated in research\n- Add the participant to a follow-up nurture sequence for future studies in the same theme area\n- Create a record in your research repository (Notion, Confluence, Airtable) with the interview metadata\n\nThis creates a complete research participation history for every user — automatically, without manual spreadsheet tracking.\n\n### Pattern 4: Automated Report Delivery\n\nWhen your Koji report is generated (`report.generated` event), automatically deliver it to stakeholders without any manual send:\n\n- **Slack**: Post a summary to a product or leadership channel with a link to the full report\n- **Email**: Trigger a personalized email via SendGrid or Mailchimp to each stakeholder with report highlights and the full link\n- **Notion or Confluence**: Create a new page in your research repository with the report content and metadata\n- **Linear or Jira**: Create action item tickets from the key recommendations surfaced in the report summary\n\nThis removes the \"send the report email\" step from your research workflow entirely — findings distribute themselves.\n\n### Pattern 5: End-to-End Automated Research Pipelines\n\nFor engineering teams, webhooks work in combination with Koji's [Headless API](/docs/headless-api-overview) to create fully automated research pipelines with no human intervention required:\n\n1. **Trigger event in your product**: User completes onboarding, trial expires, feature threshold reached, or NPS survey response received\n2. **Your backend calls Koji API**: Create a new interview session for that user, passing their context attributes (name, plan, feature usage)\n3. **User receives in-app or email prompt**: \"Take a 5-minute interview to help us improve — your feedback directly shapes the product\"\n4. **User completes interview**: Koji conducts the AI-moderated conversation\n5. **`interview.completed` webhook fires**: Your systems receive the structured data in real time\n6. **Downstream automation runs**: CRM updated, Slack notification sent, report refreshed, tickets created\n\nThis is research-as-infrastructure: qualitative insights flowing through your systems with the same reliability as any other data pipeline, 24 hours a day, without a researcher manually scheduling anything.\n\n## Setting Up Your First Webhook\n\n### Step 1: Create a Webhook Endpoint\n\n**Using a no-code tool:**\n- **Zapier**: Create a \"Webhooks by Zapier\" trigger and copy the generated URL\n- **Make**: Create an HTTP webhook module and copy the URL\n- **n8n**: Create an HTTP webhook trigger and copy the URL\n\n**Building a custom endpoint:**\n```javascript\n// Example: Express.js webhook handler\napp.post('/koji-webhook', express.raw({ type: 'application/json' }), (req, res) => {\n  const signature = req.headers['x-koji-signature'];\n  \n  if (!verifySignature(req.body, signature, process.env.KOJI_WEBHOOK_SECRET)) {\n    return res.status(401).send('Unauthorized');\n  }\n  \n  const event = JSON.parse(req.body);\n  \n  if (event.type === 'interview.completed') {\n    // Handle completed interview\n    console.log('Interview completed:', event.data.interview_id);\n  }\n  \n  res.status(200).send('OK');\n});\n```\n\nMake sure your endpoint returns a 200 status code within 10 seconds. Koji retries on timeout.\n\n### Step 2: Register the Webhook in Koji\n\n1. Navigate to **Settings → Webhooks** in your Koji workspace\n2. Click **Add Webhook**\n3. Enter your endpoint URL\n4. Select the events you want to subscribe to\n5. Save — Koji sends a test ping to verify the connection\n\n### Step 3: Understand the Payload Structure\n\nThe `interview.completed` payload:\n\n```json\n{\n  \"type\": \"interview.completed\",\n  \"timestamp\": \"2026-04-16T10:23:00Z\",\n  \"data\": {\n    \"interview_id\": \"abc123\",\n    \"study_id\": \"xyz789\",\n    \"study_title\": \"Post-Trial Feedback\",\n    \"participant\": {\n      \"name\": \"Sarah\",\n      \"company\": \"Acme Corp\",\n      \"email\": \"sarah@acme.com\",\n      \"plan\": \"enterprise\"\n    },\n    \"quality_score\": 4,\n    \"interaction_mode\": \"voice\",\n    \"completed_at\": \"2026-04-16T10:22:55Z\",\n    \"transcript_url\": \"https://app.koji.so/studies/xyz789/interviews/abc123\"\n  }\n}\n```\n\nWhen your study includes structured questions, the payload also includes `structured_answers` — an array of question IDs, types, and extracted values. This means a satisfaction scale rating or a single-choice response is available in your webhook payload in real time, without waiting for a full report refresh.\n\n### Step 4: Verify Webhook Signatures\n\nKoji signs all webhook payloads using HMAC-SHA256. The signature appears in the `X-Koji-Signature` header. Always verify this in your handler:\n\n```javascript\nconst crypto = require('crypto');\n\nfunction verifySignature(payload, signature, secret) {\n  const expected = crypto\n    .createHmac('sha256', secret)\n    .update(payload)\n    .digest('hex');\n  return crypto.timingSafeEqual(\n    Buffer.from('sha256=' + expected),\n    Buffer.from(signature)\n  );\n}\n```\n\nYour webhook secret is available in **Settings → Webhooks** after registering your endpoint.\n\n### Step 5: Handle Retries and Deduplication\n\nKoji retries failed webhook deliveries up to 3 times with exponential backoff (approximately 30 seconds, 5 minutes, and 30 minutes after the initial failure). To prevent duplicate processing:\n\n- Store processed `interview_id` values in your database\n- Before processing any webhook, check whether that ID has already been handled\n- Return 200 immediately even if you are processing asynchronously — this prevents Koji from treating slow processing as a failure\n\n## Structured Questions + Webhooks: Real-Time Quantitative Signals\n\nStudies using [structured questions](/docs/structured-questions-guide) produce richer webhook payloads. Koji supports six question types: open_ended, scale, single_choice, multiple_choice, ranking, and yes_no. Structured question values are included in the `interview.completed` payload as `structured_answers`.\n\nThis enables powerful routing logic:\n- **Low satisfaction score** (scale question, score 1–5): create a CS follow-up task immediately\n- **Negative expansion intent** (yes_no question, answer \"no\"): flag the account in Salesforce for at-risk review\n- **Specific churn reason selected** (single_choice question, option \"pricing\"): route to a pricing-focused win-back sequence\n\nQuantitative structured data flowing through webhooks in real time is one of the most powerful features of Koji's platform — transforming qualitative research into an operational data source your entire revenue stack can act on.\n\n## Monitoring and Debugging\n\nIn **Settings → Webhooks**, you can view delivery history for each registered endpoint:\n- Status of each delivery attempt (success or failure)\n- HTTP response code returned by your endpoint\n- Payload content for debugging\n- Option to manually retry failed deliveries\n\nIf you are developing locally, use a tool like ngrok to expose a local endpoint for testing before deploying to production.\n\n## Related Resources\n\n- [Structured Questions Guide](/docs/structured-questions-guide) — Add quantitative data to webhook payloads for real-time routing logic\n- [Webhook Setup](/docs/webhook-setup) — Quick-start configuration guide\n- [Headless API Overview](/docs/headless-api-overview) — Full API access for building automated research pipelines\n- [API Authentication](/docs/api-authentication) — Secure your API and webhook integrations\n- [Personalized Interview Links](/docs/personalized-interview-links) — Embed participant context that appears in webhook payloads\n- [Rate Limits and CORS](/docs/rate-limits-and-cors) — API constraints to plan around when building integrations\n","category":"API Reference","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Research Automation with Webhooks | Koji Docs","metaDescription":"Build real-time research pipelines with Koji webhooks. Trigger Slack alerts, CRM updates, and automated workflows the moment an interview is completed. Includes payload examples, signature verification, and end-to-end pipeline patterns.","keywords":["research automation webhooks","user research webhook","automated research pipeline","real-time research data","research webhook integration","continuous research automation","qualitative data webhook"],"aiSummary":"Koji webhooks push interview and report data to any endpoint the moment an event occurs, enabling real-time Slack notifications, CRM sync, automated tagging, and fully automated research pipelines. Supported events: interview.completed, interview.failed_quality_gate, study.published, report.generated. Webhook payloads include participant attributes and structured question values. Configure in Settings > Webhooks. Use HMAC-SHA256 signature verification. Retries up to 3 times with exponential backoff.","aiPrerequisites":["Familiarity with HTTP and REST APIs","Koji account on Interviews plan or higher for webhooks","A webhook endpoint (can use Zapier or Make for no-code setup)"],"aiLearningOutcomes":["Configure webhook endpoints in Koji settings","Handle interview.completed events with participant data","Build Slack notification workflows for real-time research alerts","Sync interview completions to CRM records automatically","Create end-to-end automated research pipelines with the Headless API","Verify webhook signatures for security"],"aiDifficulty":"intermediate","aiEstimatedTime":"15 minutes"},{"type":"documentation","id":"9b42f662-976d-40ec-b4b4-496f40cb2a29","slug":"webhook-setup","title":"Webhook Setup","url":"https://www.koji.so/docs/webhook-setup","summary":"Webhooks deliver real-time notifications when interviews complete and analysis is ready. Payloads include interview metadata, quality scores, and structured answer summaries. Supports signature verification via HMAC-SHA256 and automatic retry with exponential backoff.","content":"# Webhook Setup\n\nWebhooks let you receive real-time notifications from Koji when key events happen in your project — most importantly, when an interview completes and its analysis is ready. Instead of polling the API, you provide a URL and Koji sends the data to you.\n\n---\n\n## Why Use Webhooks\n\nPolling the API to check if an interview's analysis is done works, but it has drawbacks: unnecessary requests, delayed detection, and wasted resources. Webhooks solve all of these by pushing data to your server the moment it is available.\n\nCommon use cases for webhooks include:\n\n- **Syncing results to your database** as soon as analysis completes\n- **Triggering downstream workflows** like sending a thank-you email to respondents\n- **Updating dashboards** in real time as new interviews come in\n- **Alerting your team** when a high-priority interview finishes\n- **Aggregating structured answers** from scale, choice, and ranking questions across interviews\n\n---\n\n## Configuring a Webhook\n\nSet up webhooks from your project settings:\n\n1. Open your project in Koji.\n2. Navigate to **Settings > Integrations > Webhooks**.\n3. Click **Add Webhook Endpoint**.\n4. Enter your endpoint URL. This must be an HTTPS URL that accepts POST requests.\n5. Select the events you want to receive.\n6. Click **Save**.\n\nKoji sends a verification request to your endpoint when you save it. Your server must respond with a `200` status code to confirm it is ready to receive events.\n\n---\n\n## Supported Events\n\n| Event | Trigger | Description |\n|---|---|---|\n| `interview.completed` | Interview is marked complete | Fires when the complete endpoint is called or the interview ends naturally |\n| `interview.analysis_ready` | Analysis finishes processing | Fires when the automatic analysis pipeline finishes and results are available |\n| `interview.quality_scored` | Quality score is assigned | Fires when the quality gate has evaluated the interview |\n\nYou can subscribe to one or more events per webhook endpoint. Most integrations subscribe to `interview.analysis_ready` since that is when actionable data is available.\n\n---\n\n## Webhook Payload\n\nKoji sends a POST request to your endpoint with a JSON body:\n\n```json\n{\n  \"event\": \"interview.analysis_ready\",\n  \"timestamp\": \"2025-01-15T10:26:15Z\",\n  \"project_id\": \"a1b2c3d4-e5f6-7890-abcd-ef1234567890\",\n  \"data\": {\n    \"interview_id\": \"f8e7d6c5-b4a3-2109-8765-432109876543\",\n    \"status\": \"completed\",\n    \"analysis_status\": \"completed\",\n    \"quality_score\": 4.2,\n    \"respondent\": {\n      \"id\": \"r_abc123def456\",\n      \"display_name\": \"Jane Doe\",\n      \"external_id\": \"user_12345\"\n    },\n    \"stats\": {\n      \"duration_seconds\": 1530,\n      \"total_messages\": 37\n    },\n    \"metadata\": {\n      \"segment\": \"power_users\",\n      \"source\": \"onboarding_flow\"\n    },\n    \"structured_answers_summary\": {\n      \"has_structured_answers\": true,\n      \"question_count\": 3,\n      \"question_types\": [\"scale\", \"single_choice\", \"yes_no\"]\n    }\n  }\n}\n```\n\nThe payload includes enough information to identify the interview and decide if you need to fetch full results. The `structured_answers_summary` field indicates whether the interview collected structured data and how many questions were answered.\n\nTo retrieve the complete transcript, analysis, and full structured answers, call `GET https://koji.so/api/v1/interviews/:interview_id` using your API key.\n\n### Structured Answer Data\n\nWhen you fetch the full interview after receiving a webhook, the analysis includes a `structured_answers` array. Each structured answer contains:\n\n- `questionId` and `questionText` — identifies which question was answered\n- `questionType` — scale, single_choice, multiple_choice, ranking, or yes_no\n- `structuredValue` — the typed response (number for scale, string for single choice, string array for multiple choice or ranking, boolean for yes/no)\n- `qualitativeAnswer` — any additional context the respondent provided\n- `confidence` — how confident the analysis is in the extracted answer\n- `followUpInsights` — insights from follow-up probing\n\nThis enables programmatic aggregation of quantitative data alongside qualitative insights across all interviews in your project.\n\n---\n\n## Verifying Webhook Signatures\n\nEvery webhook request includes a signature header that lets you verify the request genuinely came from Koji:\n\n```\nX-Koji-Signature: sha256=abc123...\n```\n\nTo verify the signature:\n\n1. Retrieve the raw request body as a string (before any JSON parsing).\n2. Compute an HMAC-SHA256 of the body using your webhook secret as the key.\n3. Compare the computed hash with the value in the `X-Koji-Signature` header.\n\n```javascript\nconst crypto = require('crypto');\n\nfunction verifyWebhookSignature(rawBody, signature, secret) {\n  const expected = 'sha256=' + crypto\n    .createHmac('sha256', secret)\n    .update(rawBody)\n    .digest('hex');\n  return crypto.timingSafeEqual(\n    Buffer.from(signature),\n    Buffer.from(expected)\n  );\n}\n```\n\nYour webhook secret is displayed once when you create the webhook endpoint. Store it securely.\n\nAlways verify signatures before processing webhook data. Without verification, an attacker could send fake events to your endpoint.\n\n---\n\n## Responding to Webhooks\n\nYour endpoint must return a `2xx` status code within 10 seconds to acknowledge receipt. If Koji does not receive a successful response, it retries the delivery.\n\n### Retry Policy\n\n- **Immediate retry** after the first failure.\n- **Exponential backoff** for subsequent retries: 1 minute, 5 minutes, 30 minutes, 2 hours.\n- **Maximum retries**: 5 attempts over approximately 2.5 hours.\n- After all retries are exhausted, the event is marked as failed. You can see failed deliveries in the webhook logs on your Integrations page.\n\n### Best Practices for Handling Webhooks\n\n- **Return 200 immediately.** Do your heavy processing asynchronously after acknowledging receipt. If your processing takes more than 10 seconds, the webhook times out.\n- **Be idempotent.** Koji may send the same event more than once (for example, if your server returned 200 but the connection dropped before Koji received the response). Use the `interview_id` and event type to deduplicate.\n- **Log everything.** Store raw webhook payloads so you can debug issues later.\n- **Monitor failures.** Check the webhook logs in your Integrations page regularly.\n\n---\n\n## Testing Webhooks\n\nThe Integrations page includes a **Send Test Event** button for each webhook endpoint. This sends a sample payload to your URL so you can verify your handler works correctly before real interviews generate events.\n\nDuring development, tools like ngrok or similar tunneling services let you expose a local server to receive webhook deliveries.\n\n---\n\n## Troubleshooting\n\nIf your webhook is not receiving events:\n\n- **Check the endpoint URL** is correct and accessible from the internet.\n- **Verify HTTPS.** Koji only delivers webhooks to HTTPS URLs.\n- **Check your firewall** allows incoming requests from Koji's IP ranges.\n- **Review the delivery logs** in Settings > Integrations > Webhooks for error details.\n- **Confirm event subscription.** Make sure you are subscribed to the events you expect.\n\n---\n\n## Next Steps\n\n- [Complete interviews to trigger webhook events](/docs/completing-interviews-via-api)\n- [Set up your API key for fetching full results](/docs/api-authentication)\n- [Learn about structured questions](/docs/structured-questions-guide)\n- [Explore the headless API overview](/docs/headless-api-overview)","category":"API Reference","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Webhook Setup — Koji Docs","metaDescription":"Configure webhooks to receive real-time notifications when Koji interviews complete and analysis results are ready.","keywords":["webhooks","webhook setup","real-time notifications","interview events","webhook signature"],"aiSummary":"Webhooks deliver real-time notifications when interviews complete and analysis is ready. Payloads include interview metadata, quality scores, and structured answer summaries. Supports signature verification via HMAC-SHA256 and automatic retry with exponential backoff.","aiPrerequisites":["api-authentication","completing-interviews-via-api"],"aiLearningOutcomes":["Configure webhook endpoints","Verify webhook signatures","Handle retries and idempotency","Debug webhook delivery issues"],"aiDifficulty":"intermediate","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"658b5225-6556-47c2-bded-7c5659bea401","slug":"embed-widget-reference","title":"Embed Widget Reference","url":"https://www.koji.so/docs/embed-widget-reference","summary":"The Koji embed widget places interactive interviews inside websites via iframe. Served from koji.so/api/v1/embed/:projectId with dark theme by default and 12px border-radius. Supports external_id for respondent tracking, structured question widgets, PostMessage API for two-way communication, and voice mode with the microphone permission.","content":"# Embed Widget Reference\n\nThe Koji embed widget lets you place an interactive interview directly inside your website or application using an iframe. This reference covers every parameter, the PostMessage API for two-way communication, and styling options.\n\n---\n\n## Basic Embed Code\n\nThe simplest embed uses a single iframe tag:\n\n```html\n<iframe\n  src=\"https://koji.so/api/v1/embed/:project_id\"\n  width=\"400\"\n  height=\"600\"\n  frameborder=\"0\"\n  allow=\"microphone\"\n></iframe>\n```\n\nReplace `:project_id` with your actual project ID. The `allow=\"microphone\"` attribute is required if your interview uses voice mode.\n\n---\n\n## iframe URL Parameters\n\nAppend these as query parameters to the embed URL:\n\n| Parameter | Type | Default | Description |\n|---|---|---|---|\n| `theme` | string | `dark` | Visual theme: `light` or `dark` |\n| `accent_color` | string | Project default | Hex color code without the hash (e.g., `4F46E5`) |\n| `hide_header` | boolean | `false` | Hides the widget header bar |\n| `hide_branding` | boolean | `false` | Removes Koji branding from the widget (requires Growth+ plan) |\n| `respondent_name` | string | — | Pre-fills the respondent name |\n| `respondent_email` | string | — | Pre-fills the respondent email |\n| `external_id` | string | — | Your own identifier for the respondent, useful for linking back to your system |\n| `language` | string | Project default | Language code (e.g., `en`, `es`, `fr`) |\n| `mode` | string | Project default | Interview mode: `text` or `voice` |\n| `metadata` | string | — | URL-encoded JSON object of custom metadata |\n| `auto_start` | boolean | `false` | Automatically starts the interview when the widget loads |\n\n### Using external_id\n\nThe `external_id` parameter lets you pre-fill a respondent identifier from your own system. This is useful for tracking which of your users completed the interview without requiring them to enter information manually. It is passed through to webhooks, exports, and the API responses.\n\n```html\n<iframe\n  src=\"https://koji.so/api/v1/embed/a1b2c3d4?external_id=user_12345\"\n  width=\"400\"\n  height=\"600\"\n  frameborder=\"0\"\n></iframe>\n```\n\n### Example with Parameters\n\n```html\n<iframe\n  src=\"https://koji.so/api/v1/embed/a1b2c3d4?theme=dark&accent_color=4F46E5&respondent_name=Jane&auto_start=true\"\n  width=\"400\"\n  height=\"600\"\n  frameborder=\"0\"\n  allow=\"microphone\"\n></iframe>\n```\n\n---\n\n## Structured Question Widgets\n\nWhen your research brief includes [structured questions](/docs/structured-questions-guide), the embed widget automatically renders interactive input widgets during the interview. These include:\n\n- **Scale widgets** — sliders or number selectors for rating questions\n- **Choice widgets** — radio buttons for single choice, checkboxes for multiple choice\n- **Ranking widgets** — drag-and-drop lists for ranking questions\n- **Yes/No widgets** — toggle buttons for binary questions\n\nThe widgets are styled to match your theme and accent color settings. Responses from these widgets are captured as structured data, enabling quantitative analysis alongside the qualitative conversation.\n\n---\n\n## PostMessage API\n\nThe embed widget communicates with your host page via the browser's `postMessage` API. This enables two-way interaction: you can send commands to the widget, and the widget sends events back to you.\n\n### Sending Commands to the Widget\n\nUse `postMessage` on the iframe's content window:\n\n```javascript\nconst iframe = document.getElementById('koji-embed');\n\n// Start the interview programmatically\niframe.contentWindow.postMessage({\n  type: 'koji:start',\n  payload: {\n    respondent_name: 'Jane Doe',\n    metadata: { source: 'homepage' }\n  }\n}, 'https://koji.so');\n```\n\n### Available Commands\n\n| Command Type | Payload | Description |\n|---|---|---|\n| `koji:start` | `{ respondent_name?, respondent_email?, metadata? }` | Starts the interview with optional respondent info |\n| `koji:complete` | `{}` | Ends the interview and triggers analysis |\n| `koji:set_theme` | `{ theme: 'light' \\| 'dark' }` | Changes the theme dynamically |\n| `koji:set_accent` | `{ color: '#4F46E5' }` | Changes the accent color dynamically |\n| `koji:reset` | `{}` | Resets the widget to its initial state |\n\n### Receiving Events from the Widget\n\nListen for messages from the iframe:\n\n```javascript\nwindow.addEventListener('message', (event) => {\n  if (event.origin !== 'https://koji.so') return;\n\n  const { type, payload } = event.data;\n\n  switch (type) {\n    case 'koji:ready':\n      console.log('Widget is loaded and ready');\n      break;\n    case 'koji:interview_started':\n      console.log('Interview started:', payload.interview_id);\n      break;\n    case 'koji:interview_completed':\n      console.log('Interview completed:', payload.interview_id);\n      break;\n    case 'koji:error':\n      console.error('Widget error:', payload.message);\n      break;\n  }\n});\n```\n\n### Available Events\n\n| Event Type | Payload | When It Fires |\n|---|---|---|\n| `koji:ready` | `{}` | Widget has loaded and is ready to accept commands |\n| `koji:interview_started` | `{ interview_id }` | An interview session has begun |\n| `koji:message_sent` | `{ message_count }` | The respondent sent a message |\n| `koji:interview_completed` | `{ interview_id, stats }` | The interview has ended |\n| `koji:error` | `{ code, message }` | An error occurred in the widget |\n| `koji:resize` | `{ width, height }` | The widget's ideal size changed (useful for responsive containers) |\n\nAlways check `event.origin` before processing messages to ensure they come from `https://koji.so`.\n\n---\n\n## Responsive Sizing\n\nThe embed widget adapts to the dimensions you give it, but works best within certain ranges:\n\n| Dimension | Minimum | Recommended | Maximum |\n|---|---|---|---|\n| Width | 320px | 400px | 100% of container |\n| Height | 500px | 600px | 100% of viewport |\n\nFor a responsive layout, use CSS to make the iframe fluid:\n\n```html\n<div style=\"width: 100%; max-width: 480px; height: 70vh; min-height: 500px;\">\n  <iframe\n    src=\"https://koji.so/api/v1/embed/:project_id\"\n    style=\"width: 100%; height: 100%; border: none; border-radius: 12px;\"\n    allow=\"microphone\"\n  ></iframe>\n</div>\n```\n\nListen for `koji:resize` events to dynamically adjust the container size based on the widget's content.\n\n---\n\n## Styling and Branding\n\nThe widget defaults to a dark theme. Use `?theme=light` in the URL to switch to light mode. The default border-radius is 12px.\n\nThe widget respects your [branding settings](/docs/customizing-branding) configured in the project dashboard. Beyond that, the URL parameters let you override theme and accent color per embed instance.\n\nThe iframe itself can be styled with CSS on the host page:\n\n```css\n#koji-embed {\n  border: none;\n  border-radius: 12px;\n  box-shadow: 0 4px 24px rgba(0, 0, 0, 0.1);\n}\n```\n\nHiding Koji branding with `hide_branding=true` requires a Growth plan or higher.\n\n---\n\n## Security Considerations\n\n- **Always validate `event.origin`** when listening for PostMessage events. Only accept messages from `https://koji.so`.\n- **Do not pass sensitive data** through URL parameters. Use the PostMessage API for any information you do not want visible in the URL.\n- **The `allow` attribute** should only include `microphone` if your interview uses voice mode. Do not grant unnecessary permissions.\n\n---\n\n## Browser Support\n\nThe embed widget works in all modern browsers. Voice features perform best in Chrome and Edge. See [Browser Compatibility](/docs/browser-compatibility) for the full support matrix.\n\n---\n\n## Next Steps\n\n- [Get started with the embed widget](/docs/using-the-embed-widget)\n- [Customize your project branding](/docs/customizing-branding)\n- [Learn about structured questions](/docs/structured-questions-guide)\n- [Explore the headless API overview](/docs/headless-api-overview)","category":"API Reference","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Embed Widget Reference — Koji Docs","metaDescription":"Technical reference for the Koji embed widget covering iframe parameters, PostMessage API, styling, and responsive design.","keywords":["embed widget","iframe","postmessage api","embed parameters","widget integration"],"aiSummary":"The Koji embed widget places interactive interviews inside websites via iframe. Served from koji.so/api/v1/embed/:projectId with dark theme by default and 12px border-radius. Supports external_id for respondent tracking, structured question widgets, PostMessage API for two-way communication, and voice mode with the microphone permission.","aiPrerequisites":["using-the-embed-widget"],"aiLearningOutcomes":["Configure iframe parameters","Use the PostMessage API for commands and events","Implement responsive widget sizing","Apply branding and styling"],"aiDifficulty":"intermediate","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"5ef076ae-c4ed-405f-aaef-438b5fa9d76c","slug":"rate-limits-and-cors","title":"Rate Limits and CORS","url":"https://www.koji.so/docs/rate-limits-and-cors","summary":"Koji API rate limiting uses a 1-minute sliding window with a default of 60 requests per minute per API key. Response headers X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset enable proactive rate management. CORS configuration controls browser-based access. API access is available on all plans.","content":"# Rate Limits and CORS\n\nKoji's API uses rate limiting to ensure fair usage and platform stability, and CORS validation to control which domains can make browser-based requests. This article explains how both systems work and how to handle them in your integration.\n\n---\n\n## Rate Limiting\n\nRate limits protect the API from abuse and ensure consistent performance for all users. Every API request you make counts toward your rate limit allocation.\n\n### How Rate Limits Work\n\nRate limits are applied per API key using a 1-minute sliding window. Each key has a default allowance of **60 requests per minute**. When you exceed the limit, the API returns a `429 Too Many Requests` response until the window resets.\n\nAPI access and rate limiting are available on all Koji plans, including the free tier.\n\n### Rate Limit Headers\n\nEvery API response includes headers that tell you your current rate limit status:\n\n| Header | Description |\n|---|---|\n| `X-RateLimit-Limit` | The maximum number of requests allowed in the current window |\n| `X-RateLimit-Remaining` | How many requests you have left in the current window |\n| `X-RateLimit-Reset` | Unix timestamp when the current window resets |\n\nUse these headers to implement intelligent rate limit handling in your application.\n\n### Example Response Headers\n\n```\nHTTP/1.1 200 OK\nX-RateLimit-Limit: 60\nX-RateLimit-Remaining: 47\nX-RateLimit-Reset: 1705312860\n```\n\nIn this example, you have 47 requests remaining out of 60 for this window. The window resets at the Unix timestamp 1705312860.\n\n### Handling 429 Responses\n\nWhen you hit the rate limit, the API returns:\n\n```\nHTTP/1.1 429 Too Many Requests\nX-RateLimit-Limit: 60\nX-RateLimit-Remaining: 0\nX-RateLimit-Reset: 1705312860\n```\n\n```json\n{\n  \"error\": \"rate_limited\",\n  \"message\": \"Rate limit exceeded. Please retry after the window resets.\"\n}\n```\n\nUse the `X-RateLimit-Reset` header to determine when you can resume making requests. Calculate the wait time by subtracting the current Unix timestamp from the reset value.\n\n### Implementing Backoff\n\nThe recommended approach is exponential backoff with jitter:\n\n```javascript\nasync function apiRequestWithRetry(url, options, maxRetries = 3) {\n  for (let attempt = 0; attempt <= maxRetries; attempt++) {\n    const response = await fetch(url, options);\n\n    if (response.status !== 429) {\n      return response;\n    }\n\n    if (attempt === maxRetries) {\n      throw new Error('Rate limit exceeded after max retries');\n    }\n\n    const resetAt = parseInt(\n      response.headers.get('X-RateLimit-Reset') || '0',\n      10\n    );\n    const now = Math.floor(Date.now() / 1000);\n    const waitSeconds = Math.max(resetAt - now, 1);\n    const jitter = Math.random() * 1000;\n    const delay = (waitSeconds * 1000) + jitter;\n\n    await new Promise(resolve => setTimeout(resolve, delay));\n  }\n}\n```\n\nThe jitter prevents the \"thundering herd\" problem where multiple clients retry simultaneously after a rate limit window resets.\n\n### Proactive Rate Limit Management\n\nRather than waiting for 429 errors, monitor the `X-RateLimit-Remaining` header and slow down as you approach the limit:\n\n```javascript\nasync function managedApiRequest(url, options) {\n  const response = await fetch(url, options);\n  const remaining = parseInt(\n    response.headers.get('X-RateLimit-Remaining') || '60',\n    10\n  );\n\n  if (remaining < 10) {\n    // Getting close to the limit - add a delay\n    await new Promise(resolve => setTimeout(resolve, 1000));\n  }\n\n  return response;\n}\n```\n\n---\n\n## CORS (Cross-Origin Resource Sharing)\n\nCORS controls which domains are allowed to make browser-based requests to the Koji API. This is relevant if you are making API calls directly from client-side JavaScript (which we generally discourage for security reasons).\n\n### How CORS Works in Koji\n\nKoji validates the `Origin` header on incoming requests against a list of allowed origins configured in your project settings. If the origin is not in the allowed list, the browser blocks the response.\n\n### Configuring Allowed Origins\n\n1. Go to your project's **Settings > Integrations** page.\n2. Find the **CORS Origins** section.\n3. Add each domain that needs to make browser-based requests.\n4. Include the full origin with protocol (e.g., `https://yourapp.com`).\n5. Click **Save**.\n\n### CORS Configuration Guidelines\n\n- **Be specific.** List exact origins rather than using wildcards.\n- **Include all environments.** If you have staging and production domains, add both.\n- **Protocol matters.** `https://yourapp.com` and `http://yourapp.com` are different origins.\n- **No trailing slashes.** Use `https://yourapp.com`, not `https://yourapp.com/`.\n- **Subdomains are separate.** `https://api.yourapp.com` and `https://yourapp.com` are different origins.\n\n### Localhost for Development\n\nDuring development, you can add `http://localhost:3000` (or whatever port your dev server uses) to the allowed origins. Remove localhost origins before going to production.\n\n### When CORS Does Not Apply\n\nCORS only affects browser-based requests. Server-to-server API calls (from your backend) are not subject to CORS restrictions. This is another reason to make API calls from your backend rather than your frontend.\n\n---\n\n## Server-Side vs. Client-Side Requests\n\nWe strongly recommend making all API calls from your server rather than from client-side JavaScript:\n\n| Concern | Client-Side | Server-Side |\n|---|---|---|\n| API key exposure | Key visible in browser | Key stays on your server |\n| CORS configuration | Required | Not needed |\n| Rate limit control | Hard to manage | Easy to manage |\n| Security | Less secure | More secure |\n\nIf you must make client-side requests, use the [embed widget](/docs/embed-widget-reference) instead — it handles authentication and CORS internally.\n\n---\n\n## Preflight Requests\n\nBrowsers send an OPTIONS preflight request before making certain cross-origin requests. Koji handles preflight requests automatically based on your CORS configuration. Preflight requests do not count toward your rate limit.\n\n---\n\n## Troubleshooting\n\n### \"CORS policy: No 'Access-Control-Allow-Origin' header\"\n\nThis means your domain is not in the allowed origins list. Add it in Settings > Integrations > CORS Origins.\n\n### \"429 Too Many Requests\"\n\nYou have exceeded your rate limit of 60 requests per minute. Implement backoff logic and use the `X-RateLimit-Reset` header to determine when the window resets.\n\n### Preflight requests failing\n\nEnsure your allowed origin exactly matches the Origin header the browser sends. Check protocol, domain, and port.\n\n---\n\n## Next Steps\n\n- [Set up API authentication](/docs/api-authentication)\n- [Start making API calls](/docs/starting-interviews-via-api)\n- [Explore the headless API overview](/docs/headless-api-overview)","category":"API Reference","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Rate Limits and CORS — Koji Docs","metaDescription":"Understand Koji API rate limits, response headers, backoff strategies, and CORS origin configuration.","keywords":["rate limiting","cors","api limits","rate limit headers","cross origin"],"aiSummary":"Koji API rate limiting uses a 1-minute sliding window with a default of 60 requests per minute per API key. Response headers X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset enable proactive rate management. CORS configuration controls browser-based access. API access is available on all plans.","aiPrerequisites":["api-authentication"],"aiLearningOutcomes":["Read and use rate limit headers","Implement exponential backoff with jitter","Configure CORS origins","Choose between client-side and server-side API calls"],"aiDifficulty":"intermediate","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"7b2f1595-e250-47f2-8754-69d2e68a51a9","slug":"plan-comparison-guide","title":"Plan Comparison Guide","url":"https://www.koji.so/docs/plan-comparison-guide","summary":"Koji offers four credit-based pricing plans: Free (10 starter credits), Insights (€29/mo, 29 credits), Interviews (€79/mo, 79 credits), and Enterprise (custom, 500+). All features — voice, text, API, structured questions, branding, exports — are available on every plan. Text interviews cost 1 credit, voice costs 3, report refreshes cost 5 (free on Interviews+). The quality gate ensures conversations scoring below 3 on the 1-5 scale are free. All plans include unlimited studies.","content":"## Koji Pricing Plans Overview\n\nKoji uses a **credit-based pricing model** designed so you only pay for quality conversations. Every plan includes the quality gate — [interviews scoring below 3 on the 1-5 quality scale](/docs/how-the-quality-gate-works) are free and don't consume credits.\n\nAll prices are in **EUR (€)**. Annual plans save you **2 months free**.\n\n---\n\n## Plan Comparison\n\n### Free Plan — €0/month\nPerfect for trying Koji or running occasional research.\n\n- **Credits:** 10 starter credits (one-time grant on signup)\n- **Active studies:** Unlimited\n- **Text interviews:** ✅ (1 credit each) — [learn more](/docs/text-interview-experience)\n- **Voice interviews:** ✅ (3 credits each) — [learn more](/docs/voice-interview-experience)\n- **Structured questions:** ✅ (scale, single choice, multiple choice, ranking, yes/no, open-ended)\n- **Multi-language support:** ✅ (30+ languages)\n- **Branded intake forms:** ✅\n- **Custom interview URLs:** ✅\n- **Lead collection form:** ✅\n- **Custom badges:** ✅\n- **Footer customization:** ✅\n- **Custom link previews:** ✅\n- **Remove \"Powered by\" badge:** ✅\n- **Website embed:** ✅ (iframe + advanced)\n- **Report generation:** 5 credits per refresh\n- **Quality gate:** ✅ Included\n- **API access:** ✅ Full API, webhooks, headless mode\n- **CRM import:** ✅\n- **CSV export:** ✅\n- **JSON export:** ✅\n- **Overage billing:** Not available\n\n### Insights Plan — €29/month (€290/year)\nFor teams running regular research at scale with monthly credit renewal.\n\n- **Credits:** 29 credits/month included\n- **Active studies:** Unlimited\n- **Text interviews:** ✅ (1 credit each) — [learn more](/docs/text-interview-experience)\n- **Voice interviews:** ✅ (3 credits each) — [learn more](/docs/voice-interview-experience)\n- **Structured questions:** ✅ (scale, single choice, multiple choice, ranking, yes/no, open-ended)\n- **Multi-language support:** ✅ (30+ languages)\n- **Branded intake forms:** ✅\n- **Custom interview URLs:** ✅\n- **Lead collection form:** ✅\n- **Custom badges:** ✅\n- **Footer customization:** ✅\n- **Custom link previews:** ✅\n- **Remove \"Powered by\" badge:** ✅\n- **Website embed:** ✅ (iframe + advanced)\n- **Public reports:** ✅ (shareable report links)\n- **Report generation:** 5 credits per refresh\n- **Quality gate:** ✅ Included\n- **API access:** ✅ Full API, webhooks, headless mode\n- **CRM import:** ✅\n- **CSV export:** ✅\n- **JSON export:** ✅\n- **Overage billing:** €1/credit (configurable cap)\n\n### Interviews Plan — €79/month (€790/year) — Most Popular\nFor teams that need more credits and free report refreshes.\n\n- **Credits:** 79 credits/month included\n- **Active studies:** Unlimited\n- **Text interviews:** ✅ (1 credit each) — [learn more](/docs/text-interview-experience)\n- **Voice interviews:** ✅ (3 credits each) — [learn more](/docs/voice-interview-experience)\n- **Structured questions:** ✅ (scale, single choice, multiple choice, ranking, yes/no, open-ended)\n- **Multi-language support:** ✅ (30+ languages)\n- **Branded intake forms:** ✅\n- **Custom interview URLs:** ✅\n- **Lead collection form:** ✅\n- **Custom badges:** ✅\n- **Footer customization:** ✅\n- **Custom link previews:** ✅\n- **Remove \"Powered by\" badge:** ✅\n- **Website embed:** ✅ (iframe + advanced)\n- **Public reports:** ✅ (shareable report links)\n- **Report generation:** Free (unlimited refreshes)\n- **Quality gate:** ✅ Included\n- **API access:** ✅ Full API, webhooks, headless mode\n- **MCP server:** ✅ Manage studies from Claude, Cursor, and other AI assistants\n- **CRM import:** ✅\n- **CSV export:** ✅\n- **JSON export:** ✅\n- **Overage billing:** €1/credit (configurable cap)\n\n### Enterprise — Custom Pricing\nFor organizations with advanced security, compliance, or volume needs.\n\n- **Credits:** 500+/month (negotiable per contract)\n- **Active studies:** Unlimited\n- **All Interviews features** plus:\n- **SSO/SAML:** ✅\n- **Custom domain:** ✅\n- **White-label:** ✅\n- **Dedicated success manager:** ✅\n- **Custom integrations:** ✅\n\n---\n\n## How Credits Work\n\nCredits are the currency of Koji. Different actions cost different amounts:\n\n| Action | Credit Cost |\n|--------|------------|\n| [Text chat interview](/docs/text-interview-experience) | 1 credit |\n| [Voice interview](/docs/voice-interview-experience) | 3 credits |\n| Report refresh | 5 credits (free on Interviews+) |\n\n**Key rules:**\n- Credits reset monthly on your billing anniversary\n- The **[quality gate](/docs/how-the-quality-gate-works)** protects your budget — conversations scoring below 3 on the 1-5 scale are completely free\n- Overage credits are billed at a flat **€1/credit** on paid plans — [learn more about overage](/docs/overage-billing-explained)\n- You can set an overage cap to control unexpected costs\n- [Structured questions](/docs/structured-questions-guide) (scale, choice, ranking, yes/no) are included in every interview at no extra credit cost — giving you both qualitative depth and quantitative data in a single credit\n\n---\n\n## What Are Structured Questions?\n\nKoji supports **quantitative structured questions** alongside its signature conversational depth. Every plan includes:\n\n- **Scale questions** (1-5, 1-7, 1-10) — for NPS, CSAT, satisfaction ratings\n- **Single choice** — radio-button-style selection\n- **Multiple choice** — multi-select options\n- **Ranking** — drag-and-drop preference ordering\n- **Yes/No** — binary questions\n- **Open-ended** — free-text for qualitative depth\n\nThe AI interviewer delivers these conversationally, naturally weaving quantitative data collection into qualitative conversations. This makes Koji the only platform that combines survey-scale data collection with interview-depth insights — and [structured questions](/docs/structured-questions-guide) consume no extra credits beyond the standard interview cost.\n\n---\n\n## Quick Decision Framework\n\n| If you need... | Choose... |\n|---------------|----------|\n| To try Koji risk-free | **Free** |\n| Monthly credit renewal for regular research | **Insights** (€29/mo) |\n| More credits and free report refreshes | **Interviews** (€79/mo) |\n| High volume, dedicated support, custom contracts | **Enterprise** |\n\nAll plans include the same features — voice interviews, text interviews, API access, structured questions, branded forms, CRM import, and exports. The main differentiators are **credit volume** and **report refresh pricing**.\n\n---\n\n## Common Scenarios\n\n| Scenario | Recommended Plan |\n|----------|------------------|\n| Startup validating a product idea with 20 interviews/month | **Insights** |\n| Product team running weekly voice research sprints | **Interviews** |\n| Agency managing research for multiple clients | **Interviews** or **Enterprise** |\n| Research ops team with 100+ monthly interviews | **Enterprise** |\n| Student or freelancer doing occasional research | **Free** |\n| Customer success team running NPS and churn surveys | **Insights** or **Interviews** |\n| HR team conducting employee engagement surveys | **Insights** |\n\n---\n\n## Annual Billing Discount\n\nSave **2 months free** by choosing annual billing:\n\n| Plan | Monthly | Annual | You Save |\n|------|---------|--------|----------|\n| Insights | €29/mo | €290/yr (€24.17/mo) | €58/yr |\n| Interviews | €79/mo | €790/yr (€65.83/mo) | €158/yr |\n\n---\n\n## What All Plans Include\n\nEvery Koji plan — including Free — comes with:\n\n- AI-guided conversational interviews (text and voice)\n- [Structured quantitative questions](/docs/structured-questions-guide) (scale, choice, ranking, yes/no)\n- Multi-language support (30+ languages)\n- Research methodology frameworks (Mom Test, JTBD, Discovery, Exploratory, Lead Magnet)\n- Full question library with probe points and guardrails\n- Study publishing and shareable interview links\n- Automated insights and analysis\n- Response filtering and transcript viewing\n- PDF research reports\n- Branded intake forms, custom URLs, and lead collection\n- Custom badges, footer customization, and link previews\n- Remove \"Powered by\" badge\n- API access, webhooks, and headless mode\n- CRM import and CSV/JSON export\n- Website embed (iframe + advanced)\n- [Quality gate](/docs/how-the-quality-gate-works) on every conversation\n- [Unlimited studies](/docs/understanding-usage-limits)\n\nThe only differences between plans are **credit volume**, **report refresh pricing** (5 credits on Free/Insights, free on Interviews+), and **overage availability** (paid plans only).","category":"Billing & Plans","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Plan Comparison Guide — Koji Documentation","metaDescription":"Compare Koji's credit-based pricing: Free, Insights (€29/mo), Interviews (€79/mo), and Enterprise plans. Find the right fit for your research.","keywords":["koji pricing","koji plans","research tool pricing","credit-based pricing","koji free plan","koji enterprise"],"aiSummary":"Koji offers four credit-based pricing plans: Free (10 starter credits), Insights (€29/mo, 29 credits), Interviews (€79/mo, 79 credits), and Enterprise (custom, 500+). All features — voice, text, API, structured questions, branding, exports — are available on every plan. Text interviews cost 1 credit, voice costs 3, report refreshes cost 5 (free on Interviews+). The quality gate ensures conversations scoring below 3 on the 1-5 scale are free. All plans include unlimited studies.","aiPrerequisites":[],"aiLearningOutcomes":["Compare all four Koji plan tiers and their features","Choose the right plan based on research needs","Understand billing cycles and payment options","Know how the quality gate benefits every plan"],"aiDifficulty":"beginner","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"390f5815-a839-47d7-a453-5685b9438b7e","slug":"upgrading-your-plan","title":"Upgrading Your Plan","url":"https://www.koji.so/docs/upgrading-your-plan","summary":"Koji offers seamless plan changes between Free, Insights (€29/mo), Interviews (€79/mo), and Enterprise. All features are available on every plan — the difference is credit volume, report refresh pricing, and overage availability. Upgrades take effect immediately with prorated billing. Downgrades take effect at the end of the billing period. All data is preserved and all plans have unlimited studies.","content":"## Changing Your Koji Plan\n\nKoji makes it easy to move between plans as your research needs evolve. Whether you're upgrading for more credits, downgrading to save costs, or switching between monthly and annual billing, here's how it works.\n\n---\n\n## Available Plans\n\n| Plan | Monthly | Annual | Credits/Month |\n|------|---------|--------|---------------|\n| **Free** | €0 | — | 10 (one-time) |\n| **Insights** | €29/mo | €290/yr | 29 |\n| **Interviews** | €79/mo | €790/yr | 79 |\n| **Enterprise** | Custom | Custom | 500+ |\n\nAll plans include the same features — the difference is **credit volume**, **report refresh pricing**, and **overage availability**. For a detailed breakdown, see the [Plan Comparison Guide](/docs/plan-comparison-guide).\n\n---\n\n## How to Upgrade\n\n### From Free to a Paid Plan\n1. Navigate to your **Dashboard**\n2. Click on **Upgrade** or visit the **Pricing** page\n3. Select your desired plan (Insights or Interviews)\n4. Choose monthly or annual billing\n5. Complete checkout via Stripe\n6. Your new credits are available immediately\n\n### From Insights to Interviews\n1. Go to **Dashboard** → **Profile** → **Billing**\n2. Click **Change Plan** or access the Stripe billing portal\n3. Select the Interviews plan\n4. The upgrade takes effect immediately\n5. You'll receive a prorated credit for unused time on your current plan\n\n### Upgrading to Enterprise\nEnterprise plans are custom-negotiated. Contact the Koji team to discuss your needs:\n- Volume-based credit allocations (500+ credits/month)\n- Dedicated success manager\n- Custom integrations and SLAs\n\n---\n\n## How Proration Works\n\nWhen you upgrade mid-billing cycle:\n\n1. **Immediate access** — you get the new plan's credits right away\n2. **Prorated charge** — you're charged only for the remaining days in your current billing period at the new plan's rate\n3. **Prorated credit** — you receive a credit for the unused days on your previous plan\n\n**Example:** If you upgrade from Insights (€29/mo) to Interviews (€79/mo) halfway through your billing period:\n- You receive a ~€14.50 credit for the unused half of Insights\n- You're charged ~€39.50 for the remaining half on Interviews\n- Net charge: ~€25 for the remainder of the period\n- Next full billing period: €79\n\n---\n\n## How to Downgrade\n\n### From a Paid Plan to Free\n1. Go to **Dashboard** → **Profile** → **Billing**\n2. Click **Cancel Subscription** or access the Stripe billing portal\n3. Your paid plan remains active until the end of your current billing period\n4. After that, you revert to the Free plan\n\n### From Interviews to Insights\n1. Access the Stripe billing portal from **Dashboard** → **Profile** → **Billing**\n2. Select the Insights plan\n3. The change takes effect at the **end of your current billing period**\n4. You retain full access until then\n\n### What Happens to Your Data on Downgrade\n\n**Nothing is deleted.** All your data is preserved:\n\n- ✅ All studies remain intact and accessible (unlimited on every plan)\n- ✅ All interview transcripts and responses are kept\n- ✅ Reports remain accessible\n- ✅ Settings and configurations are preserved\n\nSince all plans include unlimited studies and the same feature set, downgrading only affects your monthly credit allocation and whether report refreshes cost credits.\n\n---\n\n## Switching Billing Frequency\n\n### Monthly to Annual\n- Save **2 months free** by switching to annual billing\n- Access the Stripe billing portal and select the annual option\n- The switch takes effect at your next billing date\n\n### Annual to Monthly\n- Switch at any time via the Stripe billing portal\n- The change takes effect when your annual term ends\n- No early termination fees\n\n---\n\n## What Changes Between Plans\n\nSince all features are available on every plan, the key differences when upgrading are:\n\n| Aspect | Free | Insights | Interviews | Enterprise |\n|--------|------|----------|------------|------------|\n| **Credits** | 10 (one-time) | 29/month | 79/month | 500+/month |\n| **Report refreshes** | 5 credits | 5 credits | Free | Free |\n| **Overage** | Not available | €1/credit | €1/credit | €1/credit |\n| **Studies** | Unlimited | Unlimited | Unlimited | Unlimited |\n\nAll plans include: [text](/docs/text-interview-experience) and [voice](/docs/voice-interview-experience) interviews, [structured questions](/docs/structured-questions-guide), API access, webhooks, headless mode, CRM import, branded forms, custom URLs, CSV/JSON export, lead collection, and the [quality gate](/docs/how-the-quality-gate-works).\n\n---\n\n## Frequently Asked Questions\n\n### Will I lose my data if I downgrade?\nNo. All studies, interviews, transcripts, and reports are preserved. Since all plans have unlimited studies, nothing is paused or archived on downgrade.\n\n### Can I upgrade in the middle of a billing cycle?\nYes. Upgrades take effect immediately with prorated billing. You only pay the difference for the remaining days.\n\n### Do I lose access to features when downgrading?\nNo. All features are available on every plan, including Free. The only changes are your credit allocation and whether report refreshes cost credits.\n\n### How do I cancel my subscription?\nAccess the Stripe billing portal from **Dashboard** → **Profile** → **Billing**. Your plan stays active until the end of the current period.\n\n### What about [structured questions](/docs/structured-questions-guide)?\nStructured questions (scale, choice, ranking, yes/no) are included in all interview types at no extra credit cost. They're available on every plan.","category":"Billing & Plans","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Upgrading Your Plan — Koji","metaDescription":"Learn how to upgrade, downgrade, or switch between Koji plans. Prorated billing, data preservation on downgrade, and annual discounts explained.","keywords":["koji upgrade plan","change koji plan","koji downgrade","koji annual billing","koji proration"],"aiSummary":"Koji offers seamless plan changes between Free, Insights (€29/mo), Interviews (€79/mo), and Enterprise. All features are available on every plan — the difference is credit volume, report refresh pricing, and overage availability. Upgrades take effect immediately with prorated billing. Downgrades take effect at the end of the billing period. All data is preserved and all plans have unlimited studies.","aiPrerequisites":["plan-comparison-guide"],"aiLearningOutcomes":["Change plans through account settings","Choose between monthly and annual billing","Understand proration and billing timing","Know what happens to data when upgrading or downgrading"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"1e06e547-c056-4c7a-b3a1-e046cf1bc89c","slug":"understanding-usage-limits","title":"Understanding Usage & Credits","url":"https://www.koji.so/docs/understanding-usage-limits","summary":"Koji uses a credit-based system where text interviews cost 1 credit, voice interviews cost 3, and report refreshes cost 5 (free on Interviews+). All plans include unlimited studies. Plans include Free (10 one-time credits), Insights (29/mo), Interviews (79/mo), and Enterprise (500+/mo). The quality gate ensures conversations scoring below 3 on the 1-5 scale are free. Structured questions are included at no extra credit cost. Overage is billed at €1/credit on paid plans.","content":"## How Koji's Credit System Works\n\nKoji uses a **credit-based usage model**. Every meaningful interaction on the platform consumes credits from your allocation. This system ensures you only pay for quality research — thanks to the [quality gate](/docs/how-the-quality-gate-works), low-quality conversations are always free.\n\n---\n\n## Credit Costs by Action\n\n| Action | Credit Cost | Notes |\n|--------|------------|-------|\n| **[Text chat interview](/docs/text-interview-experience)** | 1 credit | AI-moderated text conversation |\n| **[Voice interview](/docs/voice-interview-experience)** | 3 credits | AI-moderated voice conversation |\n| **Report refresh** | 5 credits | Free on Interviews and Enterprise plans |\n\nThese costs apply **only** when the conversation passes the [quality gate](/docs/how-the-quality-gate-works) (scores 3 or above on a 1-5 scale). Conversations that score below 3 are completely free.\n\n[Structured questions](/docs/structured-questions-guide) (scale, choice, ranking, yes/no) are included in every interview at no extra credit cost. Whether your interview uses open-ended questions, structured questions, or a mix of both, the credit cost remains the same — 1 credit for text, 3 for voice.\n\n---\n\n## Credits by Plan\n\n| Plan | Included Credits | Overage Rate |\n|------|-----------------|--------------|\n| **Free** | 10 (one-time starter grant) | Not available |\n| **Insights** (€29/mo or €290/yr) | 29 credits/month | €1/credit |\n| **Interviews** (€79/mo or €790/yr) | 79 credits/month | €1/credit |\n| **Enterprise** (custom) | 500+ credits/month | €1/credit |\n\nFor a detailed feature breakdown, see the [Plan Comparison Guide](/docs/plan-comparison-guide).\n\n### What this means in practice\n\nWith the **Insights plan** (29 credits/month), you could run:\n- 29 text interviews, or\n- 9 voice interviews + 2 text interviews, or\n- Any mix that adds up to 29 credits\n\nWith the **Interviews plan** (79 credits/month), you could run:\n- 79 text interviews, or\n- 26 voice interviews, or\n- 20 voice interviews + 19 text interviews\n- Plus unlimited report refreshes (free on this plan)\n\n---\n\n## Study Limits\n\n**All plans include unlimited studies.** There is no cap on the number of active studies you can run on any plan, including Free. Credits govern your interview volume, not your study count.\n\n---\n\n## When Credits Reset\n\n- **Paid plans (Insights, Interviews, Enterprise):** Credits reset on your **billing anniversary** — the date your subscription started. For example, if you subscribed on March 10, your credits reset on the 10th of each month.\n- **Free plan:** Your 10 starter credits are a **one-time grant**. They do not reset monthly.\n- **Bonus credits** (from admin grants or promotions): These do not reset and remain available until used or expired.\n\n---\n\n## Tracking Your Usage\n\nYou can monitor your credit usage from the **Dashboard**:\n\n1. Navigate to your Dashboard\n2. Your current credit balance and usage are displayed prominently\n3. View a breakdown of credits consumed by study\n4. Track how many billable conversations you've had this period\n\n### Usage Tracking Details\n\nKoji tracks these metrics per billing period:\n- **Studies created** — count of new studies\n- **Interviews completed** — total conversations\n- **Billable conversations** — conversations that passed the [quality gate](/docs/how-the-quality-gate-works) and consumed credits\n- **Report refreshes** — report generation events\n\n---\n\n## Managing Your Budget\n\n### Set an Overage Cap\nOn paid plans, you can set a **maximum overage limit** to prevent unexpected charges. This controls how many additional credits beyond your included allocation you're willing to pay for. Learn more about [how overage billing works](/docs/overage-billing-explained).\n\n- Set the cap to **0** to hard-stop at your included credits\n- Set a specific number to allow controlled overage\n- Leave it unlimited for uninterrupted research\n\n### Tips for Efficient Credit Use\n1. **Use text interviews when voice isn't necessary** — [text](/docs/text-interview-experience) costs 1 credit vs. 3 for [voice](/docs/voice-interview-experience)\n2. **Let the [quality gate](/docs/how-the-quality-gate-works) work for you** — low-quality conversations are automatically free\n3. **Batch your report refreshes** — collect more responses before refreshing to get more value per refresh\n4. **Add [structured questions](/docs/structured-questions-guide) to interviews** — get quantitative data alongside qualitative depth at no extra credit cost\n5. **Consider annual billing** — save 2 months free on any paid plan\n\n---\n\n## What Happens When You Hit Your Limit\n\n| Situation | Free Plan | Paid Plans |\n|-----------|-----------|------------|\n| Credits exhausted | Interviews pause until you upgrade | [Overage billing](/docs/overage-billing-explained) kicks in at €1/credit |\n| Overage cap reached | N/A | Interviews pause until next billing period |\n\nYou'll receive notifications as you approach your credit limit so you can plan accordingly.","category":"Billing & Plans","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Understanding Usage & Credits — Koji","metaDescription":"Learn how Koji's credit system works: text interviews cost 1 credit, voice costs 3, reports cost 5. Manage your research budget with overage caps and the quality gate.","keywords":["koji credits","koji usage limits","research credits","koji billing","credit-based pricing"],"aiSummary":"Koji uses a credit-based system where text interviews cost 1 credit, voice interviews cost 3, and report refreshes cost 5 (free on Interviews+). All plans include unlimited studies. Plans include Free (10 one-time credits), Insights (29/mo), Interviews (79/mo), and Enterprise (500+/mo). The quality gate ensures conversations scoring below 3 on the 1-5 scale are free. Structured questions are included at no extra credit cost. Overage is billed at €1/credit on paid plans.","aiPrerequisites":["plan-comparison-guide"],"aiLearningOutcomes":["Identify what resources have limits on each plan","Understand how and when limits reset","Track current usage through account settings","Manage usage strategically to maximize research output"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"1d668e04-ba64-4c60-b0ff-b63e3115d2c8","slug":"how-the-quality-gate-works","title":"How the Quality Gate Works","url":"https://www.koji.so/docs/how-the-quality-gate-works","summary":"Koji's quality gate automatically scores every conversation on a 1-5 scale. Conversations scoring below 3 are completely free — no credits consumed. Reports also filter to only include conversations scoring 3 or above, ensuring research quality. This applies on every plan including during overage billing. Typically saves ~20% on credit budgets.","content":"## The Quality Gate: Never Pay for Bad Data\n\nKoji's quality gate is a built-in protection that ensures you **only spend credits on meaningful conversations**. Any interview that scores below 3 on Koji's 1-5 quality scale is completely free — no credits consumed, no charges incurred.\n\nThis applies to **every plan**, including [overage conversations](/docs/overage-billing-explained).\n\n---\n\n## How Quality Scoring Works\n\nEvery conversation on Koji is automatically scored on a **1–5 scale** based on the depth and quality of the interaction:\n\n| Score | Quality Level | Credits Charged? |\n|-------|--------------|-----------------|\n| 1–2 | Below threshold | No — free |\n| 3 | Meets threshold | Yes — credits charged |\n| 4–5 | High quality | Yes — credits charged |\n\nThe scoring evaluates factors like:\n- **Response depth** — Did the participant give substantive, detailed answers?\n- **Conversation completeness** — Did the interview cover the key topics?\n- **Engagement level** — Was the participant actively engaged throughout?\n- **Information value** — Did the conversation generate actionable insights?\n\n---\n\n## Why the Quality Gate Matters\n\n### Budget Protection\nWithout a quality gate, every conversation would cost credits — even ones where:\n- A participant abandoned the interview early\n- Responses were too brief to be useful\n- The participant didn't engage meaningfully with the questions\n\nWith Koji's quality gate, these low-value interactions are filtered out automatically. You only pay for conversations that actually contribute to your research.\n\n### Real Impact on Your Budget\n\nConsider a typical research study with 50 total conversations:\n- ~40 conversations score 3+ (quality threshold met) — credits charged\n- ~10 conversations score below 3 — **completely free**\n\nThat's a **20% savings** on your [credit budget](/docs/understanding-usage-limits), automatically applied.\n\n---\n\n## Quality Gate Across All Plans\n\n| Plan | Quality Gate | Effect |\n|------|-------------|--------|\n| **Free** | Active | Low-quality conversations don't consume your 10 starter credits |\n| **Insights** | Active | Low-quality conversations don't consume your 29 monthly credits |\n| **Interviews** | Active | Low-quality conversations don't consume your 79 monthly credits |\n| **Enterprise** | Active | Low-quality conversations don't consume your allocated credits |\n\n### Quality Gate During Overage\nThe quality gate also applies during [overage billing](/docs/overage-billing-explained). If you've exceeded your included credits and a conversation scores below 3 on the 1-5 scale, **no overage charge** is generated. You are never billed for low-quality interactions, period.\n\n---\n\n## Quality Gate in Reports\n\nThe quality gate doesn't just protect your budget — it also protects your research quality. **Reports only include conversations that score 3 or above.** Low-scoring conversations are excluded from report aggregation, theme analysis, and statistical summaries. This ensures your research findings are built on substantive, high-quality data rather than being diluted by low-effort responses.\n\nYou can still view individual low-scoring transcripts in the Responses tab for reference, but they won't affect your aggregated insights.\n\n---\n\n## What Affects Conversation Quality\n\nSeveral factors influence whether a conversation meets the quality threshold:\n\n### Participant-Side Factors\n- **Engagement** — participants who take the interview seriously produce higher scores\n- **Response length** — one-word answers typically result in lower scores\n- **Relevance** — responses that address the actual questions score higher\n- **Completion** — finishing the full interview improves the score\n\n### Study Design Factors (You Control These)\n- **Clear, open-ended questions** — well-crafted questions encourage detailed responses\n- **Appropriate interview length** — not too short (insufficient depth) or too long (fatigue)\n- **Good probe points** — AI follow-up prompts that encourage elaboration\n- **Relevant guardrails** — keeping conversations on topic improves quality\n- **[Structured questions](/docs/structured-questions-guide)** — mixing in scale, choice, and ranking questions helps maintain engagement and produces richer data\n\n### Tips to Maximize Quality Scores\n1. **Write open-ended questions** — \"Tell me about...\" instead of \"Do you like...?\"\n2. **Set up probe points** — guide the AI to dig deeper on key topics\n3. **Keep interviews focused** — 5-8 core questions is the sweet spot\n4. **Use guardrails** — prevent conversations from going off-topic\n5. **Target the right participants** — better targeting = better engagement\n6. **Use [voice interviews](/docs/voice-interview-experience)** — voice conversations tend to produce richer, more detailed responses\n7. **Add [structured questions](/docs/structured-questions-guide)** — scale and choice questions keep participants engaged between open-ended exploration\n\n---\n\n## Viewing Quality Scores\n\nYou can see quality scores for each conversation in your study dashboard:\n\n1. Navigate to your study\n2. Open the **Responses** tab\n3. Each conversation shows its quality score (1-5)\n4. Filter by score to review which conversations met the threshold\n5. Low-scoring conversations are marked and clearly shown as free\n\n---\n\n## The Quality Gate and Research Integrity\n\nThe quality gate improves your research quality in two ways:\n\n- **Budget protection** — you only spend credits on conversations that met the quality threshold\n- **Report accuracy** — reports aggregate only conversations scoring 3 or above, ensuring your insights are built from substantive data\n- **Targeting feedback** — consistently low scores may indicate you need to adjust your participant criteria\n- **Study design insights** — patterns in low-scoring conversations reveal opportunities to refine your questions","category":"Billing & Plans","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"How the Quality Gate Works — Koji","metaDescription":"Koji's quality gate ensures conversations scoring below 3/5 are completely free. Never pay credits for low-quality interviews — protection built into every plan.","keywords":["koji quality gate","interview quality scoring","free low quality interviews","research budget protection","conversation quality"],"aiSummary":"Koji's quality gate automatically scores every conversation on a 1-5 scale. Conversations scoring below 3 are completely free — no credits consumed. Reports also filter to only include conversations scoring 3 or above, ensuring research quality. This applies on every plan including during overage billing. Typically saves ~20% on credit budgets.","aiPrerequisites":["understanding-quality-scores"],"aiLearningOutcomes":["Understand how the quality gate protects your interview budget","Know the threshold and how quality is evaluated","Maximize the quality gate benefit through study design","Distinguish between quality filtering and research bias"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"dfe7eec9-a652-4219-a204-bd00834ca979","slug":"overage-billing-explained","title":"Overage Billing Explained","url":"https://www.koji.so/docs/overage-billing-explained","summary":"Koji bills overage at a flat €1/credit on paid plans when included monthly credits are exhausted. Text overage costs €1, voice costs €3, and report refreshes cost €5. Structured questions are included at no extra cost. The quality gate (1-5 scale, threshold of 3) still applies during overage — low-quality conversations are always free. Users can set configurable overage caps to control spending.","content":"## How Overage Billing Works\n\nWhen you exceed your plan's included monthly credits, Koji's overage billing lets you continue your research without interruption. Overages are billed at a simple, flat rate with full transparency.\n\n---\n\n## Overage Pricing\n\n| Detail | Value |\n|--------|-------|\n| **Overage rate** | €1 per credit (flat, all plans) |\n| **[Text interview](/docs/text-interview-experience) overage** | €1 (1 credit) |\n| **[Voice interview](/docs/voice-interview-experience) overage** | €3 (3 credits) |\n| **Report refresh overage** | €5 (5 credits) |\n| **[Quality gate](/docs/how-the-quality-gate-works)** | Still applies — low-quality conversations are free |\n\nOverage is only available on **paid plans** (Insights, Interviews, Enterprise). Free plan users cannot go over their credit balance.\n\n[Structured questions](/docs/structured-questions-guide) (scale, choice, ranking, yes/no) are included in every interview at no extra credit cost — even during overage. Whether your interview uses open-ended questions, structured questions, or both, the credit cost stays the same.\n\n---\n\n## How Overage Is Tracked\n\nKoji uses **Stripe metered billing** to track overage in real time:\n\n1. When you complete a conversation that passes the [quality gate](/docs/how-the-quality-gate-works) (score >= 3 on the 1-5 scale), Koji checks if you have included credits remaining\n2. If your included credits are exhausted, a **meter event** is sent to Stripe\n3. The meter event records the credit cost of the action (1 for text, 3 for voice, 5 for report refresh)\n4. At the end of your billing period, Stripe tallies all overage meter events and adds them to your invoice\n\n### Credit Consumption Priority\n\nCredits are consumed in this order:\n1. **Bonus credits first** — any admin-granted or promotional credits are used before your plan's included allocation\n2. **Included plan credits** — your monthly allocation (29 for Insights, 79 for Interviews)\n3. **Overage credits** — billed at €1/credit via Stripe metering\n\n---\n\n## Configuring Your Overage Cap\n\nYou can control your maximum overage spending by setting an **overage cap**:\n\n| Setting | Behavior |\n|---------|----------|\n| **Cap = 0** | Hard stop — no overage allowed. Interviews pause when credits are exhausted. |\n| **Cap = 50** | Allow up to 50 additional credits (€50 max overage per period) |\n| **Cap = unlimited** | No limit — research continues uninterrupted, billed at €1/credit |\n\nTo configure your overage cap, contact the Koji team or adjust it through your account settings.\n\n---\n\n## The Quality Gate and Overages\n\nThe [quality gate](/docs/how-the-quality-gate-works) applies to **all conversations**, including overages. This means:\n\n- If a conversation during overage scores below 3 on the 1-5 scale, it is **completely free**\n- You are never charged for low-quality interactions, even in overage\n- This protection applies equally to text and voice interviews\n\nFor example, if you run 10 overage voice interviews and 3 of them score below the quality threshold, you're only charged for 7 (7 x 3 credits x €1 = €21), not all 10.\n\n---\n\n## When Overage Makes Sense vs. Upgrading\n\n### Stay on your current plan + overage when:\n- You occasionally exceed your credits by a small amount\n- Your usage varies significantly month to month\n- You need flexibility without commitment to a higher tier\n\n### Upgrade to the next plan when:\n- You consistently exceed your credits by more than 20-30%\n- The overage cost regularly exceeds the price difference between plans\n- You want free report refreshes (available on Interviews plan and above)\n\n### Example calculation:\nIf you're on **Insights** (€29/mo, 29 credits) and regularly use 45 credits:\n- Overage cost: 16 extra credits x €1 = €16/mo — Total: €45/mo\n- **Interviews plan**: €79/mo for 79 credits + free report refreshes\n- If you're running 45+ interviews regularly, the Interviews plan offers better value with more headroom\n\nFor a full plan comparison, see the [Plan Comparison Guide](/docs/plan-comparison-guide).\n\n---\n\n## Monitoring Overage\n\nKeep track of your overage spending:\n\n1. **Dashboard** — view your current credit usage and remaining balance\n2. **Stripe billing portal** — see detailed invoices including overage charges (access from **Dashboard** → **Profile** → **Billing**)\n3. **Usage alerts** — receive notifications as you approach your credit limit\n\nFor more on how credits work, see [Understanding Usage and Credits](/docs/understanding-usage-limits).\n\n---\n\n## Overage on Your Invoice\n\nOverage charges appear as a separate line item on your Stripe invoice:\n\n- **Base subscription**: Your plan's monthly or annual fee\n- **Metered usage**: Overage credits consumed x €1/credit\n\nInvoices are generated at the end of each billing period and processed automatically via your saved payment method. For details on managing your payment method, see [Managing Payment Methods](/docs/managing-payment-methods).","category":"Billing & Plans","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Overage Billing Explained — Koji","metaDescription":"Koji charges €1/credit for overage beyond your plan's included credits. Learn how metered billing works, how to set overage caps, and when to upgrade instead.","keywords":["koji overage","overage billing","credit overage","koji metered billing","usage-based pricing"],"aiSummary":"Koji bills overage at a flat €1/credit on paid plans when included monthly credits are exhausted. Text overage costs €1, voice costs €3, and report refreshes cost €5. Structured questions are included at no extra cost. The quality gate (1-5 scale, threshold of 3) still applies during overage — low-quality conversations are always free. Users can set configurable overage caps to control spending.","aiPrerequisites":["understanding-usage-limits","how-the-quality-gate-works"],"aiLearningOutcomes":["Understand how per-interview overage billing works","Know that the quality gate protects you even in overage","Plan around overage caps for budget predictability","Decide when overages make sense vs. upgrading your plan"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"de636705-93f7-4080-b18e-a694b9094648","slug":"managing-payment-methods","title":"Managing Payment Methods","url":"https://www.koji.so/docs/managing-payment-methods","summary":"Manage Koji payments through the Stripe billing portal, accessible from Dashboard > Profile > Billing. Update credit cards, view/download invoices, add tax IDs for VAT-compliant business invoicing, and handle failed payments. All Koji pricing is in EUR. Stripe provides PCI DSS Level 1 security — Koji never stores card details directly.","content":"## Managing Your Payment Methods\n\nKoji uses **Stripe** to securely handle all payments. You can update your card, view invoices, and manage billing settings through the Stripe billing portal — accessible directly from your Koji account.\n\n---\n\n## Updating Your Credit Card\n\n### Steps to Update Your Card\n1. Go to **Dashboard** → **Profile** → **Billing**\n2. Click **Manage Billing** to open the Stripe billing portal\n3. Under **Payment method**, click **Update**\n4. Enter your new card details\n5. Save — your new card is used for all future charges\n\n### When to Update Your Card\n- Before your current card expires\n- After receiving a failed payment notification\n- When switching to a corporate card or different payment method\n\n### Supported Payment Methods\nStripe supports most major payment methods:\n- Visa, Mastercard, American Express\n- Other card networks supported by Stripe in your region\n- Some regions may support additional methods (bank transfers, etc.)\n\n---\n\n## Viewing and Downloading Invoices\n\n### Accessing Your Invoices\n1. Go to **Dashboard** → **Profile** → **Billing**\n2. Click **Manage Billing** to open the Stripe portal\n3. Navigate to the **Invoices** or **Billing history** section\n4. View any past invoice with a detailed breakdown\n\n### What's on Your Invoice\nEach invoice includes:\n\n| Item | Description |\n|------|-------------|\n| **Plan subscription** | Base fee for your plan (Insights €29/mo, Interviews €79/mo, etc.) |\n| **Metered usage** | [Overage credits](/docs/overage-billing-explained) beyond your included allocation at €1/credit |\n| **Proration adjustments** | Credits or charges from mid-cycle [plan changes](/docs/upgrading-your-plan) |\n| **Tax** | VAT or sales tax if applicable |\n| **Total** | Final amount charged to your payment method |\n\n### Downloading Invoices\n- Click on any invoice in the Stripe portal to view the full PDF\n- Download invoices for expense reporting or accounting purposes\n- All invoices are retained indefinitely in your Stripe billing history\n\n---\n\n## Tax and VAT Invoicing\n\n### For Business Accounts\nIf you need VAT-compliant invoices for your business:\n\n1. Open the Stripe billing portal from **Dashboard** → **Profile** → **Billing**\n2. Add your **Tax ID** (VAT number, GST number, etc.)\n3. Update your **billing address** with your business address\n4. Future invoices will include your tax ID and business details\n\n### Supported Tax IDs\nStripe supports tax IDs from most countries, including:\n- EU VAT numbers\n- UK VAT numbers\n- US EIN\n- Australian ABN/GST\n- And many more regional tax identifiers\n\n### VAT for EU Customers\n- EU business customers with a valid VAT number may be eligible for **reverse charge** (0% VAT)\n- Individual EU customers are charged VAT based on their country of residence\n- Tax is calculated and applied automatically by Stripe\n\n---\n\n## Billing Currency\n\nAll Koji plans are priced in **EUR (€)**. If your card is denominated in a different currency:\n- Your bank will apply a **currency conversion** at the time of charge\n- Conversion rates and fees are set by your bank, not by Koji\n- The EUR amount on your invoice is the definitive charge\n\n---\n\n## Handling Failed Payments\n\nIf a payment fails:\n1. Stripe automatically retries the charge over the next few days\n2. You'll receive an email notification about the failed payment\n3. Update your payment method via the Stripe billing portal\n4. Once updated, Stripe retries the charge automatically\n5. Your subscription remains active during the retry period\n\n### Preventing Failed Payments\n- Keep your card details up to date\n- Ensure sufficient funds or credit limit for your plan + potential [overage](/docs/overage-billing-explained)\n- Add a backup payment method if available in your region\n\n---\n\n## Stripe Billing Portal\n\nThe Stripe billing portal is your central hub for all payment management. Access it from **Dashboard** → **Profile** → **Billing** → **Manage Billing**.\n\nFrom the portal, you can:\n- Update or change payment methods\n- View and download all invoices\n- Add tax IDs for business invoicing\n- Update billing address\n- View current subscription details\n- [Change or cancel your plan](/docs/upgrading-your-plan)\n- View upcoming invoice preview\n\n---\n\n## Payment Security\n\nYour payment information is handled exclusively by Stripe:\n- **PCI DSS Level 1** — the highest level of payment security certification\n- **256-bit SSL encryption** — all data encrypted in transit\n- **3D Secure** — additional authentication supported for compatible banks\n- **Koji never sees your card number** — all sensitive payment data stays within Stripe's secure infrastructure","category":"Billing & Plans","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Managing Payment Methods — Koji","metaDescription":"Update your credit card, view invoices, manage VAT settings, and access the Stripe billing portal for your Koji subscription.","keywords":["koji payment method","koji invoice","koji stripe portal","koji VAT","update credit card koji"],"aiSummary":"Manage Koji payments through the Stripe billing portal, accessible from Dashboard > Profile > Billing. Update credit cards, view/download invoices, add tax IDs for VAT-compliant business invoicing, and handle failed payments. All Koji pricing is in EUR. Stripe provides PCI DSS Level 1 security — Koji never stores card details directly.","aiPrerequisites":["upgrading-your-plan"],"aiLearningOutcomes":["Update payment methods through account settings","View and download invoices from the billing portal","Handle failed payments and avoid service interruptions","Configure business billing details including VAT numbers"],"aiDifficulty":"beginner","aiEstimatedTime":"5 min read"},{"type":"documentation","id":"c742246a-8af8-4a18-a310-eb67adc2bf3c","slug":"managing-research-participants","title":"Managing Research Participants: The Complete Guide to Koji's Recruit Tab","url":"https://www.koji.so/docs/managing-research-participants","summary":"Koji's Recruit tab is a full participant management system for user research. It supports CSV import with personalized interview links, status tracking (completed/partial/active/archived), quality score filtering, intake form data columns, and CSV export for CRM integration and incentive distribution. Personalized links (via ?rid= parameter) tie each response to a known contact, enabling follow-up research and longitudinal tracking across multiple study waves.","content":"# Managing Research Participants: The Complete Guide to Koji's Recruit Tab\n\nWhen you run user research at scale, managing participants becomes as important as designing the interview itself. You need to know who responded, who dropped off, which responses meet your quality bar, and how to follow up with specific individuals. Koji's participant management system — accessible via the **Recruit** tab in every study — gives you a complete operational view of your respondent pool.\n\nThis guide covers every feature in the participant management workflow: from importing a list of contacts, to tracking interview status, filtering by completion, exporting for analysis, and archiving low-quality responses.\n\n## The Recruit Tab: Your Research Operations Center\n\nEvery Koji study has a **Recruit** tab that serves as the operational hub for your participant list. Here you see every person who has started or completed your interview, along with their status, intake form data, interview quality, and individual analysis.\n\nThe key columns in the participant table:\n\n- **Name / Email** — captured via the intake form or imported from CSV\n- **Status** — Active (interview in progress), Completed, Partial (started but did not finish), Archived\n- **Quality Score** — the 1–5 AI-generated quality rating for their interview\n- **Duration** — time spent in the interview from first to last message\n- **Source** — how they accessed the interview (organic link, CSV import, custom link, API)\n- **Date** — when they first started and when they completed\n- **Intake Responses** — a column for each intake form field (email, role, company, and any custom fields you configured)\n\nClick any row to open the **Analysis Drawer** — a side panel showing that participant's full interview transcript, AI insights, quality breakdown, themes, and structured question answers without leaving the participant list.\n\n## Participant Statuses Explained\n\n**Completed:** The participant finished the interview naturally (the AI concluded the conversation) or clicked Done. Koji's quality gate has evaluated their response.\n\n**Partial:** The participant started the interview but did not complete it. Their transcript may be incomplete. Partial responses are excluded from Insights Dashboard aggregations and report generation by default.\n\n**Active:** The participant is currently in an interview session, or they have an open session that has not yet timed out.\n\n**Archived:** You have manually hidden this participant from your analysis. Archived participants are excluded from reports and the default Recruit tab view. This is useful for removing test responses, bot responses, or participants who do not match your target profile. Archiving is reversible — participants can be restored at any time.\n\n## Filtering and Searching Participants\n\nUse the filter controls at the top of the Recruit tab to narrow your view:\n\n**Status filter:**\n- All\n- Completed\n- Partial\n- Active\n- Archived\n\n**Search:**\nType any name, email address, or external ID to find a specific participant. This is useful when following up with a specific customer or verifying that a particular person's interview came through correctly.\n\n**Sort by quality score:**\nSort columns to surface your highest-quality interviews first — or to identify low-quality responses that may warrant archiving before you generate a report.\n\n## Importing Participants via CSV\n\nIf you have a list of participants you want to invite — from your CRM, customer database, or a recruitment panel — import them via CSV to pre-register them in Koji and generate personalized interview links for each person.\n\n### What the CSV Import Does\n\nWhen you import a CSV:\n1. Each row becomes a registered respondent record in Koji\n2. Each respondent receives a unique personalized interview URL with a `?rid=` parameter that tracks their identity\n3. Koji returns a downloadable CSV with the personalized links appended to each row\n4. You use those personalized links in your email outreach so responses are automatically attributed to the right person\n\n### CSV Format\n\nYour CSV needs at minimum:\n```\ndisplay_name, email\nJohn Smith, john@example.com\nSarah Jones, sarah@example.com\n```\n\nOptional columns you can include:\n- `external_id` — your internal ID for this person (CRM contact ID, user ID, etc.)\n- `metadata_company` — company name for B2B segmentation\n- `metadata_plan` — subscription plan for cohort analysis\n- `metadata_cohort` — study cohort label (e.g., \"q1-2025-enterprise\")\n- Any additional `metadata_*` columns with segment data you want to analyze later\n\n### Importing via the UI\n\nGo to **Recruit → Import Participants → Upload CSV**. Koji validates your file, shows a preview of the first few rows, and generates personalized links in about 30 seconds for lists up to 10,000 rows.\n\n### Importing via API\n\nFor automated pipelines — triggering an interview invite for every customer who reaches a milestone in your product, for example — use the Koji API:\n\n```\nPOST /api/v1/respondents/import\nAuthorization: Bearer YOUR_API_KEY\n\n{\n  \"respondents\": [\n    {\n      \"display_name\": \"John Smith\",\n      \"email\": \"john@example.com\",\n      \"external_id\": \"crm_12345\",\n      \"metadata\": {\n        \"company\": \"Acme Corp\",\n        \"plan\": \"enterprise\",\n        \"cohort\": \"q4-2025\"\n      }\n    }\n  ]\n}\n```\n\nThe API response includes a personalized `interview_url` for each respondent. See [Starting Interviews via API](/docs/starting-interviews-via-api) for complete documentation.\n\n## Personalized Interview Links\n\nPersonalized links are the mechanism that ties each response back to a specific known person. The link format is:\n\n```\nhttps://koji.so/i/your-study-slug?rid=RESPONDENT_ID\n```\n\nWhen a participant opens their personalized link:\n- Their name (if provided) appears in the welcome message\n- Their metadata is automatically attached to their interview record in the Recruit tab\n- Their intake form can be pre-filled with known information\n- Their response is attributed to their specific record\n\nThis is critical for several workflows:\n- **Follow-up research** — knowing which customer said what, so you can follow up intelligently\n- **CRM integration** — matching Koji responses back to your customer records via external_id\n- **Incentive distribution** — confirming exactly which participants completed the interview\n- **Longitudinal studies** — tracking the same participant across multiple research waves over time\n\n## Exporting Participant Data\n\nTo export your participant list:\n\n1. Go to the **Recruit** tab\n2. Apply any filters you need (e.g., Completed only, quality score above 3)\n3. Click **Export CSV**\n\nThe exported file includes:\n- All participant metadata (name, email, external_id)\n- Interview status and quality score\n- Interview duration and message count\n- All intake form responses (one column per field)\n- Date of first message and date of completion\n- Unique interview ID (for linking back to transcripts)\n- All custom metadata fields imported at the start\n\nCommon uses for participant exports:\n- Distributing gift cards or incentives to completers\n- Passing participant data back to your CRM\n- Joining with quantitative survey data for mixed-methods analysis\n- Compliance and research ethics documentation\n- Identifying participants with high-quality responses for follow-up depth interviews\n\n## Archiving and Managing Response Quality\n\nNot every response that comes in will meet your quality bar. Common reasons to archive a participant:\n\n- **Test responses** — your own test runs, or test responses from teammates before launch\n- **Off-target participants** — someone who does not match your screening criteria (they found the open link)\n- **Low-effort responses** — extremely short, nonsensical, or obviously automated answers\n- **Duplicate responses** — the same person who somehow submitted twice\n\nTo archive a participant: click the **...** menu on their row and select **Archive**. To restore an archived participant, switch the filter to **Archived** and click **Restore**.\n\nArchiving is fully non-destructive. The interview transcript and data are preserved — archiving only hides the response from your analysis views and report generation.\n\n## Koji's Automatic Quality Gate\n\nKoji automatically evaluates every completed interview before it counts toward your credit usage. The quality gate checks:\n\n- **Minimum engagement** — the participant must have exchanged enough messages to constitute a real conversation\n- **Response relevance** — the AI checks whether answers address the research topic\n- **Completion depth** — whether the AI was able to cover the key research questions in the brief\n\nInterviews that fail the quality gate are marked as **Not Counted** and do not consume a credit. You will still see them in the Recruit tab with a quality indicator, and you can review them to understand why they did not pass. See [How the Quality Gate Works](/docs/how-the-quality-gate-works) for full specification.\n\nThis is a meaningful advantage over traditional survey tools, where every low-effort submission counts against your budget and contaminates your data.\n\n## Participant Feedback\n\nEvery completed interview includes a simple thumbs up / thumbs down feedback prompt shown to participants at the end of their interview. This captures participant experience data — not response quality.\n\nParticipant feedback is visible in the Recruit tab via the Feedback column. A pattern of thumbs-down responses combined with low quality scores may indicate a problem with your interview design: the interview is too long, the questions are confusing, or participants are encountering technical issues.\n\n## Tracking Respondents Across Multiple Studies\n\nFor longitudinal research or multi-wave studies, you can track the same participant across multiple Koji studies using the `external_id` field:\n\n1. Import participants with the same `external_id` in each study wave\n2. After each wave, export participant data\n3. Join on `external_id` in your analysis tool (Excel, Google Sheets, your BI platform) to track how responses change over time\n\nThis enables powerful longitudinal analysis:\n- How does customer sentiment evolve from onboarding month one to month six?\n- How does product perception change between beta and general availability?\n- How do employee experience scores shift across quarterly pulse research waves?\n\n## Best Practices\n\n**Always use personalized links for targeted outreach.** Open recruitment links are fine for broad discovery research, but for any study where you need to know who said what, import participants first and use personalized links.\n\n**Archive test responses before analysis.** Run test interviews before launch — and remember to archive them in the Recruit tab afterward so they do not contaminate your Insights Dashboard or reports.\n\n**Use external_id for CRM integration.** Passing your CRM's contact ID as `external_id` makes it trivial to match Koji responses back to your customer database and enriches responses with all the CRM data you already have.\n\n**Export after each research wave.** Do not rely on the Koji UI for long-term data storage. Export participant data and transcripts regularly as part of your research data management practice.\n\n**Use metadata fields for segmentation.** Attaching cohort, plan, region, or segment metadata at import time makes post-analysis slicing much easier. You can filter the Recruit tab by these fields to compare how responses differ across sub-groups — enterprise vs. SMB, churned vs. retained, new vs. tenured customers.","category":"Collecting Responses","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Managing Research Participants: The Complete Guide to Koji's Recruit Tab — Koji Docs","metaDescription":"How to track, import, filter, and export research participants in Koji. Covers personalized interview links, CSV import, quality management, and longitudinal tracking.","keywords":["research participant management","interview participant tracking","managing research respondents","participant management tool","research recruit tab","research CRM integration"],"aiSummary":"Koji's Recruit tab is a full participant management system for user research. It supports CSV import with personalized interview links, status tracking (completed/partial/active/archived), quality score filtering, intake form data columns, and CSV export for CRM integration and incentive distribution. Personalized links (via ?rid= parameter) tie each response to a known contact, enabling follow-up research and longitudinal tracking across multiple study waves.","aiDifficulty":"beginner","aiEstimatedTime":"10 minutes"},{"type":"documentation","id":"64ce88a7-8e12-4e82-b662-b47bb8d65c75","slug":"crm-research-integration-guide","title":"How to Use Your CRM Data for Targeted AI Research: Import Participants and Personalize Every Interview","url":"https://www.koji.so/docs/crm-research-integration-guide","summary":"CRM-backed research lets you target exactly the customer segments that matter — churned users, high-value accounts, recently onboarded users — with personalized AI interviews that feel tailored rather than mass-distributed. Import a CSV from Salesforce, HubSpot, or any CRM, configure personalized links with participant context, and get 3–5x higher response rates than generic recruitment.","content":"# How to Use Your CRM Data for Targeted AI Research: Import Participants and Personalize Every Interview\n\nYour CRM contains the most valuable research sample you will ever have — real customers segmented by lifecycle stage, product usage, deal size, and behavior. By importing CRM data into Koji and sending personalized interview links, you can run targeted research on exactly the customer segments that matter, with response rates 3–5x higher than generic recruitment.\n\n## Why CRM-Backed Research Is Different\n\nMost research recruiting starts from scratch. You either tap a research panel of strangers who approximately match your criteria, or you run broad customer outreach and hope enough of the right people respond.\n\nCRM-backed research is fundamentally different. You already know exactly who you want to talk to. You have their name, company, deal stage, product usage tier, support ticket history, and often their job title and team. You can recruit with surgical precision instead of fishing with a broad net.\n\nThis matters because research questions are not one-size-fits-all. What you need to learn from a churned customer is completely different from what you need from a freshly onboarded user or an enterprise account that has been quiet for 90 days. Generic recruitment gives you a mixed bag. CRM-backed recruitment gives you exactly the cohort whose insights will drive your next decision.\n\n## What You Can Do with Koji + CRM Data\n\n**Segment-specific research.** Pull your churned customers from the last 90 days, your power users who have never upgraded, your enterprise accounts approaching renewal — and run targeted studies for each group with questions designed for their specific situation.\n\n**Personalized interview links.** Koji lets you create personalized interview links that pre-populate participant context. When your interview invitation says \"Hi Sarah, we noticed your team has been using the Reporting feature heavily — we would love to understand your experience,\" response rates jump significantly. Participants feel recognized, not mass-marketed to.\n\n**Longitudinal tracking.** Interview the same cohort of customers at multiple points in their journey — 7 days post-onboarding, 30 days, 90 days — using Koji's CSV import capability to maintain participant continuity across study waves.\n\n**Closed-loop research.** Connect research findings back to CRM records. When you understand why a specific segment churns, you can update CRM fields, trigger CSM alerts, or inform account health scoring with qualitative insight, not just usage metrics.\n\n## Step-by-Step: CRM to AI Interview\n\n### Step 1: Define Your Target Segment\n\nBefore touching your CRM, get precise about who you want to research. Vague segments produce unreliable data. Strong segment definitions look like:\n\n- \"Enterprise accounts (50+ seats) who completed onboarding in the last 30 days and have not activated the Reports feature\"\n- \"Customers who downgraded from the Interviews plan to the Insights plan in Q1 2026\"\n- \"Contacts tagged 'champion' in accounts with open upsell opportunities in the pipeline\"\n- \"All users who submitted a support ticket in the last 14 days that was resolved in under 2 hours\"\n\nThe more precisely you define the segment, the more targeted your questions can be — and the more actionable your findings.\n\n### Step 2: Export from Your CRM\n\nExport a CSV from Salesforce, HubSpot, Attio, or your CRM of choice. Include at minimum: first name, last name, email address. You can also include company name, account tier, and any other variables you want to reference in personalized links.\n\n**HubSpot:** Lists → Export List → CSV\n**Salesforce:** Reports → Export → CSV format\n**Attio:** Contacts view → Apply filters → Export\n\nClean the export before importing: check for duplicates, verify email formats, and confirm names are properly capitalized. A personalized link that says \"Hi MARCUS CHEN\" from a poorly cleaned export undermines the personalization effect immediately.\n\n### Step 3: Import to Koji\n\nIn Koji, navigate to your study's Recruit tab and use the CSV Import feature. Map your CSV columns to Koji's participant fields. The import process validates email addresses, removes duplicates, and creates individual participant records that you can track through to completion.\n\nFor studies where personalization matters — churn research, executive outreach, high-value account check-ins — take a few minutes to preview the imported list before distributing. Spot-check 10–15 records for data quality issues before sending to hundreds of participants.\n\n### Step 4: Configure Personalized Links\n\nKoji's personalized links feature lets you include participant-specific variables in the interview invitation. You can reference `{{first_name}}`, `{{company}}`, or custom fields you imported. Each participant's interview link is unique, which means you can track completion at the individual level without relying on email marketing attribution tools.\n\nThis individual tracking is valuable beyond completion monitoring. When you later analyze findings, you can filter by participant attributes — \"show me responses from enterprise accounts only\" or \"compare onboarding experience by plan tier\" — because the participant context flows through to your study data.\n\n### Step 5: Distribute via Your Preferred Channel\n\nFor CRM-backed research, your existing customer communication channel is almost always the most effective distribution mechanism:\n\n- **CSM-sent emails** deliver the highest trust and response rates, especially for enterprise accounts and high-value customers. A CSM who knows the account can personalize the framing beyond what the invitation template provides.\n- **CRM email sequences** work well for medium-volume campaigns (50–500 participants) where individual CSM outreach is not practical.\n- **In-product notifications** are ideal for active users — they see the research invitation in the context where they actually use the product.\n- **LinkedIn direct messages** work for contacts who are responsive on LinkedIn but not email-responsive, particularly for B2B senior personas.\n\nTrack which channel drove completions by tagging your distribution in Koji's participant notes field, so you can optimize channel selection for future studies.\n\n### Step 6: Monitor and Follow Up\n\nKoji's Recruit tab shows real-time completion status for each imported participant. You can identify who has not completed and trigger a follow-up through your CRM or email tool. A single follow-up sent 3 days after the initial invitation typically generates 40–60% of final completions — many participants intend to complete but forget without a reminder.\n\nKeep follow-ups short: \"Just a quick reminder — the interview is only 10 minutes and your input genuinely shapes how we build [product]. Click here to share your experience.\"\n\n## Designing Questions for CRM-Segmented Research\n\nWhen you know exactly who you are talking to, you can design much more targeted questions. Koji's six structured question types add particular value in CRM-backed studies because the quantitative outputs can be compared across segments.\n\n**For churn research:**\n- *Single choice:* \"Which best describes your primary reason for downgrading?\" [Cost, Missing features, Switching to a competitor, Team restructure, Other]\n- *Open-ended (with AI follow-up):* \"Walk me through what a typical week looked like when you were using the product most actively.\"\n- *Scale 1–10:* \"If we added [specific feature], how likely would you be to upgrade?\"\n\n**For expansion and upsell research:**\n- *Yes/no:* \"Has your team tried using the export functionality in the last 30 days?\"\n- *Open-ended:* \"Tell me about a moment in the last month when you wished Koji could do something it couldn't.\"\n- *Scale 1–10:* \"How confident are you that our reporting features meet your leadership's needs?\"\n\n**For onboarding research:**\n- *Ranking:* \"Rank these features by how quickly you understood their value: [Reports, Insights Dashboard, Voice Interviews, API Integration]\"\n- *Open-ended:* \"What was the first thing that made you think 'this is going to save me time'?\"\n- *Scale 1–10:* \"How well does your current workflow match what you expected when you signed up?\"\n\nThe structured questions system in Koji ensures that quantitative answers — scale ratings, choice selections, rankings — are automatically extracted and aggregated in your report. You get the distribution chart showing where your onboarding is failing and the qualitative \"why\" from the same conversation, without any manual analysis.\n\n## Connecting Research Findings Back to Your CRM\n\nThe most sophisticated use of CRM-backed research is closing the loop: updating your CRM with qualitative insights from the interviews.\n\nAfter a churn research study, you can:\n- Tag churned accounts by their stated churn reason in your CRM\n- Update health scores for at-risk accounts based on themes identified in the research\n- Create CSM tasks triggered by specific findings (\"Account said they didn't know about X feature — schedule enablement call\")\n\nAfter an expansion research study, you can:\n- Flag accounts that expressed strong interest in an unreleased feature as potential early adopters\n- Update the \"expansion likelihood\" field for accounts where research revealed strong unmet needs\n- Provide sales with specific objection-handling context for each account that participated\n\nKoji's webhook integration can automate parts of this loop — triggering a CRM update when a participant completes an interview, or flagging specific responses for CSM follow-up.\n\n## Privacy and Compliance Considerations\n\nWhen importing CRM data for research purposes:\n\n**Lawful basis.** Ensure your customer communication consent covers research invitations. In most B2B contexts, legitimate interest under GDPR applies to service improvement research sent to existing customers — but confirm this with your legal team, particularly for EU-based contacts.\n\n**Data minimization.** Import only the fields you actually need for the study. Do not import internal scoring, financial data, or proprietary account intelligence that should not appear in interview links or participant views.\n\n**Unsubscribe and opt-out.** Respect unsubscribes. Filter your CRM export to exclude contacts who have opted out of non-transactional communications. Koji tracks completion but does not manage unsubscribes — your CRM is the system of record for communication preferences.\n\n**Data retention.** Define how long participant data will be retained in Koji after the study closes. For most research, you need participant records only until findings are analyzed and documented — after that, they can be deleted from Koji while the synthesized insights live in your research repository.\n\n## Related Resources\n\n- [Personalized Interview Links: Send Targeted Research Invitations to Every Participant](/docs/personalized-interview-links)\n- [Importing Participants via CSV](/docs/importing-participants-csv)\n- [Managing Research Participants: The Complete Guide to Koji's Recruit Tab](/docs/managing-research-participants)\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide)\n- [Churned Customer Interviews: How to Talk to Users Who Left (and Win Them Back)](/docs/churned-customer-interviews)\n- [Research Automation: How to Build Real-Time Research Pipelines with Webhooks](/docs/research-automation-webhooks)","category":"Collecting Responses","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"How to Use Your CRM Data for Targeted AI Research | Koji","metaDescription":"Your CRM is your best research sample. Learn how to import customer segments into Koji, send personalized AI interview links, and get 3–5x higher response rates than generic research recruitment.","keywords":["CRM research integration","participant import user research","personalized interview links","targeted user research","customer data research","CRM user research workflow"],"aiSummary":"CRM-backed research lets you target exactly the customer segments that matter — churned users, high-value accounts, recently onboarded users — with personalized AI interviews that feel tailored rather than mass-distributed. Import a CSV from Salesforce, HubSpot, or any CRM, configure personalized links with participant context, and get 3–5x higher response rates than generic recruitment.","aiPrerequisites":["Access to a CRM with exportable contact data","An active Koji study with defined research objectives","Basic familiarity with CSV exports"],"aiLearningOutcomes":["Export targeted customer segments from any CRM into a research-ready format","Import participants into Koji and configure personalized interview links","Design structured questions optimized for specific CRM-defined cohorts","Close the loop by connecting research findings back to CRM records"],"aiDifficulty":"intermediate","aiEstimatedTime":"15 minutes"},{"type":"documentation","id":"f330fc52-80c7-45ff-92fe-e6a470a54cba","slug":"sharing-your-interview-link","title":"Sharing Your Interview Link","url":"https://www.koji.so/docs/sharing-your-interview-link","summary":"Every Koji project has a unique interview link you can copy and share. This guide covers where to find it, how to distribute it across email, Slack, social media, embeds, and CSV imports, plus tips for maximizing response rates.","content":"Every Koji project comes with a unique interview link that participants use to start their conversation. You can find it on your project dashboard, copy it in one click, and share it anywhere your participants already are.\n\n## Where to Find Your Link\n\nOpen any project from your dashboard. Near the top of the project page, you will see your interview link displayed alongside a copy button. The format looks like this:\n\n```\nhttps://yourdomain.com/i/your-custom-slug\n```\n\nThe domain is determined by your deployment. Click the copy icon to place the full URL on your clipboard. That is all you need to start collecting responses.\n\nIf you have not published your study yet, you will see a message that reads \"Publish your project to generate an interview link.\" Make sure to [publish your study](/docs/publishing-your-study) before sending the link to participants.\n\n## Customizing the Slug\n\nThe last part of the URL, known as the slug, is something you can personalize. Instead of a random string, you might use `/i/acme-feedback` or `/i/2024-product-survey`. A descriptive slug builds trust with participants and makes the link easier to remember.\n\nSee [Customizing Interview Slugs](/docs/customizing-interview-slugs) for detailed guidance on choosing and updating your slug.\n\n## Distribution Strategies\n\nOnce you have your link, the next step is getting it in front of the right people. Here are the most effective channels.\n\n### Email Outreach\n\nEmail remains the highest-conversion channel for research recruitment. A few tips:\n\n- **Keep the subject line clear.** Something like \"Share your feedback on [Topic] (10 min)\" works well.\n- **Explain the purpose.** In two or three sentences, tell participants what the interview is about and how their input will be used.\n- **Include the link prominently.** Place it on its own line or use a button-style link so it stands out.\n- **Set expectations on time.** If your landing page shows an estimated duration, mirror that in the email.\n- **Personalize when possible.** If you are importing participants via CSV, each person can receive a unique tracking link. See [Importing Participants via CSV](/docs/importing-participants-csv) for details.\n\n### Slack and Microsoft Teams\n\nFor internal research, posting in a relevant Slack channel or Teams group is fast and effective.\n\n- Post the link with a brief description of the study and who you would like to hear from.\n- Pin the message if the study runs for more than a day so it does not get buried.\n- Consider creating a dedicated channel for ongoing research recruitment if your team runs frequent studies.\n\n### Social Media\n\nIf you are recruiting from a broader audience, social media can help you cast a wide net.\n\n- **LinkedIn** works well for professional and B2B audiences. A short post explaining the research topic, paired with the link, typically performs best.\n- **Twitter/X** is useful for reaching niche communities. Tag relevant accounts or use hashtags to increase visibility.\n- **Community forums and groups** such as Reddit, Facebook Groups, or Discord servers can be great sources if your target audience congregates there. Always follow community posting guidelines.\n\nWhen sharing on social media, the [branded landing page](/docs/interview-landing-page) that participants see becomes especially important. Make sure you have configured a compelling headline and description so the Open Graph preview looks polished when the link is shared. See [Customizing Branding](/docs/customizing-branding) for how to set OG images.\n\n### QR Codes\n\nFor in-person events, printed materials, or presentations, converting your interview link into a QR code is a quick way to bridge the offline-to-online gap. Any free QR code generator will work since the input is simply your interview URL.\n\n### Website Embed\n\nIf you want participants to complete the interview directly on your website without leaving the page, Koji offers an embeddable iframe widget. This is ideal for feedback collection on product pages, post-purchase flows, or knowledge bases.\n\nSee [Using the Embed Widget](/docs/using-the-embed-widget) for setup instructions.\n\n## Structured Questions in Interviews\n\nIf your study includes [structured questions](/docs/structured-questions-guide) (such as scales, multiple choice, or ranking questions), participants will see interactive widgets during the conversation. These work seamlessly whether the participant accesses the interview via a shared link, an embed, or a personalized CSV import link.\n\n## Tips for Higher Response Rates\n\n1. **Send at the right time.** Mid-morning on weekdays tends to perform best for professional audiences.\n2. **Follow up once.** A single reminder two to three days after the initial outreach can significantly boost participation.\n3. **Keep the ask small.** Emphasize that the interview is conversational and takes just a few minutes.\n4. **Close the loop.** After the study wraps up, let participants know what you learned. This builds goodwill for future research.\n\n## What Participants See\n\nWhen someone clicks your link, they arrive at your [interview landing page](/docs/interview-landing-page). From there, they choose voice or text mode, optionally fill out an intake form, and begin the conversation. The entire flow is designed to feel welcoming and low-friction.\n\n## Next Steps\n\n- [Publishing Your Study](/docs/publishing-your-study) — make sure your interview is live before sharing\n- [Customizing Interview Slugs](/docs/customizing-interview-slugs) — personalize your URL\n- [Importing Participants via CSV](/docs/importing-participants-csv) — send unique links to a known list\n- [Using the Embed Widget](/docs/using-the-embed-widget) — embed interviews on your website\n- [Headless API Overview](/docs/headless-api-overview) — start interviews programmatically\n- [Structured Questions Guide](/docs/structured-questions-guide) — add scales, choices, and rankings to your interview","category":"Collecting Responses","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Sharing Your Interview Link — Koji Docs","metaDescription":"Learn how to find, customize, and distribute your Koji interview link across email, Slack, social media, and more.","keywords":["interview link","share interview","distribution","email outreach","QR code","research recruitment"],"aiSummary":"Every Koji project has a unique interview link you can copy and share. This guide covers where to find it, how to distribute it across email, Slack, social media, embeds, and CSV imports, plus tips for maximizing response rates.","aiPrerequisites":["publishing-your-study"],"aiLearningOutcomes":["Find and copy your interview link","Choose effective distribution channels","Track response sources with URL parameters","Maximize participation rates"],"aiDifficulty":"beginner","aiEstimatedTime":"5 min read"},{"type":"documentation","id":"afb58fbb-21d1-4266-b7dc-3a0efb632493","slug":"importing-participants-csv","title":"Importing Participants via CSV","url":"https://www.koji.so/docs/importing-participants-csv","summary":"Upload a CSV file to import up to 500 participants into your Koji project. Each person gets a unique tracking link with pre-filled intake fields. This guide covers file format, the import process, and link distribution.","content":"When you already have a list of people you want to interview, importing them from a CSV file is the fastest way to get started. Each imported participant receives a unique tracking link, and their details are pre-filled so they can jump straight into the conversation.\n\n## When to Use CSV Import\n\nCSV import is ideal when you:\n\n- Have an existing list of customers, users, or prospects in a spreadsheet or CRM export\n- Want each participant to have their own trackable link\n- Need to pre-fill intake form fields (like name or email) so participants do not have to type them again\n- Are running a targeted study where you know exactly who should participate\n\n## How It Works\n\n1. **Prepare your CSV file** with the required columns.\n2. **Import the file** from your project's respondents tab.\n3. **Koji creates a unique record** for each row, complete with a personal interview link.\n4. **Distribute the links** by email, Slack, or any other channel.\n\nWhen a participant clicks their unique link, Koji recognizes them automatically. Their name, email, and any other imported fields appear in your results without the participant needing to re-enter anything.\n\n## Preparing Your CSV File\n\n### Required Columns\n\nThere are no strictly required columns — Koji will import whatever you provide. However, the following column names are recognized and handled specially:\n\n| Column | Purpose |\n|---|---|\n| `name`, `display_name`, `fullname`, or `full_name` | Shown in your results table and used to greet the participant |\n| `email` | Stored for follow-up and identification |\n| `external_id` or `id` | Your own identifier for matching against your CRM or database |\n\nAny additional columns (such as `company`, `role`, `segment`, or custom attributes) are stored as intake data and attached to the respondent record.\n\n### Format Requirements\n\n- **File type:** `.csv` (comma-separated values)\n- **Encoding:** UTF-8 is recommended to handle international characters\n- **Header row:** The first row must contain column names\n- **Maximum rows:** Up to 500 participants per upload\n- **No duplicate IDs:** If you include an `external_id` column, each value should be unique\n\nHere is an example file:\n\n```csv\nname,email,company,role\nJane Smith,jane@acme.com,Acme Corp,Product Manager\nBob Johnson,bob@example.org,Widgets Inc,UX Designer\nLi Wei,li.wei@startup.io,StartupCo,CEO\n```\n\n## Importing Your File\n\n1. Open your project and navigate to the **Respondents** tab.\n2. Click the **Import CSV** button.\n3. Select your CSV file from your computer.\n4. Koji will parse the file and show a preview of the detected columns and rows.\n5. Confirm the import.\n\nAfter the import completes, you will see each participant listed in the respondents table. Every row includes:\n\n- The participant's name (if provided)\n- Their unique interview link\n- A status indicator showing whether they have started or completed the interview\n\n## Unique Tracking Links\n\nEach imported participant gets a personalized URL that looks like:\n\n```\nhttps://yourdomain.com/i/your-slug?rid=abc123\n```\n\nThe `rid` parameter ties the response back to that specific person. When the participant clicks the link:\n\n- Their intake form fields are pre-filled with the data you imported\n- Their interview is automatically associated with the correct respondent record\n- You can see exactly which imported participants have completed the interview and which have not\n\n## Pre-Filling Lead Form Fields\n\nIf your project has an [intake form](/docs/intake-forms-and-consent) enabled, CSV-imported data maps to form fields automatically. For example, if your intake form collects a `name` and `email`, and your CSV has those columns, participants will see those fields already filled in when they land on the interview page.\n\nParticipants can still edit pre-filled values if needed, but in most cases they simply click through to start the interview.\n\n## Plan Availability\n\nCSV import is available on all Koji plans. The feature is gated behind the CRM import entitlement, which is included with your account. If you do not see the **Import CSV** button on the respondents tab, check your plan details or contact support.\n\n## Structured Questions and CSV Imports\n\nIf your study includes [structured questions](/docs/structured-questions-guide) (scales, multiple choice, ranking, or yes/no), participants who arrive via their personalized CSV link will see the same interactive question widgets as any other participant. Structured question responses are captured and associated with the imported respondent record automatically.\n\n## After Import: Distributing Links\n\nOnce your participants are imported, the next step is sending them their unique links. You can:\n\n- **Copy individual links** from the respondents table by clicking the link icon next to each row.\n- **Export the list** with links included, then use your email tool or CRM to send personalized outreach.\n- **Combine with the Headless API** to trigger interviews programmatically. See [Headless API Overview](/docs/headless-api-overview) for more on this approach.\n\n## Monitoring Progress\n\nThe respondents table updates in real time as participants complete interviews. You can see:\n\n- **Status:** Whether the participant has not started, is in progress, or has completed the interview\n- **Quality score:** The automatically assigned score for completed interviews\n- **Conversation details:** Click into any row to read the full transcript and see the analysis\n\n## Tips for a Smooth Import\n\n1. **Clean your data first.** Remove duplicates and fix formatting issues before uploading.\n2. **Use consistent column names.** Stick to the recognized names (`name`, `display_name`, `fullname`, `full_name`, `email`, `external_id`) so Koji maps them automatically.\n3. **Stay within the 500-row limit.** Each upload supports a maximum of 500 participants. For larger lists, split into multiple files.\n4. **Test with a small batch.** Upload five to ten rows first to verify everything looks right before importing your full list.\n5. **Communicate expectations.** When you send out the links, let participants know what to expect — a brief conversational interview that takes just a few minutes.\n\n## Next Steps\n\n- [Sharing Your Interview Link](/docs/sharing-your-interview-link) — other ways to distribute your interview\n- [Headless API Overview](/docs/headless-api-overview) — manage interviews programmatically\n- [Structured Questions Guide](/docs/structured-questions-guide) — add interactive question types to your study","category":"Collecting Responses","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Importing Participants via CSV — Koji Docs","metaDescription":"Bulk import participants from a CSV spreadsheet into Koji. Each gets a unique tracking link with pre-filled intake data.","keywords":["CSV import","bulk import","participants","spreadsheet","tracking links","respondents"],"aiSummary":"Upload a CSV file to import up to 500 participants into your Koji project. Each person gets a unique tracking link with pre-filled intake fields. This guide covers file format, the import process, and link distribution.","aiPrerequisites":["sharing-your-interview-link"],"aiLearningOutcomes":["Prepare a CSV file with the correct format","Upload participants to a project","Distribute unique tracking links","Monitor participant completion status"],"aiDifficulty":"intermediate","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"0a11c680-bf35-4e7b-9fba-ba879e13d02e","slug":"using-the-embed-widget","title":"Using the Embed Widget","url":"https://www.koji.so/docs/using-the-embed-widget","summary":"The Koji embed widget is an iframe you can place on any webpage. It supports dark and light themes, external_id tracking, event listeners for interview lifecycle events, structured question widgets, and a React wrapper. Voice mode works when the microphone permission attribute is included.","content":"The Koji embed widget lets you place an interview directly on your website so participants never have to leave the page. It works as a standard iframe and takes just a couple of minutes to set up.\n\n## When to Use the Embed Widget\n\nEmbedding is a great fit when you want to:\n\n- Collect feedback on a product page, pricing page, or help center article\n- Run a post-purchase interview on a thank-you or confirmation page\n- Integrate an interview into an existing web application or dashboard\n- Keep participants in your own branded environment\n\nIf you prefer to send participants to a dedicated interview page instead, see [Sharing Your Interview Link](/docs/sharing-your-interview-link).\n\n## Getting the Embed Code\n\n1. Open your project and navigate to the **Integrate** tab.\n2. You will see the **Embed Code Generator**, which provides ready-to-copy code snippets.\n3. Choose your configuration options (theme, dimensions) and copy the code.\n\nThe generator provides three code formats:\n\n- **Basic iframe** — the simplest option, just paste and go\n- **iframe with events** — includes a JavaScript listener for interview lifecycle events\n- **React component** — a ready-made React wrapper for Next.js, Create React App, or similar frameworks\n\n## Basic Iframe\n\nThe simplest embed is a standard HTML iframe:\n\n```html\n<iframe\n  src=\"https://yourdomain.com/embed/YOUR_PROJECT_ID\"\n  width=\"100%\"\n  height=\"700px\"\n  frameborder=\"0\"\n  allow=\"microphone\"\n  style=\"border: none; border-radius: 12px;\"\n></iframe>\n```\n\nPaste this into any HTML page, CMS block, or template. The interview will render inside the frame and handle the full participant flow — landing page, mode selection, intake form, conversation, and completion.\n\n### Key Attributes\n\n- **`allow=\"microphone\"`** — required if you want participants to use voice mode. Without this attribute, the browser will block microphone access inside the iframe.\n- **`width` and `height`** — adjust to fit your page layout. A height of 700 pixels works well for most setups.\n- **`style`** — the default border-radius of 12px gives the embed rounded corners to blend in with modern designs. Adjust this value to match your site's design language.\n\n## Configuration Options\n\n### Theme\n\nBy default, the embed uses the dark theme. You can switch to light mode by adding a query parameter:\n\n```\nhttps://yourdomain.com/embed/YOUR_PROJECT_ID?theme=light\n```\n\nChoose the theme that matches your website's design.\n\n### API Key\n\nIf you want to track embed usage or enforce origin restrictions, you can pass an API key:\n\n```\nhttps://yourdomain.com/embed/YOUR_PROJECT_ID?api_key=YOUR_KEY\n```\n\nThis is optional for basic embeds but recommended if you are embedding on a production site.\n\n### External ID\n\nTo associate embedded interviews with users in your own system, pass an external identifier:\n\n```\nhttps://yourdomain.com/embed/YOUR_PROJECT_ID?external_id=user_abc123\n```\n\nThe `external_id` is attached to the respondent record and can be used to match interview data back to your internal user database. You can combine multiple parameters:\n\n```\nhttps://yourdomain.com/embed/YOUR_PROJECT_ID?theme=light&api_key=YOUR_KEY&external_id=user_abc123\n```\n\n## Structured Questions in Embeds\n\nIf your study includes [structured questions](/docs/structured-questions-guide) such as scales, multiple choice, ranking, or yes/no questions, the interactive question widgets render fully within the embed. Participants can interact with sliders, select options, and drag to rank items just as they would on the standalone interview page.\n\n## Listening for Events\n\nThe embed communicates with your parent page through the browser's `postMessage` API. This lets you react to interview milestones — for example, showing a custom thank-you message or redirecting the user after completion.\n\nAdd a message listener to your page:\n\n```javascript\nwindow.addEventListener('message', function(event) {\n  // Always verify the origin for security\n  if (event.origin !== 'https://yourdomain.com') return;\n\n  switch(event.data.type) {\n    case 'koji:ready':\n      // The interview has loaded and is ready\n      break;\n    case 'koji:interview_started':\n      // The participant has begun the conversation\n      break;\n    case 'koji:interview_completed':\n      // The interview is finished — event.data.result has details\n      break;\n  }\n});\n```\n\n### Available Events\n\n| Event | When It Fires |\n|---|---|\n| `koji:ready` | The embed has loaded and the landing page is visible |\n| `koji:interview_started` | The participant has started the conversation |\n| `koji:interview_completed` | The interview has ended (includes result data) |\n\nUse the `koji:interview_completed` event to trigger your own follow-up actions, such as showing a discount code, redirecting to another page, or logging the completion in your analytics.\n\n## React Integration\n\nIf your site uses React, the embed code generator provides a ready-made component:\n\n```jsx\nfunction KojiInterview({ onComplete }) {\n  const iframeRef = useRef(null);\n\n  useEffect(() => {\n    function handleMessage(event) {\n      if (event.origin !== 'https://yourdomain.com') return;\n      if (event.data.type === 'koji:interview_completed') {\n        onComplete?.(event.data.result);\n      }\n    }\n    window.addEventListener('message', handleMessage);\n    return () => window.removeEventListener('message', handleMessage);\n  }, [onComplete]);\n\n  return (\n    <iframe\n      ref={iframeRef}\n      src=\"https://yourdomain.com/embed/YOUR_PROJECT_ID\"\n      width=\"100%\"\n      height=\"700px\"\n      frameBorder=\"0\"\n      allow=\"microphone\"\n      style={{ border: 'none', borderRadius: '12px' }}\n    />\n  );\n}\n```\n\nDrop this into your React app and pass an `onComplete` callback to handle interview completion.\n\n## Responsive Sizing\n\nFor responsive layouts, wrap the iframe in a container and use CSS to control its dimensions:\n\n```html\n<div style=\"width: 100%; max-width: 800px; margin: 0 auto;\">\n  <iframe\n    src=\"https://yourdomain.com/embed/YOUR_PROJECT_ID\"\n    width=\"100%\"\n    height=\"700px\"\n    frameborder=\"0\"\n    allow=\"microphone\"\n    style=\"border: none; border-radius: 12px;\"\n  ></iframe>\n</div>\n```\n\nOn mobile, the embed adjusts its internal layout automatically. A minimum width of 320 pixels is recommended.\n\n## Security Considerations\n\n- **Origin verification:** Always check `event.origin` in your message listener to prevent cross-origin attacks.\n- **API key restrictions:** If you use an API key, configure allowed origins in your project settings so only your domain can load the embed.\n- **HTTPS required:** The embed must be loaded over HTTPS to enable microphone access for voice interviews.\n\n## Troubleshooting\n\n- **Microphone not working in embed:** Make sure the `allow=\"microphone\"` attribute is present on the iframe tag. The parent page must also be served over HTTPS.\n- **Embed not loading:** Verify the project ID in the URL is correct and that the study is published.\n- **Events not firing:** Confirm you are checking the correct origin in your event listener.\n\n## Next Steps\n\n- [Sharing Your Interview Link](/docs/sharing-your-interview-link) — alternative distribution methods\n- [Headless API Overview](/docs/headless-api-overview) — start interviews programmatically from your backend\n- [Customizing Branding](/docs/customizing-branding) — make the embed match your site's look and feel\n- [Structured Questions Guide](/docs/structured-questions-guide) — add interactive question types to your study","category":"Collecting Responses","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Using the Embed Widget — Koji Docs","metaDescription":"Embed Koji interviews on your website with an iframe. Includes configuration options, event listeners, and React integration.","keywords":["embed widget","iframe","website integration","embed code","React component","postMessage"],"aiSummary":"The Koji embed widget is an iframe you can place on any webpage. It supports dark and light themes, external_id tracking, event listeners for interview lifecycle events, structured question widgets, and a React wrapper. Voice mode works when the microphone permission attribute is included.","aiPrerequisites":["sharing-your-interview-link"],"aiLearningOutcomes":["Add the embed widget to a webpage","Configure theme and dimensions","Listen for interview lifecycle events","Integrate with a React application"],"aiDifficulty":"intermediate","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"f3d478ec-3b24-44b7-bf0e-9be3d37b2275","slug":"headless-api-overview","title":"Headless API Overview","url":"https://www.koji.so/docs/headless-api-overview","summary":"The Headless API lets you manage Koji interviews programmatically via REST endpoints. Start conversations, exchange messages, and complete interviews from your own backend. Available on all plans with credit-based usage. Rate limited to 60 requests per minute.","content":"The Headless API lets you run Koji interviews entirely from your own backend. Instead of sending participants to a link or embedding an iframe, you make REST API calls to start conversations, send messages, and mark interviews as complete. This is ideal for building fully custom interview experiences or integrating Koji into existing workflows.\n\n## What Is the Headless API?\n\nThink of it as Koji without the front end. Your application controls the entire flow:\n\n1. **Start an interview** by calling the start endpoint. Koji returns an interview ID, a session token, and the first message.\n2. **Exchange messages** by sending participant responses to the message endpoint. Koji replies with follow-up questions.\n3. **Complete the interview** by calling the complete endpoint. Koji triggers its analysis pipeline and generates insights.\n\nYour participants never interact with Koji's interface directly — they interact with whatever UI you build.\n\n## Plan Availability\n\nThe Headless API is available on all Koji plans. Usage is controlled by your credit balance — each interview started via the API consumes credits just like any other interview. See your account settings for current credit balance and usage.\n\n## Getting Started\n\n### Authentication\n\nAll API requests require a Bearer token in the `Authorization` header. You can generate API keys from your project settings.\n\n```\nAuthorization: Bearer YOUR_API_KEY\n```\n\nYour API key must have the appropriate permissions for the endpoints you want to use. See [API Authentication](/docs/api-authentication) for setup instructions.\n\n### Base URL\n\nAll endpoints are available at:\n\n```\nhttps://yourdomain.com/api/v1/interviews\n```\n\n## Core Endpoints\n\n### Start an Interview\n\n```\nPOST /api/v1/interviews/start\n```\n\nCreates a new interview and returns the opening message.\n\n**Request body:**\n\n```json\n{\n  \"respondent\": {\n    \"external_id\": \"crm-contact-456\",\n    \"display_name\": \"Jane Smith\",\n    \"metadata\": {\n      \"source\": \"crm-integration\",\n      \"segment\": \"enterprise\"\n    }\n  },\n  \"mode\": \"text\",\n  \"locale\": \"en\"\n}\n```\n\n**Response:**\n\n```json\n{\n  \"interview_id\": \"int_abc123\",\n  \"session_token\": \"stk_xyz789\",\n  \"initial_message\": \"Hi Jane! Thanks for taking the time...\"\n}\n```\n\nThe `respondent` object is optional. If provided, it accepts these fields:\n\n| Field | Purpose |\n|---|---|\n| `external_id` | Your own identifier for matching against your CRM or database |\n| `display_name` | Used to greet the participant and shown in your results |\n| `metadata` | Flexible key-value pairs for segmentation and tracking |\n\nThe `mode` field is optional and defaults to text. The `locale` field is optional and defaults to the study's configured language.\n\n### Send a Message\n\n```\nPOST /api/v1/interviews/:id/message\n```\n\nSends the participant's response and receives the next question.\n\n**Request body:**\n\n```json\n{\n  \"message\": \"I've been using the product for about six months now...\"\n}\n```\n\n**Response:**\n\n```json\n{\n  \"message\": {\n    \"role\": \"assistant\",\n    \"content\": \"That's great to hear. What was your initial impression when you first started using it?\"\n  },\n  \"turn_count\": 3,\n  \"is_complete\": false\n}\n```\n\nThe `is_complete` flag tells you whether the interviewer has gathered enough information and is ready to wrap up. You can use this to decide when to call the complete endpoint.\n\n### Complete an Interview\n\n```\nPOST /api/v1/interviews/:id/complete\n```\n\nMarks the interview as finished and triggers Koji's analysis pipeline.\n\n**Response:**\n\n```json\n{\n  \"status\": \"completed\",\n  \"quality_score\": 4.2\n}\n```\n\nOnce completed, the interview appears in your project dashboard with a full transcript, quality score, and generated insights.\n\n### Get Interview Details\n\n```\nGET /api/v1/interviews/:id\n```\n\nRetrieve the full transcript and metadata for a specific interview.\n\n## Structured Questions via API\n\nIf your study includes [structured questions](/docs/structured-questions-guide) (scales, multiple choice, ranking, or yes/no), the AI interviewer will present these during the conversation. When using the Headless API, structured question prompts appear in the assistant's message content. Your UI should handle rendering appropriate input widgets based on the question type indicated in the response.\n\n## Common Use Cases\n\n### In-App Feedback\n\nEmbed a feedback flow inside your own product. When a user triggers a feedback prompt, your backend starts a Koji interview, then your front end presents the conversation in your own UI components.\n\n### CRM Integration\n\nConnect Koji to your CRM pipeline. When a deal reaches a certain stage, automatically trigger an interview with the contact. Responses flow back into the CRM record.\n\n### Chatbot Handoff\n\nRoute specific conversation topics from your existing chatbot to a Koji interview. The participant continues the conversation naturally while Koji handles the research-quality follow-up questions.\n\n### Batch Research\n\nCombine CSV import with the Headless API for large-scale studies. Import your participant list, then programmatically start and manage interviews for each person.\n\n## Rate Limiting\n\nThe API enforces a rate limit of 60 requests per minute to ensure fair usage across all customers. If you exceed the limit, you will receive a `429 Too Many Requests` response. Rate limit information is included in response headers:\n\n| Header | Description |\n|---|---|\n| `X-RateLimit-Limit` | Maximum requests allowed per window |\n| `X-RateLimit-Remaining` | Requests remaining in the current window |\n| `X-RateLimit-Reset` | Unix timestamp when the window resets |\n\nBuild retry logic into your integration to handle rate limit responses gracefully.\n\n## Error Handling\n\nAll error responses follow a consistent format:\n\n```json\n{\n  \"error\": \"Description of what went wrong\"\n}\n```\n\nCommon status codes:\n\n| Code | Meaning |\n|---|---|\n| 401 | Missing or invalid API key |\n| 403 | API key lacks the required permission, or origin not allowed |\n| 404 | Interview not found |\n| 429 | Rate limit exceeded |\n\n## Best Practices\n\n1. **Store interview IDs.** Save the `interview_id` returned by the start endpoint so you can continue the conversation and retrieve results later.\n2. **Respect the is_complete flag.** When the response indicates the interview is ready to wrap up, call the complete endpoint to trigger analysis.\n3. **Handle errors gracefully.** Implement retry logic for transient failures and surface clear error messages to your users.\n4. **Secure your API key.** Never expose your API key in client-side code. All API calls should go through your backend.\n\n## Next Steps\n\n- [API Authentication](/docs/api-authentication) — generate and manage API keys\n- [Sharing Your Interview Link](/docs/sharing-your-interview-link) — alternative ways to collect responses\n- [Structured Questions Guide](/docs/structured-questions-guide) — add interactive question types to your study","category":"Collecting Responses","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Headless API Overview — Koji Docs","metaDescription":"Use the Koji REST API to start, manage, and complete interviews programmatically from your own backend code.","keywords":["headless API","REST API","programmatic interviews","API integration","Scale plan","backend integration"],"aiSummary":"The Headless API lets you manage Koji interviews programmatically via REST endpoints. Start conversations, exchange messages, and complete interviews from your own backend. Available on all plans with credit-based usage. Rate limited to 60 requests per minute.","aiPrerequisites":["api-authentication"],"aiLearningOutcomes":["Understand the Headless API architecture","Authenticate API requests","Start, message, and complete interviews via REST","Handle errors and rate limits"],"aiDifficulty":"advanced","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"9ac42d53-9726-4f8a-a12b-5c5e1b926534","slug":"customizing-interview-slugs","title":"Customizing Interview Slugs","url":"https://www.koji.so/docs/customizing-interview-slugs","summary":"Customize the slug in your Koji interview URL to make links more memorable and trustworthy. Slugs are globally unique, must be 3-50 characters of lowercase letters, numbers, and hyphens, and require the customizable slug entitlement to edit.","content":"Your interview slug is the last part of the URL that participants see when they click your link. Customizing it makes the link more memorable, more professional, and more trustworthy. Instead of a random identifier, your link can read `/i/acme-product-feedback` or `/i/2024-ux-study`.\n\n## What Is an Interview Slug?\n\nEvery Koji interview has a URL in this format:\n\n```\nhttps://yourdomain.com/i/your-slug-here\n```\n\nThe slug is the `your-slug-here` portion. When you create a new project, Koji generates a default slug based on your project name. You can change it at any time.\n\n## How to Change Your Slug\n\nSlug editing requires the **customizable slug** entitlement, which is available on paid plans. If your plan includes this entitlement:\n\n1. Open your project from the dashboard.\n2. Find your interview link displayed near the top of the project page.\n3. Click the **edit** (pencil) icon next to the slug.\n4. Type your new slug.\n5. Click **Save**.\n\nKoji validates the new slug immediately. If the slug is already taken, you will see a `409 Conflict` error and need to choose a different one.\n\n## Slug Format Rules\n\nSlugs must follow these rules:\n\n- **Lowercase letters only** — uppercase letters are automatically converted to lowercase\n- **Numbers are fine** — `2024-study` works perfectly\n- **Hyphens for separators** — use hyphens instead of spaces or underscores\n- **No special characters** — only `a-z`, `0-9`, and `-` are permitted (regex: `/^[a-z0-9-]+$/`)\n- **Length:** Between 3 and 50 characters\n\nExamples of valid slugs:\n\n```\nacme-feedback\nproduct-research-q1\nux-study-2024\ncustomer-interviews\n```\n\nExamples of invalid slugs:\n\n```\nAcme_Feedback      (uppercase and underscores)\nmy study           (spaces)\nresearch@acme      (special characters)\nab                 (too short — minimum 3 characters)\n```\n\n## Best Practices for Memorable Slugs\n\n### Keep It Short and Descriptive\n\nThe best slugs are short enough to remember but descriptive enough to convey purpose. Aim for two to four words separated by hyphens.\n\n**Good:** `acme-feedback`, `product-research`, `onboarding-study`\n**Too long:** `acme-corporation-quarterly-product-feedback-survey-2024`\n**Too vague:** `study`, `interview`, `research`\n\n### Include Your Brand\n\nIf you are sharing the link externally, including your brand name or a recognizable shorthand helps participants feel confident they are clicking a legitimate link.\n\n```\nacme-feedback\nmyapp-ux-research\nwidgets-customer-voice\n```\n\n### Add Context\n\nIf you run multiple studies, add a distinguishing element like the topic, quarter, or audience:\n\n```\nacme-onboarding-q1\nacme-pricing-feedback\nacme-enterprise-interviews\n```\n\n### Think About Sharing\n\nWhen your link is shared on social media or in a message, the slug is visible to everyone. A clean, professional slug reflects well on your study and your brand.\n\nLinks that look like `/i/acme-product-feedback` inspire more confidence than `/i/x7f92k3`.\n\n## When to Change Your Slug\n\nYou can change your slug at any time, but keep these considerations in mind:\n\n- **Before sharing:** Ideally, set your final slug before you distribute the link. This avoids the need to resend links or update references.\n- **Active links break:** If participants have already received the old link, changing the slug means those links will no longer work. There is no automatic redirect from old slugs to new ones.\n- **Imported participants:** If you have already [imported participants via CSV](/docs/importing-participants-csv), their unique tracking links include the slug. Changing the slug will break those links.\n\nThe safest approach is to finalize your slug before [publishing your study](/docs/publishing-your-study) and distributing links.\n\n## Slug Availability\n\nSlugs are unique **globally** across all Koji accounts. Two projects anywhere on the platform cannot share the same slug, even if they belong to different users. If you try to use a slug that is already in use by any project, Koji will return a `409 Conflict` error and ask you to choose a different one.\n\nIf you delete or archive a project, its slug becomes available for reuse.\n\n## How the Slug Affects SEO and Open Graph\n\nWhen someone shares your interview link on social media, the Open Graph preview uses your [branding settings](/docs/customizing-branding) — headline, description, and OG image. The slug itself appears in the URL preview, so a descriptive slug like `/i/acme-product-feedback` looks more clickable than a random string.\n\nSearch engines generally do not index interview pages (they require interaction to be useful), so SEO is not a primary concern here. The slug's value is purely in human readability and trust.\n\n## Troubleshooting\n\n### \"Slug is already taken\"\n\nAnother project on the platform is using this slug. Since slugs are globally unique, this could be a project belonging to any account. Choose a different slug — adding your brand name or a specific qualifier usually resolves conflicts.\n\n### \"Invalid slug format\"\n\nYour slug contains characters that are not allowed or does not meet the length requirements. Use only lowercase letters, numbers, and hyphens, and make sure the slug is between 3 and 50 characters long.\n\n### Links stopped working after changing the slug\n\nThe old slug is no longer active, and there is no automatic redirect. Update any shared links or email templates to use the new slug. For participants who already received the old link, you will need to resend the updated URL.\n\n## Next Steps\n\n- [Sharing Your Interview Link](/docs/sharing-your-interview-link) — distribute your customized link\n- [Publishing Your Study](/docs/publishing-your-study) — make your interview live for participants\n- [Structured Questions Guide](/docs/structured-questions-guide) — add interactive question types to your study","category":"Collecting Responses","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Customizing Interview Slugs — Koji Docs","metaDescription":"Personalize your Koji interview URL with a custom slug. Learn format rules, best practices, and how to update it.","keywords":["interview slug","custom URL","personalize link","branded URL","interview URL"],"aiSummary":"Customize the slug in your Koji interview URL to make links more memorable and trustworthy. Slugs are globally unique, must be 3-50 characters of lowercase letters, numbers, and hyphens, and require the customizable slug entitlement to edit.","aiPrerequisites":["sharing-your-interview-link"],"aiLearningOutcomes":["Change your interview slug","Follow slug format rules","Choose memorable and professional slugs","Understand the impact of changing slugs on existing links"],"aiDifficulty":"beginner","aiEstimatedTime":"5 min read"},{"type":"documentation","id":"efd92c39-a9c7-4424-885e-1e9dcdcbbb97","slug":"personalized-interview-links","title":"Personalized Interview Links: Send Targeted Research Invitations to Every Participant","url":"https://www.koji.so/docs/personalized-interview-links","summary":"Personalized interview links embed participant-specific context (name, company, role) into Koji interview URLs. The AI interviewer uses this context to greet participants by name and ask more relevant questions. Set up by importing a CSV in the Recruit tab. Supports any custom attributes and integrates with HubSpot, Salesforce, and Intercom. Works best when combined with structured questions for automatic segment-level analysis.","content":"\n# Personalized Interview Links: Send Targeted Research Invitations to Every Participant\n\nPersonalized interview links let you embed participant-specific context directly into a Koji interview URL, so the AI interviewer greets participants by name, references their company, and tailors the conversation to their specific situation — all before a single question is asked.\n\nThe result: higher response rates, more relevant conversations, and insights that are already segmented by participant attributes when you open your report.\n\n## What Are Personalized Interview Links?\n\nA standard Koji interview link looks like this:\n\n```\nhttps://interviews.koji.so/s/your-study-slug\n```\n\nA personalized link looks like this:\n\n```\nhttps://interviews.koji.so/s/your-study-slug?name=Sarah&company=Acme&role=VP+of+Engineering\n```\n\nWhen Sarah clicks her link, the AI interviewer greets her by name, knows she works at Acme as a VP of Engineering, and can reference that context throughout the conversation. \"As a VP of Engineering at a company like Acme, what does your current research workflow look like?\" is a very different opening than \"Tell me about yourself.\"\n\nThis is personalization at the interview level — not just in the invitation email.\n\n## Why Personalized Links Outperform Generic Invitations\n\nResearch on survey and interview response rates consistently shows that personalization matters. Personalized outreach generates significantly higher engagement than generic blasts. The same principle applies to qualitative research:\n\n**Participants feel seen, not processed.** When an AI interviewer references your specific role, company, or recent purchase, you know it is not a generic survey blast. The conversation feels purposeful.\n\n**Completion rates improve.** Koji customers running personalized interview campaigns report 20–40% higher completion rates compared to generic study links, simply because the opening message is contextually relevant.\n\n**Data arrives pre-segmented.** Because participant attributes are embedded in the URL, Koji automatically tags each interview with those attributes. Your analysis report can break down themes by company size, role, or any other dimension you tracked — without manual tagging.\n\n**AI follow-ups are smarter.** The AI interviewer uses participant context to ask better follow-up questions. A generic study asks \"What is your biggest challenge?\" — a personalized study asks \"As an engineering leader managing a distributed team, what is your biggest challenge?\"\n\n## How to Set Up Personalized Interview Links\n\n### Step 1: Import Your Participant List\n\nIn the **Recruit** tab of your study, click **Import via CSV**. Your CSV should have one row per participant, with columns for:\n\n- `email` (required for tracking)\n- `name` (recommended — enables personal greetings)\n- Any custom attributes: `company`, `role`, `plan`, `segment`, `cohort`, `tenure`, or anything else relevant to your research\n\nKoji accepts any column headers as custom attributes. There is no fixed schema — you define what is relevant for your study.\n\n```csv\nemail,name,company,role,plan\nsarah@acme.com,Sarah,Acme Inc,VP Engineering,enterprise\ntom@beta.io,Tom,Beta Corp,Product Manager,starter\n```\n\nUpload the CSV and Koji generates a unique personalized link for each row, with all the attributes embedded as URL parameters.\n\n### Step 2: Configure Which Fields the AI Uses\n\nIn your study brief, you can tell the AI interviewer how to use participant attributes. Navigate to the **Brief** section and add participant context instructions:\n\n> \"The participant's name is {{name}}. They work at {{company}} as a {{role}}. Reference their company context when relevant, but do not make assumptions about their technical background.\"\n\nThis gives you fine-grained control over how the AI uses personalization without hard-coding anything into the study design itself.\n\n### Step 3: Distribute Links via Your Existing Tools\n\nKoji generates a unique link per participant. You can:\n\n- **Paste links into email outreach** — Outreach, Salesloft, Mailchimp, or any tool that supports mail merge\n- **Send via your CRM** — add the Koji link field to your contact records in HubSpot, Salesforce, or Intercom\n- **Include in Slack messages** — ideal for internal employee research\n- **Embed in post-purchase or in-app flows** — trigger the interview link when a user reaches a product milestone\n\nNo third-party recruitment platform required. You own the distribution channel.\n\n### Step 4: Track Completion by Participant\n\nThe Recruit tab shows real-time completion status for each imported participant. As interviews are completed, you see exactly who has responded and who has not — enabling targeted follow-up outreach to non-responders without revealing any individual responses.\n\n## Personalized Links + Structured Questions: A Powerful Combination\n\nKoji's [structured questions](/docs/structured-questions-guide) add a quantitative dimension to your personalized interviews. Structured questions support six types: open_ended, scale, single_choice, multiple_choice, ranking, and yes_no. When combined with personalized links, you can:\n\n- **Pre-segment NPS or satisfaction scores by company or role** — not just overall averages\n- **Track ranking responses by participant cohort** — do enterprise customers rank features differently than SMB customers?\n- **Compare yes/no responses across segments** — does feature awareness vary by onboarding cohort?\n\nBecause each participant's attributes are stored alongside their interview, report aggregations automatically break down by segment. You get the depth of qualitative interviews plus the statistical cuts of structured survey data — for every attribute you tracked.\n\n## Use Case: B2B Account Expansion Research\n\nA SaaS company wants to understand why some accounts expand to higher tiers while others do not. They export 150 accounts from their CRM with attributes: `company`, `current_plan`, `months_since_signup`, and `has_expanded` (yes/no).\n\nThey import this into Koji and create a study with structured questions including:\n- A scale question (1–10): \"How well does the product support your current workflows?\"\n- A single-choice question: \"What is the primary reason you have not upgraded yet?\"\n- An open-ended question: \"Walk me through the last time you considered upgrading.\"\n\nThe AI interviewer references each participant's `company` and `current_plan`, making the conversation feel like a genuine account review rather than a cold survey.\n\nThe resulting report breaks down satisfaction scores, upgrade blockers, and upgrade intent by `current_plan` and `has_expanded` cohorts — automatically generated. Traditionally, this level of segmentation would require weeks of manual transcript coding.\n\n## Use Case: Post-Onboarding Feedback for New Users\n\nA product team wants to understand onboarding friction for users who signed up in the last 30 days. They export a user list from their database with: `name`, `email`, `signup_date`, `plan`, `completed_onboarding`, and `features_activated`.\n\nThe AI interviewer knows which features each user has activated, allowing contextual follow-ups: \"You have been using the reporting feature — what was your first impression?\" rather than generic onboarding questions.\n\nThis is the kind of contextually-aware research that was previously only possible with a skilled human moderator who had read through each participant's profile beforehand — now automated for every participant.\n\n## CRM Integration Patterns\n\nPersonalized links make Koji a natural extension of your existing CRM workflow:\n\n**HubSpot**: Add a Koji interview link to your contact sequences. When a trial user reaches day 14, trigger an automated email with their personalized Koji link via a HubSpot workflow.\n\n**Salesforce**: Add a Koji column to your opportunity records. After closing a deal (won or lost), send a personalized win/loss interview link automatically via Salesforce Flow.\n\n**Intercom**: Trigger Koji links via Intercom campaigns. \"You have been with us for 90 days — would you share your experience in a 5-minute AI interview?\" with the participant's name and plan pre-filled.\n\nIn each case, the link carries participant context so the AI interviewer starts from a position of knowledge, not a blank slate.\n\n## Privacy and Data Handling\n\nPersonalized links are one-time-use by default — a participant's link will not work if forwarded to someone else, preventing duplicate submissions and ensuring data integrity.\n\nAll participant data imported via CSV is stored securely in your Koji workspace and is subject to Koji's standard data processing terms. Participant attributes are only used to personalize the interview experience and for segmentation in your reports.\n\n## Related Resources\n\n- [Structured Questions Guide](/docs/structured-questions-guide) — Add quantitative dimensions to your personalized interviews for segment-level analysis\n- [Importing Participants via CSV](/docs/importing-participants-csv) — Step-by-step guide to setting up your participant list\n- [Managing Research Participants](/docs/managing-research-participants) — Track completions and manage your Recruit tab\n- [Customizing Your Study](/docs/customizing-your-study) — Brand your interview experience from landing page to completion\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — Configure participant context instructions for the AI interviewer\n- [B2B Customer Research with AI](/docs/b2b-customer-research-ai-interviews) — Techniques for running B2B research at scale\n","category":"Collecting Responses","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Personalized Interview Links | Koji Docs","metaDescription":"Send personalized AI interview invitations with participant names, company, and role pre-filled. Higher completion rates, richer conversations, and pre-segmented insights — all from a CSV import.","keywords":["personalized interview links","custom interview invitations","personalized survey links","CRM import research","targeted research outreach","participant personalization"],"aiSummary":"Personalized interview links embed participant-specific context (name, company, role) into Koji interview URLs. The AI interviewer uses this context to greet participants by name and ask more relevant questions. Set up by importing a CSV in the Recruit tab. Supports any custom attributes and integrates with HubSpot, Salesforce, and Intercom. Works best when combined with structured questions for automatic segment-level analysis.","aiPrerequisites":["Basic familiarity with Koji studies","A participant list in CSV format"],"aiLearningOutcomes":["Set up personalized interview links via CSV import","Configure AI participant context in the study brief","Distribute personalized links through CRM and email tools","Track participant completion status in the Recruit tab","Combine personalization with structured questions for segmented analysis"],"aiDifficulty":"beginner","aiEstimatedTime":"8 minutes"},{"type":"documentation","id":"cbf02ffd-e084-4bba-9b65-44a3a93f7c1c","slug":"koji-vs-typeform","title":"Koji vs. Typeform — When You Need Depth, Not Just Data Collection","url":"https://www.koji.so/docs/koji-vs-typeform","summary":"Comprehensive comparison of Koji (AI-powered research interviews) vs Typeform (form-based surveys). Koji conducts adaptive conversations with automatic follow-up probing, voice support, methodology guardrails, and automated insight analysis. Typeform excels at structured data collection and beautiful form design. Best for teams who need to understand the why behind customer behavior rather than collect predefined responses.","content":"## The Short Answer\n\nTypeform is excellent for structured data collection — event signups, feedback forms, lead capture. But if you need to **understand why** customers behave the way they do, Typeform's fixed-question format cannot follow up, probe deeper, or adapt to unexpected answers. Koji conducts AI-powered interviews that behave like a trained researcher: asking open-ended questions, following interesting threads, and delivering analyzed insights automatically.\n\n---\n\n## Who Each Tool Is Built For\n\n### Typeform Is Built For:\n- **Marketers** collecting leads, event registrations, and contact info\n- **Customer success teams** running NPS and CSAT surveys\n- **Operations teams** gathering structured feedback with predefined answer options\n- **Anyone** who needs a beautiful form that gets high completion rates\n\n### Koji Is Built For:\n- **Product teams** running continuous customer discovery interviews\n- **UX researchers** conducting qualitative studies at scale without manual moderation\n- **Founders and GTM teams** validating ideas through real customer conversations\n- **Market researchers** replacing focus groups with AI-moderated depth interviews\n- **Anyone** who needs to understand the *why* behind customer behavior\n\n---\n\n## Feature-by-Feature Comparison\n\n| Capability | Typeform | Koji |\n|-----------|----------|------|\n| **Question format** | Fixed questions, branching logic | Adaptive AI conversation, real-time follow-ups |\n| **Response depth** | Short answers, multiple choice, scales | Open-ended dialogue, 150-500 words per response |\n| **Follow-up probing** | ❌ Static branching only | ✅ AI probes deeper on interesting answers |\n| **Voice interviews** | ❌ | ✅ Natural voice conversations |\n| **Methodology support** | None | Mom Test, Jobs-to-be-Done, Discovery, and more |\n| **Automated analysis** | Basic analytics & charts | AI-generated themes, insights, sentiment, reports |\n| **Research reports** | ❌ Manual export to spreadsheet | ✅ Auto-generated shareable research reports |\n| **Participant experience** | Form filling (2-5 min) | Conversation (10-20 min) |\n| **Completion rates** | 40-60% for short forms | 60-80% for AI interviews |\n| **Setup time** | 15-30 minutes (design + logic) | 5-10 minutes (describe goal, AI generates plan) |\n| **API & embed** | ✅ Robust API and embed | ✅ Full API, embed widget, headless mode |\n| **MCP / AI assistant integration** | ❌ | ✅ [Claude MCP integration](/docs/mcp-overview) |\n| **Pricing model** | Per-response tiers ($29-99/mo) | Credit-based, interviews included in plan |\n\n---\n\n## Where Typeform Falls Short for Research\n\n### 1. You Cannot Follow Up on Interesting Answers\n\nA Typeform respondent writes: *\"I stopped using your product because the onboarding was confusing.\"* That is a valuable signal — but what specifically was confusing? Which step? What did they try? What would have helped?\n\nWith Typeform, you get the one-line answer and move on. With Koji, the AI interviewer automatically asks: *\"You mentioned the onboarding was confusing. Can you walk me through what happened when you first signed up?\"* — surfacing the specific friction points you need to fix.\n\n### 2. Branching Logic Is Not the Same as Conversation\n\nTypeform's branching logic lets you skip questions based on previous answers. But branches are **pre-defined** — you have to anticipate every possible path. AI interviews adapt in real-time to responses you never predicted.\n\nA [2024 study](https://www.qualtrics.com/experience-management/research/survey-fatigue/) found that survey response rates have dropped below 5% in many industries, partly because respondents feel their answers disappear into a void. Conversational interfaces like Koji's AI interviews achieve 60-80% completion rates because participants feel genuinely heard.\n\n### 3. Typeform Gives You Data, Not Insights\n\nAfter 200 Typeform responses, you have a spreadsheet. After 200 Koji interviews, you have:\n- **Automatically identified themes** across all conversations\n- **Sentiment analysis** per topic and overall\n- **Quality-scored responses** (filtering out low-effort answers via the [quality gate](/docs/how-the-quality-gate-works))\n- **A shareable research report** ready for stakeholders ([learn about reports](/docs/generating-research-reports))\n\n### 4. No Research Methodology Guardrails\n\nWhen a product manager creates a Typeform survey, nothing prevents leading questions like *\"How much do you love our new feature?\"* Koji's AI interviewer follows [proven research methodologies](/docs/choosing-a-methodology) — the Mom Test ensures questions focus on past behavior, not hypotheticals. Jobs-to-be-Done surfaces the actual progress customers are trying to make.\n\n---\n\n## Real-World Comparison\n\n### Scenario: Understanding Why Users Churn\n\n**Typeform Approach:**\n```\nQ1: Why did you cancel? (Multiple choice)\n☐ Too expensive  ☐ Missing features  ☐ Found alternative  ☐ Other\n\nQ2: How likely are you to recommend us? (1-10 scale)\n\nQ3: Any additional feedback? (Open text)\n```\n**Result:** 45% say \"Missing features.\" You still do not know *which* features, *why* they matter, or *what they switched to.*\n\n**Koji Approach:**\n```\nAI: \"Tell me about the last time you used [product]. \n     What were you trying to accomplish?\"\nUser: \"I was trying to share a report with my team but \n      the export only gave me a PDF...\"\nAI: \"What happened when you got the PDF? Walk me through it.\"\nUser: \"I needed to pull out specific quotes for a presentation. \n      With the PDF I had to manually copy-paste everything. \n      I ended up paying for Dovetail just for that feature...\"\nAI: \"You mentioned switching to Dovetail for quote extraction. \n     How long had you been looking for that capability?\"\n```\n**Result:** You discover the specific workflow gap, the competitor they switched to, how long the pain existed, and what a fix would need to look like.\n\n---\n\n## When Typeform Is the Better Choice\n\nBe honest — Typeform is better when:\n\n- You need **structured data collection** (event registrations, contact forms, order forms)\n- Your questions have **predefined answer categories** and you need quantitative metrics\n- You are running **NPS, CSAT, or satisfaction benchmarks** that require identical questions each time\n- You need **beautiful, branded form experiences** with advanced design customization\n- You are collecting **high-volume, low-depth feedback** where 5-word answers are sufficient\n\n---\n\n## When to Switch from Typeform to Koji\n\nMake the switch when you find yourself:\n\n- Adding \"Why?\" open-text fields after every multiple-choice question\n- Reading survey responses and wishing you could ask follow-ups\n- Spending hours manually analyzing open-text responses\n- Getting low response rates because respondents are fatigued by another survey\n- Presenting survey results and hearing stakeholders ask *\"but why do customers feel that way?\"*\n- Running both surveys AND scheduling manual interviews to fill the insight gap\n\n---\n\n## Migrating from Typeform to Koji\n\nThe switch is straightforward:\n\n1. **[Create your Koji account](/docs/creating-your-account)** — free tier includes interviews to test with\n2. **Translate your survey goal** — Instead of listing 20 questions, describe what you want to learn. Koji's [AI consultant](/docs/understanding-the-ai-consultant) generates the interview plan\n3. **Share the same way** — Replace your Typeform link with a [Koji interview link](/docs/sharing-your-interview-link). Works in email, Slack, social, or [embedded on your site](/docs/using-the-embed-widget)\n4. **Skip the analysis** — Results are automatically themed and summarized in your [Insights Dashboard](/docs/insights-dashboard)\n\nFor teams using Claude or AI assistants, you can also [set up the MCP integration](/docs/mcp-setup-claude) to create studies and analyze results conversationally.\n\n---\n\n## Pricing Comparison\n\n| | Typeform Basic | Typeform Plus | Typeform Business | Koji Starter | Koji Pro |\n|---|---|---|---|---|---|\n| Monthly cost | $29/mo | $59/mo | $99/mo | Free | See [pricing](/pricing) |\n| Responses/interviews | 100 responses | 1,000 responses | 10,000 responses | 5 interviews | Based on plan |\n| Depth per response | Shallow | Shallow | Shallow | Deep (AI conversation) | Deep (AI conversation) |\n| Automated analysis | ❌ Basic charts | ❌ Basic charts | ❌ Basic charts | ✅ AI themes + reports | ✅ AI themes + reports |\n| Follow-up capability | ❌ | ❌ | ❌ | ✅ | ✅ |\n| Cost per actionable insight | High (manual analysis) | High | High | Low (automated) | Low (automated) |\n\nThe real cost comparison is not price-per-response — it is **cost-per-actionable-insight**. A $99/month Typeform plan might generate 1,000 shallow responses that take 8 hours to manually analyze. Koji generates deep insights automatically from every conversation.\n\n---\n\n## Next Steps\n\n- **[Quick Start Guide](/docs/quick-start-guide)** — Run your first AI interview in 10 minutes\n- **[AI Interviews vs. Surveys](/docs/ai-interviews-vs-surveys)** — Deep dive on why conversations produce better research data\n- **[Writing a Research Question](/docs/writing-a-research-question)** — Turn your survey goal into a research question\n- **[Choosing a Methodology](/docs/choosing-a-methodology)** — Pick the right framework for your study\n- **[Voice Interview Experience](/docs/voice-interview-experience)** — See what AI voice interviews look and feel like","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Typeform — AI Interviews vs. Form-Based Surveys | Koji","metaDescription":"Compare Koji and Typeform for customer research. See why AI-powered interviews deliver deeper insights than form-based surveys, with automatic analysis, follow-up probing, and research methodology guardrails.","keywords":["Koji vs Typeform","Typeform alternative","Typeform alternative for research","AI interview vs survey form","best Typeform alternative qualitative research","Typeform vs Koji comparison","replace Typeform with AI interviews","qualitative research tool comparison","survey tool alternative","better than Typeform for user research"],"aiSummary":"Comprehensive comparison of Koji (AI-powered research interviews) vs Typeform (form-based surveys). Koji conducts adaptive conversations with automatic follow-up probing, voice support, methodology guardrails, and automated insight analysis. Typeform excels at structured data collection and beautiful form design. Best for teams who need to understand the why behind customer behavior rather than collect predefined responses.","aiPrerequisites":["Basic understanding of survey tools","Familiarity with customer research goals"],"aiLearningOutcomes":["Understand key differences between AI interviews and survey forms","Know when to use Typeform vs Koji","Compare pricing and cost-per-insight","Plan migration from Typeform to Koji"],"aiDifficulty":"beginner","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"c149b456-8daa-451d-aa58-49f8e95c3964","slug":"koji-vs-surveymonkey","title":"Koji vs. SurveyMonkey — Moving Beyond Multiple Choice to Real Customer Understanding","url":"https://www.koji.so/docs/koji-vs-surveymonkey","summary":"Comparison of Koji (AI-powered interviews) vs SurveyMonkey (quantitative survey platform). Koji conducts adaptive conversations with automatic probing, voice support, methodology guardrails, and AI-generated analysis. SurveyMonkey excels at quantitative benchmarking, NPS tracking, and high-volume structured data collection. Koji is better when teams need to understand the why behind customer behavior.","content":"## The Short Answer\n\nSurveyMonkey is the default choice for collecting quantitative feedback at scale — NPS scores, employee engagement surveys, market research polls. But when you need to understand *why* customers are dissatisfied, *what* they actually need, or *how* they make decisions, SurveyMonkey's multiple-choice format hits a ceiling. Koji conducts AI-powered conversations that adapt to each respondent, follow up on unexpected answers, and deliver analyzed insights without manual spreadsheet work.\n\n---\n\n## The Core Difference\n\n**SurveyMonkey** answers: *\"What percentage of customers are satisfied?\"*\n**Koji** answers: *\"Why are customers dissatisfied, what specific experiences drive that feeling, and what would change their mind?\"*\n\nBoth are valuable. They are fundamentally different tools for fundamentally different questions. The problem is that most teams use SurveyMonkey for *both* types of questions — and get shallow answers to the deep ones.\n\n---\n\n## Feature Comparison\n\n| Capability | SurveyMonkey | Koji |\n|-----------|-------------|------|\n| **Best for** | Quantitative feedback at scale | Qualitative understanding at scale |\n| **Question format** | Multiple choice, scales, matrix, ranking | Open-ended AI conversation with adaptive follow-ups |\n| **Response depth** | 1-15 words average | 150-500 words per response |\n| **Max responses** | 40,000/year (Premier) | Based on plan credits |\n| **Follow-up probing** | ❌ Fixed questions only | ✅ AI probes automatically on interesting answers |\n| **Voice support** | ❌ | ✅ [Natural voice interviews](/docs/voice-interview-experience) |\n| **Survey/interview design** | Manual question writing + skip logic | Describe your goal, [AI generates the plan](/docs/understanding-the-ai-consultant) |\n| **Analysis** | Charts, cross-tabs, basic text analysis | [AI-generated themes, sentiment, insights, reports](/docs/ai-generated-insights) |\n| **Research methodology** | None built-in | [Mom Test, JTBD, Discovery, and more](/docs/choosing-a-methodology) |\n| **Bias prevention** | ❌ (users write leading questions) | ✅ Methodology guardrails prevent leading questions |\n| **Shareable reports** | PDF export of charts | [Auto-generated research reports](/docs/publishing-sharing-reports) |\n| **API** | ✅ Extensive | ✅ [Full REST API + headless mode](/docs/api-authentication) |\n| **AI assistant integration** | ❌ | ✅ [Claude MCP integration](/docs/mcp-overview) |\n\n---\n\n## Why Teams Outgrow SurveyMonkey\n\n### 1. The Open-Text Analysis Problem\n\nSurveyMonkey offers open-text fields, but analyzing them is painful. After 500 responses, you have 500 short text snippets to manually read, code, and categorize. SurveyMonkey's built-in text analysis uses basic word clouds and sentiment — missing nuance, context, and the connections between themes.\n\nWith Koji, every response is a deep conversation. The platform automatically identifies [themes and patterns](/docs/understanding-themes-patterns) across all interviews, tags sentiment by topic, and surfaces the most cited issues — no manual coding required.\n\n**The math:** Manually analyzing 100 survey open-text responses takes 2-4 hours. Koji analyzes 100 interview transcripts in minutes, producing richer themes from conversations that were 10-50x more detailed than survey text fields.\n\n### 2. Survey Fatigue Is Destroying Response Rates\n\nSurvey response rates have declined steadily for two decades. The average email survey response rate is now **below 5%** in many industries. Respondents see another SurveyMonkey link and think: *\"Not another survey.\"*\n\nAI interviews flip this dynamic. Completion rates for Koji interviews average **60-80%** because the conversational format feels like being heard rather than being processed. Respondents consistently report higher satisfaction with the interview experience.\n\n### 3. SurveyMonkey Tells You What. Not Why.\n\nConsider an employee engagement survey:\n- 34% of employees rate work-life balance as \"Poor\"\n- That number is alarming. But what causes it? Long hours? Meeting overload? Unclear expectations? Childcare conflicts?\n\nA SurveyMonkey survey can tell you the number. An AI interview reveals that engineering team members specifically feel overwhelmed by after-hours Slack messages from sales, while marketing team members struggle with unclear project scope — two completely different problems requiring different solutions.\n\n### 4. The Hidden Cost of Bad Questions\n\nSurveyMonkey puts the burden of question design on you. Without research methodology training, most teams write questions that produce unreliable data:\n\n- **Leading questions:** *\"How much do you enjoy our new dashboard?\"* (presumes enjoyment)\n- **Double-barreled questions:** *\"How satisfied are you with our pricing and features?\"* (conflates two things)\n- **Hypothetical questions:** *\"Would you use X if we built it?\"* (people say yes to everything hypothetical)\n\nKoji's AI interviewer follows [proven methodologies](/docs/choosing-a-methodology) that prevent these biases. The [Mom Test methodology](/docs/mom-test-methodology) focuses on past behavior instead of hypothetical future behavior. [JTBD interviews](/docs/jobs-to-be-done-interviews) surface the actual progress customers are trying to make.\n\n---\n\n## Side-by-Side: Product Feedback Research\n\n### SurveyMonkey Approach\n```\nQ1: How satisfied are you with Feature X? (1-5 scale)\nQ2: How often do you use Feature X? (Daily/Weekly/Monthly/Never)\nQ3: Would you recommend Feature X? (1-10 NPS)\nQ4: Any additional comments? (Open text)\n\nResults (500 responses):\n- Avg satisfaction: 3.2/5\n- 60% use weekly, 15% never\n- NPS: 22\n- Comments: 80 responses, mostly 3-8 words each\n```\n\n**What you learn:** Feature X has mediocre satisfaction and moderate NPS. Not enough to make a prioritization decision.\n\n### Koji Approach\n```\nAI: \"Tell me about the last time you needed to [use case \n     that Feature X addresses]. What did you do?\"\nUser: \"I tried using Feature X but it only works with \n      CSV files. Our data is in Google Sheets...\"\nAI: \"What happened after that?\"\nUser: \"I exported to CSV, but it stripped all the \n      formatting and I lost my column headers. I ended \n      up just doing it manually in Excel.\"\nAI: \"How often does that happen — needing to use \n     data from Google Sheets?\"\nUser: \"Every single week. It is our main data source.\"\n```\n\n**What you learn:** Feature X has a specific integration gap (Google Sheets) that creates a weekly pain point. Users have developed workarounds that cost them time. The fix is clear: add Google Sheets import. One Koji interview produced more actionable insight than 500 survey responses.\n\n---\n\n## When SurveyMonkey Is the Better Choice\n\nSurveyMonkey wins when:\n\n- You need **quantitative benchmarking** — tracking NPS, CSAT, or satisfaction scores over time with identical questions\n- You need **statistical significance** from hundreds or thousands of responses\n- You are running **compliance surveys** that require standardized, auditable question sets\n- You need **advanced survey logic** — complex piping, randomization, quotas, and A/B testing of question wording\n- You are doing **academic research** that requires validated survey instruments\n- Your use case is **high-volume, low-depth** — quick polls, event feedback, preference ranking\n\n---\n\n## When to Switch to Koji\n\nMake the move when you realize:\n\n- You keep adding open-text fields because multiple choice does not capture what you need\n- You are manually reading hundreds of text responses and wishing you could ask follow-ups\n- Stakeholders ask *\"but why?\"* after every survey report presentation\n- Your response rates are declining and respondents are rushing through answers\n- You are running surveys AND booking manual interview calls to fill the qualitative gap\n- You spend more time analyzing results than collecting them\n- You need to [present research findings](/docs/presenting-research-findings) that drive real product decisions, not just pie charts\n\n---\n\n## Pricing Comparison\n\n| | SurveyMonkey Standard | SurveyMonkey Advantage | SurveyMonkey Premier | Koji |\n|---|---|---|---|---|\n| Annual cost | ~$468/yr | ~$1,188/yr | ~$1,188+/yr | Free tier + paid plans |\n| Responses/interviews | 1,000 responses/mo | 15,000/yr | 40,000/yr | Based on credits |\n| Analysis | Basic charts | Sentiment analysis | Advanced analytics | AI themes, reports, insights |\n| Follow-ups | ❌ | ❌ | ❌ | ✅ Automatic |\n| Research reports | ❌ Manual | ❌ Manual | ❌ Manual | ✅ [Auto-generated](/docs/generating-research-reports) |\n| Time to insights | Hours (manual) | Hours (manual) | Hours (manual) | Minutes (automated) |\n\n**The ROI calculation:** A product team spending 8 hours/week analyzing SurveyMonkey data at $75/hr average loaded cost spends ~$31,200/year on analysis alone. Koji eliminates that entirely with automated [insight generation](/docs/ai-generated-insights) and [themed analysis](/docs/understanding-themes-patterns).\n\n---\n\n## Getting Started\n\n1. **[Create your account](/docs/creating-your-account)** — start with the free tier\n2. **[Run your first study](/docs/creating-your-first-study)** — describe your research goal, Koji generates the interview plan\n3. **[Share your interview link](/docs/sharing-your-interview-link)** — send it wherever you used to send your SurveyMonkey link\n4. **[Review AI-analyzed results](/docs/insights-dashboard)** — themes, sentiment, and insights are generated automatically\n5. **[Generate a stakeholder report](/docs/generating-research-reports)** — shareable research reports in one click\n\n---\n\n## Next Steps\n\n- **[AI Interviews vs. Surveys](/docs/ai-interviews-vs-surveys)** — Deep dive on why conversations outperform forms\n- **[Koji vs. Typeform](/docs/koji-vs-typeform)** — How Koji compares to another popular form tool\n- **[The Definitive Guide to User Interviews](/docs/user-interview-guide)** — Master qualitative research methodology\n- **[How to Write Great Interview Questions](/docs/writing-interview-questions)** — From survey questions to research questions\n- **[Quick Start Guide](/docs/quick-start-guide)** — Your first AI interview in 10 minutes","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. SurveyMonkey — AI Interviews vs. Multiple-Choice Surveys | Koji","metaDescription":"Compare Koji and SurveyMonkey for customer research. Discover why AI-powered interviews deliver deeper, more actionable insights than multiple-choice surveys — with automated analysis, real-time follow-ups, and stakeholder-ready reports.","keywords":["Koji vs SurveyMonkey","SurveyMonkey alternative","SurveyMonkey alternative for qualitative research","AI interview vs survey","best SurveyMonkey alternative","SurveyMonkey vs Koji","replace SurveyMonkey","better than SurveyMonkey for research","survey tool alternative deep insights","qualitative research platform comparison"],"aiSummary":"Comparison of Koji (AI-powered interviews) vs SurveyMonkey (quantitative survey platform). Koji conducts adaptive conversations with automatic probing, voice support, methodology guardrails, and AI-generated analysis. SurveyMonkey excels at quantitative benchmarking, NPS tracking, and high-volume structured data collection. Koji is better when teams need to understand the why behind customer behavior.","aiPrerequisites":["Familiarity with survey tools","Basic understanding of qualitative vs quantitative research"],"aiLearningOutcomes":["Understand differences between AI interviews and surveys","Know when SurveyMonkey vs Koji is the right choice","Compare pricing and ROI","Plan migration from SurveyMonkey to Koji"],"aiDifficulty":"beginner","aiEstimatedTime":"14 minutes"},{"type":"documentation","id":"785d8f2e-bc31-410f-8d9b-cf491387a95f","slug":"koji-vs-usertesting","title":"Koji vs. UserTesting — Enterprise Research Quality at a Fraction of the Cost","url":"https://www.koji.so/docs/koji-vs-usertesting","summary":"Comparison of Koji (AI-powered interviews) vs UserTesting (enterprise usability testing platform). UserTesting offers screen recording, human moderation, and panel access starting at $15K/year. Koji provides AI-powered interviews with automatic analysis, voice support, and methodology guardrails at a fraction of the cost. Best for teams needing continuous qualitative research without enterprise budgets.","content":"## The Short Answer\n\nUserTesting built the gold standard for remote usability testing and moderated research — used by 75 of the Fortune 100. But at **$15,000-$50,000+ per year** with per-session costs of **$200-300**, it prices out most startups, scale-ups, and lean teams. Koji delivers comparable depth through AI-powered interviews at a fraction of the cost, with studies that launch in minutes instead of days and results that are analyzed automatically.\n\n---\n\n## Who Each Tool Serves\n\n### UserTesting Is Built For:\n- **Enterprise UX teams** with dedicated research budgets ($50K+/year)\n- **Usability testing** — watching users interact with prototypes and live products\n- **Panel access** — recruiting from UserTesting's proprietary participant panel\n- **Video-based studies** — screen recordings with think-aloud narration\n- **Large organizations** that need SOC 2, SSO, and enterprise compliance\n\n### Koji Is Built For:\n- **Product teams of any size** — from solo founders to enterprise research orgs\n- **Qualitative interviews** — understanding the *why* behind user behavior through conversation\n- **Continuous discovery** — running research every week, not quarterly\n- **Teams without dedicated researchers** — the AI handles moderation and analysis\n- **Anyone who needs depth without the enterprise price tag**\n\n---\n\n## Feature Comparison\n\n| Capability | UserTesting | Koji |\n|-----------|------------|------|\n| **Primary method** | Usability testing + moderated interviews | AI-powered qualitative interviews |\n| **Moderation** | Human moderators or unmoderated tasks | AI moderator with [methodology guardrails](/docs/choosing-a-methodology) |\n| **Voice support** | ✅ Video calls with screen share | ✅ [AI voice interviews](/docs/voice-interview-experience) |\n| **Participant panel** | ✅ Large proprietary panel | Bring your own + [import via CSV](/docs/importing-participants-csv) |\n| **Setup time** | Hours to days (scheduling required) | [5-10 minutes](/docs/creating-your-first-study) |\n| **Time to first result** | 1-3 days | Minutes (first interview can start immediately) |\n| **Follow-up probing** | ✅ Manual (moderator decides) | ✅ Automatic (AI probes on interesting answers) |\n| **Analysis** | Manual (watch videos, take notes) | [Automated themes, insights, reports](/docs/ai-generated-insights) |\n| **Research reports** | Manual creation | [Auto-generated, shareable](/docs/publishing-sharing-reports) |\n| **Screen recording** | ✅ Core feature | ❌ Interview-focused, not task-based |\n| **Prototype testing** | ✅ Click-through testing | ❌ (focused on conversational research) |\n| **API** | ✅ Enterprise API | ✅ [Full REST API + embed](/docs/api-authentication) |\n| **AI assistant integration** | ❌ | ✅ [Claude MCP](/docs/mcp-overview) |\n| **Pricing** | $15,000-$50,000+/year | Free tier + affordable plans |\n\n---\n\n## Where UserTesting Falls Short\n\n### 1. Cost Prohibits Continuous Research\n\nAt $200-300 per session, a 10-participant study costs **$2,000-3,000**. Running that monthly costs $24,000-36,000/year — before the platform subscription. Most teams can only afford 2-4 studies per quarter, creating long gaps between customer contact.\n\nTeresa Torres' Continuous Discovery framework recommends talking to customers **every week**. At UserTesting prices, that is mathematically impossible for most teams. Koji makes weekly research economically viable — studies launch in minutes and interviews are included in plan pricing.\n\n### 2. Scheduling Creates Delays\n\nA typical UserTesting study timeline:\n- Day 1: Design study, write tasks and questions\n- Day 2-3: Recruit and schedule participants\n- Day 3-5: Conduct sessions\n- Day 5-7: Watch recordings, take notes, synthesize\n- Day 7-10: Create and share report\n\nTotal: **7-10 business days** from question to answer.\n\nA typical Koji study timeline:\n- Minutes 1-10: Describe research goal, [AI generates the plan](/docs/understanding-the-ai-consultant)\n- Minutes 10-15: [Share interview link](/docs/sharing-your-interview-link)\n- Hours 1-48: Interviews happen asynchronously\n- Immediate: [AI analyzes and themes results](/docs/understanding-themes-patterns)\n\nTotal: **24-48 hours** from question to analyzed insights.\n\n### 3. Manual Analysis Is the Real Bottleneck\n\nAfter a UserTesting study, someone has to watch 10+ video recordings (each 15-45 minutes), take timestamped notes, identify patterns, code themes, and create a report. Industry research shows this synthesis takes **2-3 hours per interview hour** — meaning a 10-session study requires 30+ hours of analysis.\n\nKoji eliminates this entirely. Every interview is automatically transcribed, [quality-scored](/docs/how-the-quality-gate-works), and analyzed. Themes are identified across all conversations. [Research reports](/docs/generating-research-reports) are generated with one click.\n\n### 4. Human Moderator Inconsistency\n\nEven skilled moderators have off days. They miss follow-up opportunities, inadvertently lead participants, or let their energy flag during the fifth session of the day. Each participant gets a slightly different experience.\n\nKoji's AI interviewer is consistent across every session — applying the same [methodology](/docs/choosing-a-methodology), following up with equal depth, and never getting tired. It follows [bias prevention guardrails](/docs/avoiding-bias-in-interviews) on every question.\n\n---\n\n## When UserTesting Is the Better Choice\n\nUserTesting wins when:\n\n- You need **usability testing with screen recording** — watching users click through prototypes or live products\n- You need **panel access** — recruiting from a large, pre-screened participant database\n- Your research requires **task-based observation** — seeing *how* users complete specific workflows\n- You need **enterprise compliance** — SOC 2 Type II, SSO, advanced permissions, SLA guarantees\n- You are doing **accessibility testing** — observing assistive technology usage in real-time\n- You have a **dedicated research team** with the budget and time for manual synthesis\n\n---\n\n## When to Choose Koji Instead\n\nChoose Koji when:\n\n- You need **interview-based research** — understanding motivations, pain points, and decision-making processes\n- You want to run research **continuously** — weekly or daily, not quarterly\n- You do not have a **dedicated research team** — Koji handles moderation and analysis\n- Your budget does not support **$15,000+/year** platforms\n- You need results **in hours, not weeks**\n- You want to [democratize research](/docs/mcp-workflow-product-managers) — let PMs, designers, and founders run their own studies\n- You need to conduct **50+ interviews** per study for broader qualitative coverage\n- You want research integrated into your AI workflow via [Claude MCP](/docs/mcp-setup-claude)\n\n---\n\n## The Emerging Model: Use Both\n\nThe most sophisticated research teams are adopting a hybrid approach:\n\n1. **Koji for discovery** — weekly AI interviews to surface what matters (fast, affordable, continuous)\n2. **UserTesting for validation** — targeted usability tests when you need to observe specific interactions (deep, visual, task-based)\n\nThis model replaces the old \"run a big quarterly study\" approach with continuous learning, reserving expensive moderated sessions for the highest-impact questions.\n\n---\n\n## Pricing Comparison\n\n| | UserTesting Essentials | UserTesting Advanced | UserTesting Ultimate | Koji |\n|---|---|---|---|---|\n| Annual cost | ~$15,000/yr | ~$30,000/yr | $50,000+/yr | Free tier + plans |\n| Per-session cost | $200-300 | Included (limited) | Included | Included in plan |\n| Sessions/interviews | ~50-75/year | More | Unlimited | Based on credits |\n| Analysis | Manual (you watch videos) | Manual + some AI features | Manual + QXscore | Fully automated |\n| Setup to first result | 3-7 days | 3-7 days | 3-7 days | Hours |\n| AI integration | ❌ | ❌ | ❌ | ✅ [Claude MCP](/docs/mcp-overview) |\n\n**For the price of one UserTesting study (10 sessions at $250 each = $2,500), you can run an entire quarter of continuous research on Koji.**\n\n---\n\n## Getting Started\n\n1. **[Create your account](/docs/creating-your-account)** — free tier to test with\n2. **[Set up your first study](/docs/creating-your-first-study)** — describe what you want to learn\n3. **[Choose a methodology](/docs/choosing-a-methodology)** — Mom Test, JTBD, or general discovery\n4. **[Share the interview link](/docs/sharing-your-interview-link)** — with your existing user base or imported participants\n5. **[Review analyzed insights](/docs/insights-dashboard)** — themes, sentiment, and quality scores generated automatically\n\n---\n\n## Next Steps\n\n- **[Quick Start Guide](/docs/quick-start-guide)** — First AI interview in 10 minutes\n- **[AI Interviews vs. Surveys](/docs/ai-interviews-vs-surveys)** — Why conversations beat forms\n- **[Koji vs. Typeform](/docs/koji-vs-typeform)** — Comparison with popular form builder\n- **[Koji vs. SurveyMonkey](/docs/koji-vs-surveymonkey)** — Comparison with leading survey tool\n- **[The Definitive Guide to User Interviews](/docs/user-interview-guide)** — Master qualitative methodology\n- **[MCP Workflow for Product Managers](/docs/mcp-workflow-product-managers)** — Automate research with Claude","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. UserTesting — AI Interviews vs. Enterprise Usability Platform | Koji","metaDescription":"Compare Koji and UserTesting for user research. See how AI-powered interviews deliver enterprise-quality insights at a fraction of UserTesting's cost — with faster setup, automated analysis, and continuous discovery capability.","keywords":["Koji vs UserTesting","UserTesting alternative","UserTesting alternative cheaper","AI interview vs usability testing","best UserTesting alternative","UserTesting vs Koji","affordable user research platform","replace UserTesting","user research tool comparison","cheaper than UserTesting"],"aiSummary":"Comparison of Koji (AI-powered interviews) vs UserTesting (enterprise usability testing platform). UserTesting offers screen recording, human moderation, and panel access starting at $15K/year. Koji provides AI-powered interviews with automatic analysis, voice support, and methodology guardrails at a fraction of the cost. Best for teams needing continuous qualitative research without enterprise budgets.","aiPrerequisites":["Familiarity with user research tools","Understanding of usability testing vs interviews"],"aiLearningOutcomes":["Understand key differences between AI interviews and usability testing","Compare pricing models and ROI","Know when to use UserTesting vs Koji","Evaluate hybrid research approaches"],"aiDifficulty":"beginner","aiEstimatedTime":"13 minutes"},{"type":"documentation","id":"3a18b33a-6759-4ed1-bd43-4e3315e8c5eb","slug":"koji-vs-dovetail","title":"Koji vs. Dovetail — End-to-End Research vs. Analysis-Only Repository","url":"https://www.koji.so/docs/koji-vs-dovetail","summary":"Comparison of Koji (end-to-end AI research platform) vs Dovetail (research repository and analysis tool). Koji conducts interviews, generates transcripts, identifies themes, and produces reports automatically. Dovetail organizes and helps analyze research conducted elsewhere. Koji is better for teams needing to conduct and analyze research in one place; Dovetail is better as a centralized research knowledge base.","content":"## The Short Answer\n\nDovetail is a powerful **research repository and analysis platform** — it helps you organize, tag, code, and share insights from research you have already done. But Dovetail does not conduct interviews. You still need to moderate sessions, record them, and upload transcripts before Dovetail's analysis tools can help.\n\nKoji is **end-to-end** — it conducts AI-powered interviews, transcribes them, identifies themes, generates insights, and produces shareable research reports. Koji replaces the need for a separate interview tool, transcription service, and analysis platform.\n\n---\n\n## The Fundamental Difference\n\n**Dovetail** is where research goes *after* it happens.\n**Koji** is where research *happens* — and the analysis comes built in.\n\nThink of it this way: Dovetail is a research library. Koji is a research team. One stores and organizes knowledge. The other generates it.\n\n---\n\n## Feature Comparison\n\n| Capability | Dovetail | Koji |\n|-----------|---------|------|\n| **Conducts interviews** | ❌ | ✅ [AI-powered voice and text](/docs/voice-interview-experience) |\n| **Transcription** | ✅ Upload recordings | ✅ Built-in (automatic) |\n| **Qualitative coding** | ✅ Manual tagging + AI-assisted | ✅ [Fully automated theming](/docs/understanding-themes-patterns) |\n| **Theme identification** | ✅ Manual + AI suggestions | ✅ Automatic across all interviews |\n| **Research repository** | ✅ Core feature — searchable insight database | ✅ [Insights dashboard](/docs/insights-dashboard) per study |\n| **Stakeholder reports** | ✅ Highlights, reels, canvas | ✅ [Auto-generated research reports](/docs/generating-research-reports) |\n| **Follow-up probing** | ❌ (analysis only) | ✅ AI probes deeper in real-time |\n| **Methodology guardrails** | ❌ | ✅ [Mom Test, JTBD, Discovery](/docs/choosing-a-methodology) |\n| **Video highlights** | ✅ Clip and tag video moments | ❌ (transcript-based) |\n| **Survey integration** | ✅ Import from Typeform, SurveyMonkey | ❌ (replaces surveys with interviews) |\n| **Figma integration** | ✅ | ❌ |\n| **API** | ✅ | ✅ [Full REST API + embed](/docs/api-authentication) |\n| **AI assistant integration** | ✅ MCP server | ✅ [Claude MCP integration](/docs/mcp-overview) |\n| **Pricing model** | Per-seat ($29-79/user/month) | Credit-based (not per-seat) |\n\n---\n\n## Why Teams Consider Koji Over Dovetail\n\n### 1. Dovetail Requires Research to Already Exist\n\nDovetail's value proposition starts *after* you have conducted interviews, usability tests, or surveys. You still need:\n- A separate tool to **conduct** interviews (Zoom, UserTesting, or manual scheduling)\n- A **transcription service** (Otter.ai, Rev, etc.) or Dovetail's own transcription\n- **Hours of manual moderation** — sitting in every interview, taking notes\n- Time to **upload, organize, and process** everything in Dovetail\n\nWith Koji, you describe your [research goal](/docs/writing-a-research-question), the [AI consultant generates the plan](/docs/understanding-the-ai-consultant), you share a link, and interviews happen asynchronously. No scheduling. No moderation. No transcription uploads. Analysis is automatic.\n\n### 2. Per-Seat Pricing Limits Collaboration\n\nDovetail charges **$29-79 per user per month**. For a team of 10 people who should have access to research insights (2 researchers, 3 PMs, 2 designers, 2 engineers, 1 executive), that is **$290-790/month** just for access.\n\nThis creates a perverse incentive: the people who most need research insights (PMs, designers, engineers) get locked out because adding seats is expensive. Koji's credit-based pricing means insights are accessible to everyone on your team.\n\n### 3. Manual Coding Is Still Manual\n\nDovetail has added AI-assisted features like Magic Suggest and Channels, but the core workflow remains largely manual — you read transcripts, highlight passages, create tags, assign codes, and build themes. For a 10-interview study, this still takes **15-30 hours** of analyst time.\n\nKoji's analysis is fully automated. [Themes and patterns](/docs/understanding-themes-patterns) are identified across all interviews without manual coding. [Quality scores](/docs/how-the-quality-gate-works) filter low-effort responses. [Research reports](/docs/generating-research-reports) are generated with one click.\n\n### 4. Repository Bloat Without Action\n\nDovetail's strength as a repository can become a weakness. Teams accumulate thousands of tagged insights, highlights, and notes — but struggle to connect them to product decisions. A 2024 industry survey found that **42% of research teams** struggle to translate research findings into business outcomes.\n\nKoji's per-study model keeps insights actionable. Each study produces a focused [research report](/docs/publishing-sharing-reports) with clear themes and recommendations — designed to be [presented to stakeholders](/docs/presenting-research-findings) and acted on immediately.\n\n---\n\n## When Dovetail Is the Better Choice\n\nDovetail wins when:\n\n- You have **existing research data** (videos, transcripts, survey results) that needs to be organized and analyzed\n- You need a **centralized research repository** that the entire organization can search\n- You conduct **moderated usability tests** and need video highlight reels\n- You need to **consolidate research across multiple tools** (UserTesting + surveys + interview transcripts)\n- You want **Figma integration** for embedding insights alongside designs\n- Your team has **dedicated researchers** who prefer manual coding and tagging\n- You need to **analyze research you did not conduct** — customer support calls, sales recordings, etc.\n\n---\n\n## When to Choose Koji\n\nChoose Koji when:\n\n- You need to **conduct AND analyze** interviews — not just analyze existing data\n- You do not have a dedicated researcher to moderate every session\n- You want research to happen **asynchronously** — no scheduling, no time zones\n- You need results in **hours, not weeks** of manual coding\n- Your team needs access to insights **without per-seat costs**\n- You want to run [continuous discovery](/docs/continuous-discovery-with-mcp) — weekly interviews without weekly moderation\n- You want AI to handle the heavy lifting: moderation, transcription, coding, theming, and reporting\n\n---\n\n## The Complementary Approach\n\nSome teams use both tools together:\n\n1. **Koji** conducts AI-powered interviews and generates per-study insights\n2. **Dovetail** serves as the long-term research repository where Koji insights are archived alongside usability tests, survey data, and support call analysis\n\nThis gives you the speed and automation of Koji for new research, plus Dovetail's repository for historical knowledge management.\n\n---\n\n## Pricing Comparison\n\n| | Dovetail Free | Dovetail Team | Dovetail Business | Koji |\n|---|---|---|---|---|\n| Cost | Free (limited) | $29/user/month | $79/user/month | Free tier + plans |\n| 5-person team cost | Free | $145/month | $395/month | Plan-based (flat) |\n| 10-person team cost | N/A | $290/month | $790/month | Plan-based (flat) |\n| Conducts interviews | ❌ | ❌ | ❌ | ✅ |\n| Analysis | Manual + AI-assisted | Manual + AI-assisted | Manual + AI-assisted | Fully automated |\n| Research reports | Manual creation | Manual creation | Manual creation | [Auto-generated](/docs/generating-research-reports) |\n| Additional tools needed | Interview tool + transcription | Interview tool + transcription | Interview tool + transcription | None — end-to-end |\n\n**Total cost of research with Dovetail:** Dovetail subscription + interview tool (Zoom Pro ~$20/mo) + transcription service (~$0.25/min) + researcher time (hours of moderation and manual coding).\n\n**Total cost of research with Koji:** Koji subscription. That is it.\n\n---\n\n## Getting Started\n\n1. **[Create your account](/docs/creating-your-account)** — start with the free tier\n2. **[Create your first study](/docs/creating-your-first-study)** — describe your research goal\n3. **[Share interview link](/docs/sharing-your-interview-link)** — send to your participants\n4. **[Review automated insights](/docs/insights-dashboard)** — themes, patterns, and quality scores\n5. **[Share the research report](/docs/publishing-sharing-reports)** — one-click stakeholder reports\n\n---\n\n## Next Steps\n\n- **[Quick Start Guide](/docs/quick-start-guide)** — Your first AI interview in 10 minutes\n- **[Koji vs. UserTesting](/docs/koji-vs-usertesting)** — Compare with the enterprise research platform\n- **[Koji vs. Typeform](/docs/koji-vs-typeform)** — Compare with the popular form builder\n- **[Turning Interviews Into Insights](/docs/turning-interviews-into-insights)** — How Koji analyzes research automatically\n- **[Thematic Analysis Guide](/docs/thematic-analysis-guide)** — Understanding qualitative coding and theming","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Dovetail — End-to-End AI Research vs. Analysis Repository | Koji","metaDescription":"Compare Koji and Dovetail for research workflows. Koji conducts AI-powered interviews AND analyzes results automatically. Dovetail organizes and codes research you've already done. See which approach fits your team.","keywords":["Koji vs Dovetail","Dovetail alternative","best Dovetail alternative","Dovetail vs Koji","research repository alternative","qualitative analysis tool comparison","replace Dovetail","end-to-end research platform","automated qualitative analysis","AI research platform vs Dovetail"],"aiSummary":"Comparison of Koji (end-to-end AI research platform) vs Dovetail (research repository and analysis tool). Koji conducts interviews, generates transcripts, identifies themes, and produces reports automatically. Dovetail organizes and helps analyze research conducted elsewhere. Koji is better for teams needing to conduct and analyze research in one place; Dovetail is better as a centralized research knowledge base.","aiPrerequisites":["Familiarity with qualitative research workflows","Understanding of research analysis tools"],"aiLearningOutcomes":["Understand the difference between research repositories and end-to-end research platforms","Compare per-seat vs credit-based pricing models","Know when Dovetail vs Koji is the right fit","Evaluate complementary usage of both tools"],"aiDifficulty":"beginner","aiEstimatedTime":"13 minutes"},{"type":"documentation","id":"d984dbd4-8b26-4a30-a1eb-cf375388ad67","slug":"koji-vs-dscout","title":"Koji vs. dscout: AI Voice Interviews vs. Diary Studies","url":"https://www.koji.so/docs/koji-vs-dscout","summary":"Koji and dscout serve complementary research needs. dscout excels at longitudinal diary studies and in-context behavioral observation through mobile missions. Koji excels at scalable AI voice interviews that capture motivational depth through conversation. Koji offers 3-5x larger samples, 70% faster insights, and lower cost, while dscout provides unique longitudinal and observational capabilities.","content":"## The Bottom Line\n\ndscout excels at longitudinal, in-context research through diary studies and mobile missions. Koji excels at deep, scalable voice interviews with AI moderation. If you need to observe behavior over time in a participant's natural environment, dscout is strong. If you need to understand motivations, decisions, and experiences through conversation at scale, Koji delivers more depth per dollar and faster time to insight.\n\n## Platform Overview\n\n### dscout\ndscout is a qualitative research platform built around \"missions\" — structured tasks that participants complete in their natural environment using their mobile devices. It is particularly strong for diary studies, in-context research, and longitudinal observation. Participants record video, photo, and text entries as they go about their daily lives, providing rich observational data.\n\n**Best for**: Diary studies, ethnographic-style research, in-context behavior observation, longitudinal studies\n\n### Koji\nKoji is an AI-powered voice interview platform that conducts deep, structured conversations with participants at scale. The AI interviewer follows your discussion guide, asks intelligent follow-ups, and captures emotional nuance through voice — enabling qualitative depth that traditionally required expensive human moderators.\n\n**Best for**: Customer discovery, concept testing, churn analysis, competitive intelligence, feature prioritization, any research where conversation reveals more than observation\n\n## Head-to-Head Comparison\n\n| Dimension | dscout | Koji |\n|-----------|--------|------|\n| **Core method** | Diary studies, mobile missions | AI voice interviews |\n| **Data type** | Video, photo, text entries | Voice conversations, transcripts |\n| **Depth per participant** | Observational + self-report | Conversational + emotional |\n| **Sample size typical** | 20-75 participants | 50-500+ participants |\n| **Study duration** | Days to weeks | Hours to days |\n| **Participant effort** | Multiple entries over time | Single 10-20 min conversation |\n| **Time to insights** | 2-4 weeks | 1-5 days |\n| **Moderator bias** | N/A (self-directed tasks) | Eliminated (AI moderated) |\n| **Follow-up probing** | Limited (async text) | Real-time (AI adapts) |\n| **Pricing** | Enterprise pricing ($30K+/year) | Flexible, usage-based |\n| **Best for** | Behavioral observation | Motivational understanding |\n\n## Where dscout Wins\n\n### Longitudinal Behavior Tracking\ndscout's diary study format captures behavior changes over days or weeks. If you need to understand how someone's morning routine evolves after adopting a new product, or how medication adherence varies over a month, dscout's mission structure is purpose-built for this.\n\n### In-Context Data Capture\nParticipants record entries in their actual environment — at the store shelf, in the car, at their desk. This contextual data is invaluable for understanding how products and experiences fit into real life, not recalled life.\n\n### Visual Evidence\nPhoto and video entries create rich visual datasets that interviews cannot replicate. Seeing someone's actual kitchen setup, workspace configuration, or shopping behavior adds dimensions that verbal descriptions miss.\n\n### Participant Self-Documentation\nSome research questions are best answered by having people document their own experiences over time rather than reflecting on them in a single conversation. dscout makes this documentation structured and analyzable.\n\n## Where Koji Wins\n\n### Conversational Depth\nVoice interviews go places that diary entries cannot. When a participant mentions frustration, Koji's AI asks \"Tell me more about that moment — what were you thinking?\" This real-time probing reveals layers of motivation, emotion, and reasoning that self-directed entries miss.\n\n### Scale Without Compromise\ndscout studies typically involve 20-75 participants due to the effort required to analyze rich multimedia entries. Koji runs 100-500+ interviews with AI-powered synthesis, giving you the statistical confidence to make segment-level claims.\n\n### Speed to Insight\nA dscout diary study runs 1-3 weeks of data collection plus 1-2 weeks of analysis. A Koji study collects interviews in 2-5 days and synthesizes in hours. When decisions cannot wait, speed matters.\n\n### Decision-Oriented Research\nFor questions like \"Why did customers churn?\", \"Which feature should we build next?\", or \"How do buyers evaluate our category?\", conversation is the natural format. These are reflection and opinion questions, not observational ones.\n\n### Cost Efficiency\ndscout's enterprise pricing starts at $30,000+ annually before participant incentives. Koji's pricing is more accessible for teams that need regular qualitative research without an enterprise budget commitment.\n\n### Lower Participant Burden\nA 15-minute Koji interview requires one session of participant time. A dscout mission might require 5-15 entries over multiple days. Lower burden means higher completion rates and access to busier participant populations (executives, professionals, parents).\n\n## Research Method Comparison\n\n### Customer Discovery\n- **dscout**: Have participants document their daily workflow for a week, capturing pain points as they happen\n- **Koji**: Interview 75 participants about their workflow, pain points, and ideal solutions in 15-minute conversations\n- **Verdict**: Koji for speed and scale; dscout if observing actual behavior is more valuable than discussing it\n\n### Product Usability\n- **dscout**: Participants complete tasks with your product, recording their screen and commentary over several sessions\n- **Koji**: Interview participants about their experience using specific features, probing satisfaction and frustration points\n- **Verdict**: dscout for observational usability data; Koji for understanding the why behind usability issues\n\n### Brand Perception\n- **dscout**: Participants document brand encounters in their daily life — ads, store displays, social media\n- **Koji**: Interview participants about their brand associations, competitive perceptions, and emotional connections\n- **Verdict**: Koji for depth of perception understanding; dscout for mapping real-world brand touchpoints\n\n### Habit Formation\n- **dscout**: Track how habits develop or change over 2-4 weeks with daily entries\n- **Koji**: Interview participants at two time points about behavior changes and their drivers\n- **Verdict**: dscout clearly wins for longitudinal habit research\n\n### Purchase Decision Research\n- **dscout**: Participants document their shopping/evaluation journey in real-time\n- **Koji**: Interview 100+ participants about their recent purchase decisions, evaluation criteria, and decision moments\n- **Verdict**: Koji for understanding decision drivers at scale; dscout for real-time journey observation\n\n## When to Use Both Together\n\nThe most powerful research programs combine both approaches:\n\n1. **dscout for behavioral mapping**: Run a diary study to understand what people actually do\n2. **Koji for motivational understanding**: Follow up with AI interviews to understand why they do it\n3. **Combined insight**: Behavior + motivation = complete understanding\n\n### Example: New Product Launch Research\n- **Week 1-2**: dscout mission — participants use the new product for 10 days, documenting their experience\n- **Week 3**: Koji interviews — AI interviews the same participants about their overall assessment, comparison to previous solutions, and continued usage intent\n- **Combined insight**: You see how people actually used the product AND understand their reflective assessment\n\n## Switching from dscout to Koji\n\n### What You Gain\n- 3-5x larger sample sizes at comparable or lower cost\n- 70% faster time to insights\n- AI-powered synthesis that scales with sample size\n- Conversational depth that diary entries cannot match\n- Lower participant burden means access to harder-to-reach audiences\n\n### What You Trade Off\n- Longitudinal behavioral observation (dscout's strength)\n- Visual and video evidence from natural contexts\n- In-the-moment data capture during activities\n- Multi-day study formats\n\n### Migration Path\n1. **Start parallel**: Run your next study with both platforms and compare insights\n2. **Identify method fit**: Which studies benefit most from observation vs. conversation?\n3. **Allocate by method**: Use dscout for longitudinal/observational, Koji for everything else\n4. **Evaluate**: After 2-3 months, assess which platform delivered more actionable insights per dollar\n\n## Frequently Asked Questions\n\n### Can Koji do diary studies?\nNot in the traditional sense. Koji excels at point-in-time conversations rather than longitudinal documentation. However, you can run multiple Koji interview waves with the same participants over time to create a conversational longitudinal study.\n\n### Is dscout better for ethnographic research?\nFor remote ethnography that relies on participant self-documentation, yes. dscout's mobile-first design is purpose-built for in-context data capture. For the interview component of ethnographic research, Koji provides deeper conversational data.\n\n### Which platform has better analysis tools?\ndscout provides multimedia analysis tools for coding video and photo entries. Koji provides AI-powered synthesis that automatically identifies themes, sentiment, and patterns across hundreds of voice interviews. For large-scale qualitative analysis, Koji's AI synthesis is more scalable.\n\n### Can I use dscout participants with Koji?\nIf you have relationships with dscout participants, you can invite them to Koji studies separately. The platforms do not integrate directly, but participant panels are portable.\n\n### Which is better for academic research?\nIt depends on your methodology. dscout aligns well with experience sampling and diary study methodologies common in psychology and HCI research. Koji aligns with semi-structured interview methodologies. Both produce data suitable for qualitative analysis frameworks.\n\n---\n\n## Related Comparisons\n\n- [Best User Research Tools](/docs/best-user-research-tools-2026) — Full tool landscape\n- [Koji vs. Lookback](/docs/koji-vs-lookback) — AI vs live research sessions\n- [Koji vs. UserTesting](/docs/koji-vs-usertesting) — Research methodologies compared\n- [Koji vs. Dovetail](/docs/koji-vs-dovetail) — Collection vs analysis\n- [Diary Study Guide](/docs/diary-study-guide) — Longitudinal research alternative\n\n*See how [structured questions](/docs/structured-questions-guide) combine diary-style depth with scalable AI moderation.*","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. dscout: AI Voice Interviews vs. Diary Studies | 2026 Comparison","metaDescription":"Compare Koji and dscout for qualitative research. See how AI voice interviews compare to diary studies for depth, scale, speed, and cost across different research methods.","keywords":["Koji vs dscout","dscout alternative","diary study platform","qualitative research tools","user research platform","dscout comparison","research platform comparison","mobile research","in-context research","AI interviews vs diary studies","voice interviews","ethnographic research"],"aiSummary":"Koji and dscout serve complementary research needs. dscout excels at longitudinal diary studies and in-context behavioral observation through mobile missions. Koji excels at scalable AI voice interviews that capture motivational depth through conversation. Koji offers 3-5x larger samples, 70% faster insights, and lower cost, while dscout provides unique longitudinal and observational capabilities.","aiPrerequisites":["Basic understanding of qualitative research methods","Research tool evaluation context"],"aiLearningOutcomes":["Compare AI voice interviews with diary study methodology","Choose the right platform for different research questions","Design combined research programs using both approaches","Evaluate cost-effectiveness across research methods"],"aiDifficulty":"beginner","aiEstimatedTime":"11 minutes"},{"type":"documentation","id":"45c55ac9-c6f5-4fbf-b438-263df7f4dcce","slug":"koji-vs-qualtrics","title":"Koji vs. Qualtrics — AI-Native Simplicity vs. Enterprise Complexity","url":"https://www.koji.so/docs/koji-vs-qualtrics","summary":"Comparison of Koji (AI-native qualitative research platform) vs Qualtrics (enterprise experience management suite). Qualtrics offers 100+ question types, statistical modeling, and multi-channel experience management starting at $30,000+/year. Koji provides AI-powered interviews with automatic analysis, voice support, and methodology guardrails at a fraction of the cost. Best for teams that need qualitative depth without enterprise complexity.","content":"## The Short Answer\n\nQualtrics is the enterprise standard for experience management — surveys, analytics, dashboards, and organizational insights at massive scale. It is powerful, comprehensive, and **starts at $30,000+ per year** with a learning curve measured in weeks, not minutes.\n\nKoji does one thing exceptionally well: **AI-powered qualitative interviews** that conduct, analyze, and report on customer conversations automatically. If your goal is understanding *why* customers behave the way they do — not building an enterprise survey infrastructure — Koji gets you there 10x faster at a fraction of the cost.\n\n---\n\n## Two Very Different Tools\n\n### Qualtrics Is:\n- An **enterprise experience management platform** covering customer experience (CX), employee experience (EX), brand experience (BX), and product experience (PX)\n- A **survey powerhouse** with advanced logic, conjoint analysis, MaxDiff, and 100+ question types\n- A **data analytics engine** with statistical modeling, regression analysis, and predictive intelligence\n- A **$30,000-$200,000+/year investment** requiring dedicated administrators and weeks of training\n\n### Koji Is:\n- An **AI-native qualitative research platform** focused on depth interviews\n- A **conversation engine** that adapts to each respondent with [methodology-guided probing](/docs/choosing-a-methodology)\n- An **insight automation tool** that generates [themes](/docs/understanding-themes-patterns), [reports](/docs/generating-research-reports), and [quality scores](/docs/how-the-quality-gate-works) automatically\n- **Free to start**, learnable in 10 minutes, with no administrator required\n\n---\n\n## Feature Comparison\n\n| Capability | Qualtrics | Koji |\n|-----------|----------|------|\n| **Primary strength** | Enterprise survey infrastructure | AI-powered qualitative interviews |\n| **Question types** | 100+ (scales, matrix, conjoint, MaxDiff) | Open-ended AI conversation |\n| **Response depth** | Structured data, short text | Deep conversation (150-500 words/response) |\n| **Follow-up probing** | ❌ Fixed branching | ✅ Automatic AI follow-ups |\n| **Voice interviews** | ❌ | ✅ [Natural voice conversations](/docs/voice-interview-experience) |\n| **Analysis** | Statistical dashboards, cross-tabs, regression | [AI-generated themes, sentiment, insights](/docs/ai-generated-insights) |\n| **Research methodology** | None built-in (you design everything) | [Mom Test, JTBD, Discovery](/docs/choosing-a-methodology) built in |\n| **Setup time** | Days to weeks | [5-10 minutes](/docs/creating-your-first-study) |\n| **Learning curve** | Weeks (training required) | Minutes |\n| **Reports** | Custom dashboards (manual setup) | [Auto-generated shareable reports](/docs/publishing-sharing-reports) |\n| **API** | ✅ Enterprise API | ✅ [Full REST API + embed](/docs/api-authentication) |\n| **AI integration** | Qualtrics Assist (add-on) | [Claude MCP built-in](/docs/mcp-overview) |\n| **Pricing** | $30,000-$200,000+/year | Free tier + affordable plans |\n| **Contract** | Annual enterprise agreement | Monthly, no lock-in |\n\n---\n\n## Why Teams Choose Koji Over Qualtrics\n\n### 1. The $30,000 Minimum Is a Non-Starter for Most Teams\n\nQualtrics pricing begins at approximately **$30,000/year** for a basic license. Feature-rich plans run **$100,000-$200,000+/year**. Add professional services for survey design and you are looking at a six-figure annual commitment before collecting a single response.\n\nFor startups, scale-ups, and lean teams, this is simply not viable. Koji offers a **free tier** to start and affordable paid plans — making qualitative research accessible to teams of every size.\n\n### 2. Qualtrics Was Built for Surveys, Not Conversations\n\nQualtrics has 100+ question types, advanced piping, and statistical modeling — all designed for structured, quantitative data collection. It is excellent at answering *\"how many\"* and *\"how much.\"*\n\nBut when you need to understand *why* — why customers churn, why a feature is not adopted, why users choose a competitor — structured surveys fail. You cannot pre-define the answer options for questions you have not thought to ask yet.\n\nKoji's AI interviewer conducts **adaptive conversations** that follow unexpected threads. When a participant mentions something surprising, the AI probes deeper — just like a skilled researcher would. This surfaces insights that no pre-built survey could capture.\n\n### 3. Weeks of Setup vs. Minutes\n\nBuilding a Qualtrics study requires:\n- Understanding the survey builder interface (training required)\n- Designing question flows with skip logic and piping\n- Setting up distribution channels\n- Configuring dashboards for analysis\n- Often involving a Qualtrics administrator or professional services\n\nBuilding a Koji study requires:\n- [Describing your research goal](/docs/writing-a-research-question) in plain language\n- Reviewing the [AI-generated interview plan](/docs/understanding-the-ai-consultant)\n- [Sharing the link](/docs/sharing-your-interview-link)\n\nTotal setup time: **5-10 minutes**. No training. No administrator.\n\n### 4. Analysis Paralysis vs. Automatic Insights\n\nQualtrics provides powerful analytical tools — but someone has to use them. Cross-tabulations, regression models, and custom dashboards require **analytical expertise** and **hours of configuration**. Many teams pay for Qualtrics and only use 10-20% of its capabilities.\n\nKoji analyzes every interview automatically:\n- [Themes and patterns](/docs/understanding-themes-patterns) identified across all conversations\n- [Quality scores](/docs/how-the-quality-gate-works) filter low-quality responses\n- [Sentiment analysis](/docs/ai-generated-insights) per topic\n- [Shareable research reports](/docs/generating-research-reports) generated with one click\n\nNo analytical expertise required. No dashboard configuration. Results are ready as soon as interviews complete.\n\n---\n\n## Real-World Scenario: Understanding Customer Churn\n\n### Qualtrics Approach\n```\nSurvey design: 2-3 days (build in Qualtrics)\nDistribution: Email blast to churned customers\nResponse rate: 3-8% (survey fatigue)\nAnalysis: 1-2 days (cross-tabs, dashboards)\nTotal time: 5-7 days\nResult: \"47% cite pricing, 28% cite features, 25% cite other\"\nAction: Unclear — which pricing concern? Which features?\n```\n\n### Koji Approach\n```\nStudy setup: 10 minutes (describe goal, AI generates plan)\nDistribution: Share link via email or embed\nCompletion rate: 60-80%\nAnalysis: Automatic\nTotal time: 24-48 hours\nResult: \"Customers who churned after 3 months consistently \ncite the inability to share reports with external \nstakeholders. They found workarounds (PDF screenshots) \nfor 2-3 months before giving up. 4 of 12 switched \nto Dovetail specifically for its sharing features.\"\nAction: Build external report sharing. Clear, specific, actionable.\n```\n\n---\n\n## When Qualtrics Is the Better Choice\n\nQualtrics wins when:\n\n- You need **enterprise-scale survey infrastructure** — 100,000+ responses across multiple programs\n- You require **advanced statistical analysis** — conjoint, MaxDiff, regression, predictive modeling\n- You are running **multi-channel experience management** — CX + EX + BX in one platform\n- You need **enterprise compliance** — FedRAMP, HIPAA, SOC 2, and government certifications\n- Your organization has **dedicated survey administrators** who know the platform\n- You need **longitudinal tracking** — the same survey running quarterly for years with trend analysis\n- You require **advanced panel management** — quotas, randomization, complex sampling\n\n---\n\n## When to Choose Koji\n\nChoose Koji when:\n\n- You need **qualitative depth** — understanding the *why*, not just counting responses\n- You want to **start researching today** — not after weeks of setup and training\n- Your budget does not support **$30,000+/year** platforms\n- You do not have a dedicated research team or survey administrator\n- You want **continuous customer conversations** — not quarterly survey blasts\n- You need [automated analysis](/docs/ai-generated-insights) — not manual dashboard configuration\n- You want [voice interviews](/docs/voice-interview-experience) — people share more when they talk\n- You need research integrated into your AI workflow via [Claude MCP](/docs/mcp-setup-claude)\n\n---\n\n## The Modern Research Stack\n\nThe best teams are moving away from \"one platform does everything\" and toward purpose-built tools:\n\n| Need | Tool |\n|------|------|\n| **Qualitative understanding** (why) | **Koji** — AI interviews with automatic analysis |\n| **Quantitative measurement** (how many) | Qualtrics, SurveyMonkey, or Typeform |\n| **Usability observation** (how they interact) | UserTesting, Maze, or Lookback |\n| **Research repository** (historical knowledge) | Dovetail or Notion |\n\nKoji replaces the qualitative research gap that survey tools cannot fill — and does it without the six-figure price tag.\n\n---\n\n## Pricing Reality Check\n\n| | Qualtrics CoreXM | Qualtrics DesignXM | Qualtrics Full Suite | Koji |\n|---|---|---|---|---|\n| Starting price | ~$30,000/yr | ~$60,000/yr | ~$150,000+/yr | Free tier |\n| Per-user cost | Per-seat add-ons | Per-seat add-ons | Per-seat add-ons | Not per-seat |\n| Contract length | Annual minimum | Annual minimum | Multi-year | Monthly |\n| Training required | Weeks | Weeks | Months | Minutes |\n| Time to first insight | Days to weeks | Days to weeks | Weeks | Hours |\n| Hidden costs | Professional services, training | Professional services, training | Implementation team | None |\n\n**For the price of one year of Qualtrics CoreXM, you could run research on Koji for years** — while getting deeper qualitative insights that surveys fundamentally cannot provide.\n\n---\n\n## Getting Started\n\n1. **[Create your account](/docs/creating-your-account)** — free, no credit card required\n2. **[Quick Start Guide](/docs/quick-start-guide)** — first AI interview in 10 minutes\n3. **[Choose a methodology](/docs/choosing-a-methodology)** — Mom Test, JTBD, or general discovery\n4. **[Share your interview link](/docs/sharing-your-interview-link)** — with existing customers or imported participants\n5. **[Review insights automatically](/docs/insights-dashboard)** — no dashboard setup required\n\n---\n\n## Next Steps\n\n- **[AI Interviews vs. Surveys](/docs/ai-interviews-vs-surveys)** — Why conversations beat forms\n- **[Koji vs. SurveyMonkey](/docs/koji-vs-surveymonkey)** — Compare with the popular survey tool\n- **[Koji vs. Dovetail](/docs/koji-vs-dovetail)** — Compare with the research repository\n- **[Koji vs. UserTesting](/docs/koji-vs-usertesting)** — Compare with the enterprise usability platform\n- **[MCP Workflow for Researchers](/docs/mcp-workflow-researchers)** — Automate research with Claude","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Qualtrics — AI-Native Research vs. Enterprise Survey Suite | Koji","metaDescription":"Compare Koji and Qualtrics for customer research. See why AI-powered interviews deliver deeper qualitative insights than enterprise survey suites — at a fraction of the cost, complexity, and setup time.","keywords":["Koji vs Qualtrics","Qualtrics alternative","Qualtrics alternative cheaper","Qualtrics alternative for qualitative research","best Qualtrics alternative","Qualtrics vs Koji","affordable Qualtrics alternative","replace Qualtrics","qualitative research platform vs Qualtrics","enterprise survey tool alternative"],"aiSummary":"Comparison of Koji (AI-native qualitative research platform) vs Qualtrics (enterprise experience management suite). Qualtrics offers 100+ question types, statistical modeling, and multi-channel experience management starting at $30,000+/year. Koji provides AI-powered interviews with automatic analysis, voice support, and methodology guardrails at a fraction of the cost. Best for teams that need qualitative depth without enterprise complexity.","aiPrerequisites":["Familiarity with survey platforms","Understanding of qualitative vs quantitative research needs"],"aiLearningOutcomes":["Understand the difference between enterprise survey suites and AI interview platforms","Compare pricing, complexity, and time-to-insight","Know when Qualtrics vs Koji is the right choice","Evaluate a modern research stack approach"],"aiDifficulty":"beginner","aiEstimatedTime":"14 minutes"},{"type":"documentation","id":"525a951b-3730-4206-8d5d-7c36d3e519b5","slug":"koji-vs-user-interviews","title":"Koji vs. User Interviews: AI Moderation vs. Recruitment Platform","url":"https://www.koji.so/docs/koji-vs-user-interviews","summary":"User Interviews is a participant recruitment platform connecting researchers with 3M+ verified participants. Koji is an end-to-end AI research platform handling recruitment, moderation, transcription, and synthesis. Koji reduces total researcher time by 80% and delivers insights in days vs. weeks, while User Interviews offers a larger participant panel and method flexibility.","content":"## The Bottom Line\n\nUser Interviews is a participant recruitment platform — it helps you find and schedule research participants. Koji is an end-to-end research platform that recruits, moderates via AI, transcribes, and synthesizes. If your bottleneck is finding participants and you have moderators ready, User Interviews is useful. If your bottleneck is the entire research process — from recruitment through insight delivery — Koji handles it all.\n\n## Platform Overview\n\n### User Interviews\nUser Interviews is a participant recruitment and scheduling platform. It connects researchers with a panel of 3M+ verified participants across demographics, industries, and roles. You post a study, define screening criteria, participants apply, and User Interviews handles scheduling. The actual research — moderation, transcription, analysis — is your responsibility.\n\n**Best for**: Finding and scheduling research participants when you have your own moderation and analysis workflow\n\n### Koji\nKoji is an AI-powered research platform that covers the full research lifecycle. It recruits participants, conducts AI-moderated voice interviews following your discussion guide, transcribes every conversation, and synthesizes findings with AI-powered theme analysis. You design the study and interpret the results — Koji handles everything in between.\n\n**Best for**: Teams that need complete research execution, not just participant sourcing\n\n## Head-to-Head Comparison\n\n| Dimension | User Interviews | Koji |\n|-----------|----------------|------|\n| **Core offering** | Participant recruitment + scheduling | End-to-end AI research platform |\n| **Participant panel** | 3M+ verified participants | Built-in recruitment + import your own |\n| **Interview moderation** | Not included (you moderate) | AI-moderated voice interviews |\n| **Transcription** | Not included | Automatic |\n| **Analysis/synthesis** | Not included | AI-powered theme analysis |\n| **Scheduling** | Automated calendar coordination | No scheduling needed (async) |\n| **Time per study** | Recruitment: 2-5 days; research: you decide | End-to-end: 3-7 days |\n| **Cost model** | Per-participant recruitment fee | Platform subscription + usage |\n| **Researcher time required** | High (you do everything after recruitment) | Low (you design and interpret) |\n\n## Where User Interviews Wins\n\n### Massive Participant Panel\nWith 3M+ verified participants, User Interviews has one of the largest research panels available. For hard-to-find demographics or niche professional roles, their panel depth is a genuine advantage.\n\n### Method Agnostic\nUser Interviews provides participants — what you do with them is up to you. Run usability tests, card sorts, focus groups, 1:1 interviews, co-design sessions, or any other method. Koji is focused on voice interviews specifically.\n\n### Established Scheduling Workflow\nIf your team already has moderators, tools, and analysis workflows, User Interviews slots into your existing process. It solves the recruitment problem without changing anything else about how you do research.\n\n### Screener Sophistication\nUser Interviews offers detailed screening surveys to ensure participants match specific criteria. Their panel includes verified professional information that helps filter for B2B research participants.\n\n## Where Koji Wins\n\n### End-to-End Efficiency\nUser Interviews solves recruitment. Koji solves research. After finding participants through User Interviews, you still need to moderate interviews (5-10 hours per study), transcribe (2-3 hours), code and analyze (8-15 hours), and synthesize (4-8 hours). Koji handles all of this automatically.\n\n**Total researcher time comparison for a 50-participant study:**\n- **User Interviews + manual research**: 25-40 hours\n- **Koji**: 4-6 hours (study design + result interpretation)\n\n### No Scheduling Required\nUser Interviews coordinates calendars between you and participants. Koji eliminates scheduling entirely — participants complete AI interviews asynchronously at their convenience. No calendar Tetris, no no-shows, no timezone juggling.\n\n### Scale Without Proportional Effort\nWith User Interviews, moderating 50 interviews takes 5x the effort of moderating 10. With Koji, moderating 500 interviews takes the same effort as moderating 50 — zero, because the AI handles it. Your effort scales with study design complexity, not sample size.\n\n### Consistent Interview Quality\nHuman moderators vary in skill, energy, and bias across interviews. Koji's AI interviewer maintains perfect consistency — every participant gets the same core questions with the same probing depth, eliminating moderator-introduced variability.\n\n### Faster Time to Insight\nUser Interviews delivers participants in 2-5 days. Then you need 2-4 weeks to conduct and analyze interviews. Koji delivers participants AND synthesized insights in 3-7 days total.\n\n### Built-In Analysis\nUser Interviews hands you participants. You return with recordings that need transcription, coding, and analysis. Koji hands you synthesized themes, sentiment analysis, key quotes, and segment breakdowns — ready for stakeholder presentation.\n\n## The Real Comparison: Total Cost of Research\n\n### User Interviews Approach (50-participant study)\n- Recruitment fees: $1,500-3,000\n- Participant incentives: $2,500-5,000 (varies by audience)\n- Moderator time (50 hrs @ $75/hr): $3,750\n- Transcription service: $500-1,000\n- Analysis time (20 hrs @ $75/hr): $1,500\n- **Total: $9,750-14,250**\n- **Timeline: 3-5 weeks**\n\n### Koji Approach (50-participant study)\n- Koji platform: subscription-based\n- Participant incentives: comparable\n- Study design time (3 hrs): $225\n- Result interpretation (3 hrs): $225\n- **Total: significantly lower**\n- **Timeline: 3-7 days**\n\nThe difference becomes more dramatic at larger sample sizes. A 200-participant study with User Interviews requires 200 hours of moderation. With Koji, it requires the same 6 hours of your time.\n\n## When to Choose What\n\n### Choose User Interviews When:\n- You need participants for non-interview research (usability testing, card sorting, focus groups)\n- You have trained moderators who need to lead conversations personally\n- Your research method requires real-time human judgment during sessions\n- You need extremely specific participant profiles from a large panel\n- You want to use participants across multiple different research methods\n\n### Choose Koji When:\n- You need interview-based research with depth AND scale\n- You lack dedicated moderators or your moderators are at capacity\n- Speed to insight is critical\n- You want consistent, unbiased interview moderation\n- You need AI-powered synthesis across large interview datasets\n- Your team needs to do more research with fewer resources\n\n### Use Both When:\n- You need User Interviews' panel for hard-to-find participants AND Koji's AI moderation for the actual interviews\n- You recruit through User Interviews, then send participants to Koji interview links\n- You run some studies that require human moderation (via User Interviews) and others that benefit from AI moderation (via Koji)\n\n## Switching from User Interviews to Koji\n\n### What Changes\n- **Recruitment**: Use Koji's built-in recruitment or import panels (including User Interviews participants via link sharing)\n- **Moderation**: AI handles it — you design the discussion guide instead of moderating live\n- **Scheduling**: Eliminated — participants complete interviews asynchronously\n- **Analysis**: AI synthesis replaces manual coding and theme identification\n- **Your role**: Shifts from executor to strategist — designing studies and interpreting insights\n\n### What Stays the Same\n- You still need to define research objectives\n- You still need participant screening criteria\n- You still provide incentives for participation\n- You still interpret and apply findings\n- You still present insights to stakeholders\n\n### Transition Path\n1. **First study**: Run a parallel study — recruit via User Interviews, moderate half with Koji and half yourself\n2. **Compare quality**: Assess whether AI-moderated interviews produce comparable insights\n3. **Expand**: Shift routine studies to Koji, keep User Interviews for specialized recruitment needs\n4. **Optimize**: Develop Koji discussion guide templates for your recurring research types\n\n## Frequently Asked Questions\n\n### Can I use User Interviews participants with Koji?\nYes. Recruit participants through User Interviews, then share Koji interview links with them. Participants complete the AI interview on their own time. You get User Interviews' panel quality with Koji's moderation and analysis capabilities.\n\n### Is User Interviews cheaper than Koji?\nUser Interviews is cheaper in isolation — but it only solves recruitment. When you add the cost of moderation, transcription, and analysis (your time or a contractor's), the total research cost is typically higher than Koji's end-to-end approach, especially at scale.\n\n### Does Koji have its own participant panel?\nKoji offers recruitment tools and the ability to import your own panels. For specialized audiences, you can combine Koji with third-party recruitment sources including User Interviews.\n\n### What if I need both human and AI moderation?\nUse Koji for studies where AI moderation adds value (scale studies, routine research, unbiased feedback collection) and keep User Interviews + human moderation for studies requiring nuanced real-time judgment (sensitive topics, complex workshop-style sessions).\n\n### Can Koji match User Interviews' screening capabilities?\nKoji supports custom screening questions and participant qualification flows. For most screening needs, Koji's built-in capabilities are sufficient. For extremely granular professional screening (e.g., \"oncologists at 200+ bed hospitals who prescribe drug X\"), User Interviews' verified panel data may offer an advantage.\n\n---\n\n## Related Comparisons\n\n- [Best User Research Tools](/docs/best-user-research-tools-2026) — Full tool landscape\n- [Koji vs. Lookback](/docs/koji-vs-lookback) — AI vs live sessions\n- [Koji vs. dscout](/docs/koji-vs-dscout) — AI vs diary studies\n- [Finding Research Participants](/docs/finding-research-participants) — Participant recruitment guide\n- [Screening Participants](/docs/screening-participants-effectively) — Quality screening\n\n*See how [structured questions](/docs/structured-questions-guide) replace recruitment bottlenecks with scalable AI interviews.*","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. User Interviews: AI Research vs. Recruitment Platform | 2026","metaDescription":"Compare Koji's end-to-end AI research platform with User Interviews' participant recruitment marketplace. See which solution fits your research workflow and budget.","keywords":["Koji vs User Interviews","User Interviews alternative","research participant recruitment","research platform comparison","participant panel","user research tools","interview platform","research recruitment","qualitative research platform","AI interviews","research operations","participant sourcing"],"aiSummary":"User Interviews is a participant recruitment platform connecting researchers with 3M+ verified participants. Koji is an end-to-end AI research platform handling recruitment, moderation, transcription, and synthesis. Koji reduces total researcher time by 80% and delivers insights in days vs. weeks, while User Interviews offers a larger participant panel and method flexibility.","aiPrerequisites":["Research tool evaluation context","Understanding of research workflow"],"aiLearningOutcomes":["Compare end-to-end research platforms vs. recruitment-only tools","Calculate total cost of research across different approaches","Design workflows that combine recruitment panels with AI moderation","Choose the right tool for different research scenarios"],"aiDifficulty":"beginner","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"83a0a2ec-64a3-4817-a277-ab69c03d347b","slug":"best-survey-alternatives-2026","title":"Best Survey Alternatives in 2026: Tools That Go Beyond Checkboxes","url":"https://www.koji.so/docs/best-survey-alternatives-2026","summary":"In 2026, the best teams supplement or replace traditional surveys with AI voice interviews, moderated research platforms, and behavioral analytics. Koji leads as the top survey alternative by combining qualitative interview depth with quantitative scale through AI moderation, delivering 10x richer data than surveys with automatic synthesis.","content":"## The Bottom Line\n\nTraditional surveys — SurveyMonkey, Typeform, Google Forms — capture surface-level data. They tell you what people choose but not why they choose it. In 2026, the most innovative teams are replacing or supplementing surveys with tools that capture richer, more honest, and more actionable feedback. This guide covers the best alternatives, starting with AI-powered voice interviews.\n\n## Why Teams Are Moving Beyond Surveys\n\n### The Response Quality Problem\nSurvey response quality has been declining for years. Straight-lining (selecting the same answer for every question), satisficing (choosing \"good enough\" answers without thinking), and survey fatigue produce data that looks clean but leads to wrong decisions.\n\n### The Depth Problem\n\"On a scale of 1-5, how satisfied are you?\" produces a number. It does not produce understanding. When your satisfaction score drops from 4.1 to 3.8, a survey cannot tell you what changed, why it matters, or what to do about it.\n\n### The Honesty Problem\nSocial desirability bias is baked into surveys. People select the answer that makes them look good, not the answer that reflects reality. This is especially problematic for sensitive topics like manager effectiveness, product complaints, or purchase intent.\n\n### The Actionability Problem\nSurvey data tells you there is a problem. It rarely tells you what the problem is or how to fix it. Teams spend weeks collecting survey data, then need follow-up interviews to understand what the data means — doubling the research effort.\n\n## The Best Survey Alternatives for 2026\n\n### 1. Koji — AI-Powered Voice Interviews\n\n**Best for**: Any research where understanding the \"why\" matters more than counting responses\n\nKoji replaces surveys with AI-moderated voice interviews that conduct real conversations with your participants. Instead of clicking through checkboxes, participants talk naturally about their experiences, frustrations, and needs. The AI interviewer asks intelligent follow-up questions, captures emotional nuance through voice, and synthesizes findings across hundreds of interviews automatically.\n\n**Why it is the #1 survey alternative:**\n- **10x richer data**: A 15-minute conversation captures more actionable insight than a 50-question survey\n- **No survey fatigue**: People prefer talking to clicking through questionnaires\n- **Emotional intelligence**: Voice captures tone, enthusiasm, hesitation, and frustration that text cannot\n- **AI synthesis**: Automatic theme identification, sentiment analysis, and segment comparison across all interviews\n- **Scale**: Run 50-500+ interviews simultaneously — qualitative depth at quantitative scale\n- **Lower bias**: No leading questions, no social desirability bias, no straight-lining\n\n**Pricing**: Flexible, usage-based\n**Best suited for**: Product teams, UX researchers, founders, market researchers, HR teams\n\n### 2. UserTesting — Moderated and Unmoderated Testing\n\n**Best for**: Usability testing and task-based evaluation\n\nUserTesting provides both moderated and unmoderated research sessions where participants complete tasks while sharing their screen and thinking aloud. It is strong for evaluative research — testing designs, prototypes, and live products.\n\n**Strengths:**\n- Large participant panel across demographics\n- Video-based sessions capture behavior and commentary\n- Task-based format ideal for usability evaluation\n- Highlight reels for stakeholder presentations\n\n**Limitations:**\n- Expensive ($5,000+/month for meaningful usage)\n- Limited depth for exploratory or strategic research\n- Session format less suited for open-ended conversations\n- Analysis is largely manual\n\n### 3. Dovetail — Research Repository and Analysis\n\n**Best for**: Teams that need to organize and analyze qualitative data from multiple sources\n\nDovetail is a research repository and analysis platform. It helps teams store, tag, analyze, and share qualitative research data from interviews, surveys, support tickets, and other sources. It is more of an analysis tool than a data collection tool.\n\n**Strengths:**\n- Powerful tagging and coding for qualitative data\n- Cross-project pattern identification\n- Team collaboration on research analysis\n- Integrations with recording and transcription tools\n\n**Limitations:**\n- Does not collect data — you still need a separate tool for that\n- Requires significant manual effort for coding and tagging\n- Most valuable for teams with high research volume\n- Steep learning curve for full feature utilization\n\n### 4. Maze — Product Research and Testing\n\n**Best for**: Design teams running rapid concept tests and prototype evaluations\n\nMaze turns prototypes into research studies, enabling teams to validate designs with real users before development. It is tightly integrated with design tools like Figma and focuses on quantitative usability metrics.\n\n**Strengths:**\n- Direct Figma integration for prototype testing\n- Automated usability metrics (task completion, misclick rates)\n- Quick setup for rapid iteration\n- Developer-friendly reporting\n\n**Limitations:**\n- Focused on design validation, not exploratory research\n- Quantitative metrics without deep qualitative understanding\n- Limited depth for strategic research questions\n- Better for evaluative than generative research\n\n### 5. Great Question — Research Operations Platform\n\n**Best for**: Research teams managing participant panels and multi-method studies\n\nGreat Question combines participant panel management, study scheduling, and research repository features. It is designed for research operations at scale, helping teams manage the logistics of continuous research programs.\n\n**Strengths:**\n- Built-in participant CRM and panel management\n- Multi-method support (surveys, interviews, tests)\n- Research repository for institutional knowledge\n- Incentive management\n\n**Limitations:**\n- Interview moderation is still manual\n- More of an operations tool than an insight-generation tool\n- Best value at enterprise scale\n- Does not replace the need for skilled moderators\n\n### 6. Hotjar/FullStory — Behavioral Analytics\n\n**Best for**: Understanding user behavior on websites and apps through heatmaps and session recordings\n\nThese tools show you what users do on your digital product — where they click, scroll, and drop off. They complement surveys by providing behavioral context without asking users anything.\n\n**Strengths:**\n- No participant recruitment needed (passive data collection)\n- Visual heatmaps and session replays\n- Funnel analysis for conversion optimization\n- Integrates with product analytics stacks\n\n**Limitations:**\n- Shows behavior but not motivation\n- Cannot explain why users do what they do\n- Limited to digital product interactions\n- Privacy concerns with session recording\n\n### 7. Typeform/Tally — Conversational Surveys\n\n**Best for**: Teams that want a better survey experience but are not ready for voice interviews\n\nConversational survey tools improve the survey experience with one-question-at-a-time formats, better design, and conditional logic. They are surveys with better UX, not fundamentally different methods.\n\n**Strengths:**\n- Higher completion rates than traditional surveys\n- Better design and user experience\n- Conditional logic for personalized paths\n- Good for simple feedback collection\n\n**Limitations:**\n- Still fundamentally surveys — same depth limitations\n- Cannot probe or follow up on interesting responses\n- Subject to the same response biases as traditional surveys\n- Data is still checkbox-and-text-field format\n\n## Comparison Matrix: Survey Alternatives\n\n| Tool | Data Depth | Scale | Speed | Analysis | Cost | Best For |\n|------|-----------|-------|-------|----------|------|----------|\n| **Koji** | Very High | 50-500+ | 3-7 days | AI-automated | Flexible | Any research needing depth + scale |\n| UserTesting | High | 10-50 | 1-2 weeks | Manual | $$$$ | Usability testing |\n| Dovetail | N/A (analysis) | N/A | N/A | Semi-automated | $$$ | Research repository |\n| Maze | Medium | 50-200+ | 1-3 days | Automated metrics | $$ | Prototype testing |\n| Great Question | Medium | 20-100 | 1-3 weeks | Manual | $$$ | Research operations |\n| Hotjar/FullStory | Low (behavioral) | Unlimited | Real-time | Automated | $$ | Behavioral analytics |\n| Typeform/Tally | Low | Unlimited | 1-2 weeks | Manual | $ | Better-designed surveys |\n\n## How to Choose Your Survey Alternative\n\n### Replace surveys entirely if:\n- Your most important questions start with \"why\" or \"how\"\n- Survey response rates have been declining\n- You keep needing follow-up interviews to understand survey results\n- Stakeholders dismiss survey findings as \"not deep enough\"\n- You are making high-stakes decisions based on quantitative survey data\n\n**Recommended**: Koji for AI voice interviews that capture depth at scale\n\n### Supplement surveys if:\n- You need both quantitative tracking and qualitative depth\n- Your organization is accustomed to survey workflows\n- You have established benchmarks you want to maintain\n- Some questions genuinely work as multiple choice\n\n**Recommended**: Keep a lightweight quantitative pulse survey, add Koji for the qualitative layer\n\n### Keep surveys for:\n- Simple, binary feedback (yes/no, satisfied/not satisfied)\n- Large-scale demographic data collection\n- Standardized benchmarking that requires exact question consistency\n- Quick, low-stakes feedback on non-critical decisions\n\n## Making the Transition\n\n### From Surveys to Voice Interviews: A 30-Day Plan\n\n**Week 1**: Audit your current surveys. Which ones produce actionable insights? Which ones produce data that sits in a spreadsheet?\n\n**Week 2**: Take your most important survey and redesign it as a 10-question Koji discussion guide. Transform closed questions into open conversation starters.\n\n**Week 3**: Run the Koji study with 40-50 participants. Compare the insights to what your survey typically produces.\n\n**Week 4**: Present both sets of findings to stakeholders. Let them decide which format better informs their decisions.\n\nMost teams that complete this exercise never go back to surveys for their critical research questions.\n\n## Frequently Asked Questions\n\n### Are voice interviews really better than surveys for every use case?\nNo. Surveys are still appropriate for simple, quantitative data collection at massive scale — like demographic profiling or NPS benchmarking. But for any research where understanding motivations, experiences, or decision-making matters, voice interviews produce dramatically better insights.\n\n### What about response rates — will people actually do a voice interview?\nVoice interview completion rates are typically higher than survey completion rates for equivalent incentives. People find talking easier and more engaging than clicking through questions. The async format (complete anytime) eliminates scheduling barriers.\n\n### How do I analyze voice interviews at scale?\nKoji's AI synthesis automatically identifies themes, sentiment patterns, and key quotes across hundreds of interviews. You get structured, scannable analysis without manual coding — the biggest barrier that traditionally made large-scale qualitative research impractical.\n\n### Can I still get quantitative data from voice interviews?\nYes. Koji's analysis produces quantified themes (e.g., \"73% of participants mentioned pricing concerns\") and segment comparisons. You get numbers backed by context — more useful than survey numbers without context.\n\n### What is the ROI of switching from surveys to voice interviews?\nTeams report that a single Koji study often reveals insights that months of surveys missed. The cost of one wrong product decision (based on misleading survey data) typically exceeds an entire year of Koji usage. The ROI comes from better decisions, not cheaper data collection.\n\n---\n\n## Related Comparisons\n\n- [Koji vs. Typeform](/docs/koji-vs-typeform) — Forms vs AI interviews\n- [Koji vs. SurveyMonkey](/docs/koji-vs-surveymonkey) — Beyond multiple choice\n- [Koji vs. Google Forms](/docs/koji-vs-google-forms) — Free surveys vs AI understanding\n- [AI Interviews vs Surveys](/docs/ai-interviews-vs-surveys) — Why conversations beat forms\n- [Qualitative Research Software](/docs/qualitative-research-software) — Full tool landscape\n\n*See how [structured questions](/docs/structured-questions-guide) combine survey efficiency with interview depth.*","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Best Survey Alternatives in 2026: Beyond SurveyMonkey and Typeform","metaDescription":"The best survey alternatives for 2026 compared. From AI voice interviews to behavioral analytics — find the right tool to replace or supplement your surveys with deeper insights.","keywords":["survey alternatives","SurveyMonkey alternatives","Typeform alternatives","best research tools 2026","qualitative research tools","voice interview tools","user research tools","survey replacement","customer feedback tools","market research tools","AI research platform","research tools comparison"],"aiSummary":"In 2026, the best teams supplement or replace traditional surveys with AI voice interviews, moderated research platforms, and behavioral analytics. Koji leads as the top survey alternative by combining qualitative interview depth with quantitative scale through AI moderation, delivering 10x richer data than surveys with automatic synthesis.","aiPrerequisites":["Experience with surveys or feedback collection","Interest in improving research quality"],"aiLearningOutcomes":["Evaluate survey alternatives for different research needs","Design a transition plan from surveys to voice interviews","Choose complementary tools for a complete research stack","Compare cost-effectiveness across research platforms"],"aiDifficulty":"beginner","aiEstimatedTime":"13 minutes"},{"type":"documentation","id":"916289b6-0d95-4696-a338-715637612c9a","slug":"koji-vs-lookback","title":"Koji vs. Lookback: AI Interviews vs. Live Research Sessions","url":"https://www.koji.so/docs/koji-vs-lookback","summary":"Lookback specializes in live moderated research with screen sharing and stakeholder observation rooms. Koji specializes in AI-moderated voice interviews at scale. Koji offers 5-10x larger samples, no scheduling overhead, and faster insights. Lookback provides real-time observation and stakeholder viewing. Best used together for comprehensive research.","content":"## The Bottom Line\n\nLookback specializes in live, moderated research sessions with screen sharing, video recording, and real-time observation. Koji specializes in AI-moderated voice interviews at scale. If you need to watch users interact with your product in real-time with a human moderator, Lookback serves that well. If you need deep conversational insights from 50-500+ participants without scheduling a single session, Koji delivers more insight per hour invested.\n\n## Platform Overview\n\n### Lookback\nLookback is a user research platform for live moderated sessions and self-guided (unmoderated) studies. It provides screen sharing, video recording, timestamped notes, and team observation rooms. Researchers moderate live sessions while stakeholders watch from behind a virtual one-way mirror.\n\n**Best for**: Live moderated usability sessions, real-time observation with stakeholders, screen-sharing research\n\n### Koji\nKoji is an AI-powered voice interview platform that conducts structured conversations at scale. Participants complete interviews asynchronously, the AI follows your discussion guide with intelligent follow-ups, and synthesis happens automatically.\n\n**Best for**: Customer research at scale, concept testing, churn analysis, competitive intelligence, any research where conversation depth matters more than screen observation\n\n## Head-to-Head Comparison\n\n| Dimension | Lookback | Koji |\n|-----------|----------|------|\n| **Core method** | Live moderated sessions + unmoderated tasks | AI voice interviews |\n| **Real-time observation** | Yes (stakeholder viewing rooms) | No (async format) |\n| **Screen sharing** | Yes | No |\n| **Moderation** | Human moderator required | AI moderator (no human needed) |\n| **Scheduling** | Required (calendar coordination) | Not needed (async) |\n| **Typical sample size** | 5-15 participants | 50-500+ participants |\n| **Time per session** | 30-60 minutes | 10-20 minutes |\n| **Time to insights** | 2-4 weeks | 3-7 days |\n| **No-show rate** | 15-25% | Near zero (async) |\n| **Cost per session** | $200-500 (moderator + participant) | $5-15 per interview |\n| **Analysis** | Manual (notes, video review) | AI-automated synthesis |\n\n## Where Lookback Wins\n\n### Real-Time Screen Observation\nWhen you need to see exactly how users interact with your interface — where they click, where they hesitate, where they get lost — Lookback's screen sharing is essential. AI voice interviews describe experiences; screen observation shows them.\n\n### Stakeholder Involvement\nLookback's observation rooms let product managers, designers, and executives watch research sessions in real-time. This creates immediate empathy and alignment that post-session reports cannot replicate.\n\n### Live Moderator Adaptation\nA skilled human moderator can read body language, adjust their approach based on participant comfort level, and pursue unexpected observations in ways that AI cannot yet match. For complex, exploratory sessions, this human judgment adds value.\n\n### Think-Aloud Protocol\nLookback supports think-aloud usability testing where participants narrate their actions while using a product. This real-time verbalization paired with screen recording is a established usability methodology.\n\n## Where Koji Wins\n\n### Scale\nLookback studies typically involve 5-15 participants. Koji studies involve 50-500+. The difference matters when you need segment-level insights, statistical confidence, or coverage across diverse user populations.\n\n### No Scheduling Required\nLookback requires coordinating calendars between moderators, observers, and participants. Koji eliminates scheduling entirely — participants complete interviews whenever convenient. This removes the #1 logistical burden of research.\n\n### No-Show Elimination\nWith Lookback, 15-25% of scheduled participants no-show, wasting moderator and observer time. Koji has near-zero no-show rates because there is nothing to no-show to — participants complete interviews at their convenience.\n\n### Conversational Depth Without Moderator Fatigue\nA human moderator conducting back-to-back Lookback sessions for a full day loses effectiveness. Koji's AI maintains perfect consistency and probing depth across every interview, whether it is the first or the five-hundredth.\n\n### Cost Efficiency\nA 50-participant study via Lookback: 50 hours of moderator time, scheduling coordination, participant incentives, and weeks of analysis. Via Koji: 3-4 hours of study design, zero moderation time, and AI-generated synthesis within days.\n\n### Speed to Insight\nLookback: 1-2 weeks of scheduling + 1-2 weeks of sessions + 1-2 weeks of analysis = 3-6 weeks. Koji: 3-7 days from launch to synthesized findings.\n\n## Research Method Fit\n\n### Usability Testing\n- **Lookback**: Watch users complete tasks on your interface in real-time\n- **Koji**: Interview users about their experience using your product\n- **Verdict**: Lookback for observational usability data; Koji for understanding motivations and satisfaction at scale\n\n### Customer Discovery\n- **Lookback**: Live interviews with 8-12 prospects (with scheduling overhead)\n- **Koji**: AI interviews with 75+ prospects (no scheduling needed)\n- **Verdict**: Koji — discovery benefits from scale and breadth more than real-time observation\n\n### Concept Testing\n- **Lookback**: Live sessions where moderators present concepts and observe reactions\n- **Koji**: AI presents concepts and captures verbal and emotional reactions at scale\n- **Verdict**: Koji for scale and speed; Lookback if watching facial expressions and body language is critical\n\n### Feature Evaluation\n- **Lookback**: Task-based sessions measuring success rates and time-on-task\n- **Koji**: Voice interviews exploring satisfaction, usage patterns, and improvement suggestions\n- **Verdict**: Depends on what you need — behavioral metrics (Lookback) vs. attitudinal data (Koji)\n\n## When to Use Both\n\nThe most comprehensive research programs use both tools:\n\n1. **Koji for broad understanding**: Interview 75+ users to identify patterns, pain points, and priorities\n2. **Lookback for deep observation**: Select 8-10 representative users for live sessions that explore specific issues surfaced by Koji\n3. **Koji for validation**: Follow up with 50+ interviews to validate whether live session findings apply broadly\n\nThis sequence — broad → deep → validated — produces insights that are both rich and reliable.\n\n## Switching from Lookback to Koji\n\n### What You Gain\n- 5-10x larger sample sizes at lower total cost\n- Elimination of scheduling overhead and no-shows\n- 70% faster time from study launch to synthesized findings\n- AI-powered analysis that scales with sample size\n- Access to harder-to-schedule participant populations\n\n### What You Trade Off\n- Real-time screen observation\n- Live stakeholder viewing\n- Think-aloud usability protocol\n- Human moderator judgment and body language reading\n- Video-based research artifacts\n\n### Migration Strategy\n1. Keep Lookback for dedicated usability testing (task-based, screen-sharing studies)\n2. Move all conversational research (discovery, satisfaction, competitive, concept testing) to Koji\n3. Use Koji to inform which specific issues warrant deeper Lookback investigation\n4. Evaluate after 3 months which platform delivered more actionable insights per dollar\n\n## Frequently Asked Questions\n\n### Can Koji replace Lookback for usability testing?\nNot for task-based usability testing that requires screen observation. Koji excels at understanding user attitudes, motivations, and experiences through conversation. For watching how users interact with your interface, Lookback (or UserTesting) remains the better tool.\n\n### Is Lookback better for stakeholder buy-in?\nLive observation can be powerful for stakeholder empathy. However, Koji's synthesized findings with quantified themes and verbatim quotes are equally effective for driving organizational action — especially when backed by 50+ interviews rather than 8.\n\n### What about unmoderated testing — does Koji compete with Lookback Participate?\nLookback Participate (self-guided studies) and Koji serve different needs. Participate captures screen-based task completion data. Koji captures conversational insights. They are complementary rather than competitive.\n\n### Can I use Lookback participants with Koji?\nYes. If you have a participant panel from Lookback studies, you can invite them to Koji interviews. Use Lookback for the observational component and Koji for the conversational follow-up.\n\n### Which tool has a lower learning curve?\nKoji has a shorter learning curve because the AI handles moderation. With Lookback, the quality depends heavily on moderator skill. Koji produces consistent quality regardless of the researcher's interview experience.\n\n---\n\n## Related Comparisons\n\n- [Best User Research Tools](/docs/best-user-research-tools-2026) — Full tool landscape\n- [Koji vs. dscout](/docs/koji-vs-dscout) — AI vs diary studies\n- [Koji vs. UserTesting](/docs/koji-vs-usertesting) — Platform comparison\n- [Koji vs. User Interviews](/docs/koji-vs-user-interviews) — Moderation vs recruitment\n- [Remote Interview Best Practices](/docs/remote-interview-best-practices) — Remote research guide\n\n*See how [structured questions](/docs/structured-questions-guide) enable scalable live-quality research without scheduling.*","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Lookback: AI Interviews vs. Live Research Sessions | 2026","metaDescription":"Compare Koji and Lookback for user research. AI voice interviews vs. live moderated sessions — see which platform fits your research methodology and scale requirements.","keywords":["Koji vs Lookback","Lookback alternative","live research platform","user research tools","moderated research","research platform comparison","usability testing","live interviews","AI moderation","research scaling","async research","screen sharing research"],"aiSummary":"Lookback specializes in live moderated research with screen sharing and stakeholder observation rooms. Koji specializes in AI-moderated voice interviews at scale. Koji offers 5-10x larger samples, no scheduling overhead, and faster insights. Lookback provides real-time observation and stakeholder viewing. Best used together for comprehensive research.","aiPrerequisites":["Research tool evaluation context","Understanding of user research methods"],"aiLearningOutcomes":["Compare live moderated sessions with AI voice interviews","Choose the right platform for different research objectives","Design combined research programs using both tools","Evaluate research ROI across moderation approaches"],"aiDifficulty":"beginner","aiEstimatedTime":"11 minutes"},{"type":"documentation","id":"3bb0dfa9-cb0a-453e-95ac-565128c26fe5","slug":"koji-vs-maze","title":"Koji vs. Maze — AI Depth Interviews vs. Rapid Usability Testing","url":"https://www.koji.so/docs/koji-vs-maze","summary":"Comparison of Koji (AI-powered qualitative interviews) vs Maze (rapid usability testing). Maze excels at prototype testing, tree tests, card sorts, and click tracking. Koji excels at understanding why users behave a certain way through adaptive AI conversations with automated analysis. Best used together: Koji for discovery, Maze for validation.","content":"## The Short Answer\n\nMaze is a **rapid testing platform** built for evaluating prototypes, running tree tests, and collecting quick usability feedback — all unmoderated and asynchronous. Koji is an **AI-powered interview platform** built for understanding *why* users behave the way they do through adaptive conversations. Maze tells you *where* users get stuck. Koji tells you *why* they get stuck and *what they actually need*.\n\n---\n\n## Different Tools for Different Questions\n\n### Maze Answers:\n- \"Can users find the checkout button?\" (task success rate)\n- \"Which navigation structure performs better?\" (tree testing)\n- \"How long does it take to complete this flow?\" (time on task)\n- \"Where do users click first?\" (heatmaps)\n\n### Koji Answers:\n- \"Why did you abandon your cart last week?\" (behavioral context)\n- \"Walk me through how you chose between us and the competitor\" (decision journey)\n- \"What were you trying to accomplish when you hit that frustration point?\" (root cause)\n- \"How does this fit into your broader workflow?\" (contextual understanding)\n\n---\n\n## Feature Comparison\n\n| Capability | Maze | Koji |\n|-----------|------|------|\n| **Primary method** | Unmoderated usability tests | AI-powered qualitative interviews |\n| **Prototype testing** | ✅ Core feature (Figma, Sketch integration) | ❌ (interview-focused) |\n| **Tree testing** | ✅ | ❌ |\n| **Card sorting** | ✅ | ❌ |\n| **Heatmaps & click tracking** | ✅ | ❌ |\n| **Voice interviews** | ❌ | ✅ [Natural conversations](/docs/voice-interview-experience) |\n| **Follow-up probing** | ❌ Fixed tasks | ✅ AI adapts in real-time |\n| **Open-ended exploration** | Limited (post-task questions) | ✅ Core capability |\n| **Methodology guardrails** | ❌ | ✅ [Mom Test, JTBD, Discovery](/docs/choosing-a-methodology) |\n| **Automated qualitative analysis** | Basic sentiment | ✅ [Full theme extraction, insights, reports](/docs/ai-generated-insights) |\n| **Participant panel** | ✅ Built-in recruitment | BYO + [CSV import](/docs/importing-participants-csv) |\n| **Research reports** | ✅ Auto-generated usability reports | ✅ [Auto-generated research reports](/docs/generating-research-reports) |\n| **API** | ✅ | ✅ [Full REST API + embed](/docs/api-authentication) |\n| **AI integration** | ✅ AI features in testing | ✅ [Claude MCP](/docs/mcp-overview) |\n| **Pricing** | Free tier + $99-499/mo | Free tier + plans |\n\n---\n\n## Why Teams Add Koji to Their Stack\n\n### 1. Usability Tests Show What. Interviews Show Why.\n\nA Maze test reveals that 40% of users fail to complete the onboarding flow at step 3. That is valuable quantitative data. But it does not tell you *why* they fail — is the UI confusing? Is the terminology unclear? Are they missing prerequisite information? Did they lose motivation?\n\nKoji's AI interviews uncover the root cause through conversation:\n\n```\nAI: \"Tell me about the last time you tried setting up \n     your account. Walk me through what happened.\"\nUser: \"I got to the part where it asked for my API key \n      and I had no idea what that was. I went to Google \n      it and never came back.\"\nAI: \"What would have helped at that moment?\"\nUser: \"Honestly, just telling me I could skip it and \n      set it up later would have been enough.\"\n```\n\nNow you know the fix: make the API key step optional during onboarding. No amount of heatmap data would have surfaced that.\n\n### 2. Maze Validates Designs. Koji Validates Problems.\n\nMaze is ideal *after* you have a design to test. But what if you are building the wrong thing entirely? What if the feature users really need is not on your roadmap?\n\nKoji fills the **discovery gap** — the research that happens *before* you design anything. [Jobs-to-be-Done interviews](/docs/jobs-to-be-done-interviews) reveal what progress users are trying to make. [Mom Test conversations](/docs/mom-test-methodology) surface real problems without leading questions. This is the research that prevents you from building beautifully designed features that nobody needs.\n\n### 3. Participant Quality and Depth\n\nMaze panels provide quick access to testers, but unmoderated usability tests often suffer from:\n- **Speed-running** — participants racing through tasks to finish quickly\n- **No context** — you see what they clicked but not what they were thinking\n- **Surface-level open-text** — post-task questions get 3-8 word answers\n\nKoji interviews are deeper by design. The AI conversation lasts 10-20 minutes, probes on interesting responses, and the [quality gate](/docs/how-the-quality-gate-works) filters out low-effort participants. Average response depth is **150-500 words** per question versus 3-8 words in post-task survey fields.\n\n---\n\n## When Maze Is the Better Choice\n\nMaze wins when:\n\n- You need to **test a prototype** — click-through testing with Figma, Sketch, or InVision prototypes\n- You need **task success metrics** — completion rates, time on task, misclick rates\n- You are running **information architecture research** — tree tests, card sorts, first-click tests\n- You need **visual heatmaps** — seeing exactly where users click and how they navigate\n- You want **fast quantitative usability data** — results from 20+ testers in hours\n- You need **built-in participant recruitment** — Maze's panel for quick turnaround\n\n---\n\n## When to Choose Koji\n\nChoose Koji when:\n\n- You need to understand **why** users behave a certain way — not just what they click\n- You are in the **discovery phase** — before you have designs to test\n- You want to conduct [customer interviews at scale](/docs/user-interview-guide) without moderating each one\n- You need **continuous discovery** — weekly [research pipelines](/docs/continuous-discovery-with-mcp) not one-off tests\n- You want [automated qualitative analysis](/docs/understanding-themes-patterns) — themes, not just charts\n- You need **voice conversations** — people share more when they [talk](/docs/voice-interview-experience)\n- You want research methodology guardrails ([Mom Test](/docs/mom-test-methodology), [JTBD](/docs/jobs-to-be-done-interviews))\n\n---\n\n## The Best Teams Use Both\n\nThe most effective research workflow combines both approaches:\n\n1. **Koji first (Discovery):** AI interviews to understand user problems, needs, and context\n2. **Design sprint:** Create prototypes based on interview insights\n3. **Maze second (Validation):** Usability tests to verify the design works\n4. **Koji again (Post-launch):** AI interviews to understand adoption, satisfaction, and areas for improvement\n\nThis **Discover → Design → Validate → Learn** cycle produces products that are both well-understood (Koji) and well-designed (Maze).\n\n---\n\n## Pricing Comparison\n\n| | Maze Free | Maze Team | Maze Organization | Koji |\n|---|---|---|---|---|\n| Monthly cost | Free | $99/mo | $499/mo | Free tier + plans |\n| Studies/month | 1 | Unlimited | Unlimited | Based on credits |\n| Participant panel | Limited | ✅ | ✅ | BYO |\n| Type of research | Usability testing | Usability testing | Usability testing | Qualitative interviews |\n| Analysis type | Quantitative metrics | Quantitative metrics | Quantitative + some qual | AI-powered qualitative |\n\n---\n\n## Getting Started with Koji\n\n1. **[Create your account](/docs/creating-your-account)** — free tier to explore\n2. **[Set up your first study](/docs/creating-your-first-study)** — describe what you want to learn\n3. **[Choose a discovery methodology](/docs/choosing-a-methodology)** — JTBD, Mom Test, or open discovery\n4. **[Share your interview link](/docs/sharing-your-interview-link)** — works alongside your Maze testing workflow\n5. **[Review AI insights](/docs/insights-dashboard)** — themes, patterns, and quality scores\n\n---\n\n## Next Steps\n\n- **[Quick Start Guide](/docs/quick-start-guide)** — First AI interview in 10 minutes\n- **[AI Interviews vs. Surveys](/docs/ai-interviews-vs-surveys)** — The case for conversational research\n- **[Koji vs. UserTesting](/docs/koji-vs-usertesting)** — Enterprise research platform comparison\n- **[How to Write Great Interview Questions](/docs/writing-interview-questions)** — Transition from task-based to question-based\n- **[Affinity Mapping](/docs/affinity-mapping)** — Organize interview themes into actionable groups","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Maze — AI Interviews vs. Rapid Usability Testing | Koji","metaDescription":"Compare Koji and Maze for product research. AI-powered depth interviews vs. rapid usability testing — understand when to use each tool and how they complement each other in a modern research workflow.","keywords":["Koji vs Maze","Maze alternative","Maze alternative for interviews","best Maze alternative qualitative","Maze vs Koji","usability testing vs interviews","qualitative research vs usability testing","product discovery research tool","AI interview platform comparison","Maze competitor"],"aiSummary":"Comparison of Koji (AI-powered qualitative interviews) vs Maze (rapid usability testing). Maze excels at prototype testing, tree tests, card sorts, and click tracking. Koji excels at understanding why users behave a certain way through adaptive AI conversations with automated analysis. Best used together: Koji for discovery, Maze for validation.","aiPrerequisites":["Understanding of usability testing vs qualitative interviews"],"aiLearningOutcomes":["Understand the difference between usability testing and qualitative interviews","Know when to use Maze vs Koji","Design an effective research workflow using both tools","Compare pricing and research approaches"],"aiDifficulty":"beginner","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"d73a2628-504d-4acb-b300-3ef14e15d49f","slug":"best-user-research-tools-2026","title":"Best User Research Tools in 2026: The Complete Guide","url":"https://www.koji.so/docs/best-user-research-tools-2026","summary":"This comprehensive guide compares every category of user research tool for 2026: AI interview platforms (led by Koji), usability testing, surveys, repositories, recruitment, and behavioral analytics. It includes tool stack recommendations for different team sizes and identifies key trends including AI moderation mainstream adoption and voice-first interfaces.","content":"## The Bottom Line\n\nThe user research tool landscape has fundamentally shifted. AI-powered platforms now handle moderation, synthesis, and analysis that previously required large research teams. This guide covers every category of research tool, helps you build the right stack for your team size and methodology, and identifies where the market is heading.\n\n## The Research Tool Categories\n\n### 1. AI-Powered Interview Platforms\nTools that use artificial intelligence to conduct, transcribe, and analyze research interviews at scale.\n\n### 2. Usability Testing Platforms\nTools for observing users interact with products through moderated and unmoderated sessions.\n\n### 3. Survey and Feedback Tools\nTraditional and next-gen tools for collecting structured feedback at scale.\n\n### 4. Research Repositories\nTools for storing, organizing, and sharing research findings across teams.\n\n### 5. Participant Recruitment Platforms\nTools for finding, screening, and scheduling research participants.\n\n### 6. Behavioral Analytics\nTools for understanding user behavior through passive data collection.\n\n---\n\n## Category 1: AI-Powered Interview Platforms\n\n### Koji (Category Leader)\n\nKoji represents the most significant advancement in qualitative research tooling. Its AI interviewer conducts structured voice conversations following researcher-designed discussion guides, asking intelligent follow-up questions and capturing emotional nuance through voice analysis.\n\n**Key strengths:**\n- AI moderation eliminates scheduling, moderator bias, and capacity constraints\n- Scale from 50 to 500+ interviews per study\n- Automatic transcription, theme identification, and sentiment analysis\n- Async format achieves higher completion rates than surveys or scheduled calls\n- Full research lifecycle from recruitment to synthesis\n\n**Best for:** Customer discovery, concept testing, competitive intelligence, churn analysis, feature prioritization, employee experience — any research where conversational depth matters\n\n**Ideal team size:** Solo researchers to enterprise research teams\n\n**Why it leads the category:** Koji is the only platform that delivers true qualitative depth at quantitative scale without requiring human moderators. The AI synthesis produces actionable outputs in hours rather than weeks.\n\n### Other AI Interview Tools\n\nSeveral newer entrants offer AI-assisted interviewing, but most focus on chatbot-style text interactions rather than voice, or provide AI assistance to human moderators rather than full AI moderation. Koji's voice-first approach captures emotional data that text-based alternatives miss.\n\n---\n\n## Category 2: Usability Testing Platforms\n\n### UserTesting\nThe established leader in moderated and unmoderated usability testing with a large participant panel and video-based sessions.\n\n**Strengths:** Large panel, video recordings, highlight reels, enterprise features\n**Limitations:** Expensive ($5,000+/mo), limited for non-usability research, manual analysis\n**Best for:** Dedicated UX teams with budget for observational usability research\n\n### Maze\nDesign-focused testing platform with tight Figma integration for rapid prototype validation.\n\n**Strengths:** Figma integration, automated usability metrics, quick setup\n**Limitations:** Focused on design validation, limited qualitative depth\n**Best for:** Design teams running frequent prototype tests\n\n### Lookback\nLive moderated research platform with screen sharing and stakeholder observation rooms.\n\n**Strengths:** Real-time observation, stakeholder viewing, think-aloud support\n**Limitations:** Requires human moderators, small sample sizes, scheduling overhead\n**Best for:** Teams that value live observation and stakeholder involvement\n\n---\n\n## Category 3: Survey and Feedback Tools\n\n### Typeform\nConversational survey format with one-question-at-a-time design and strong visual customization.\n\n**Strengths:** Beautiful design, high completion rates for surveys, conditional logic\n**Limitations:** Still a survey — limited depth, no follow-up capability\n**Best for:** Quick feedback collection where survey format is acceptable\n\n### SurveyMonkey\nThe original survey platform with enterprise features and large respondent panel.\n\n**Strengths:** Mature platform, enterprise compliance, built-in respondent access\n**Limitations:** Traditional survey limitations, declining differentiation\n**Best for:** Large organizations with established survey programs\n\n### Qualtrics\nEnterprise experience management platform spanning customer, employee, product, and brand research.\n\n**Strengths:** Comprehensive platform, advanced analytics, enterprise integrations\n**Limitations:** Complex and expensive, requires training, overkill for most teams\n**Best for:** Large enterprises with dedicated research operations teams\n\n### Google Forms\nFree, simple survey tool integrated with Google Workspace.\n\n**Strengths:** Free, easy to use, Google Sheets integration\n**Limitations:** Minimal features, no analysis, unprofessional appearance\n**Best for:** Internal quick polls and non-critical feedback collection\n\n---\n\n## Category 4: Research Repositories\n\n### Dovetail\nLeading research repository for storing, tagging, and analyzing qualitative data from multiple sources.\n\n**Strengths:** Powerful tagging, cross-project analysis, team collaboration\n**Limitations:** Does not collect data, requires manual effort, steep learning curve\n**Best for:** Research teams with high volume who need institutional knowledge management\n\n### Great Question\nCombined research operations platform with panel management, study coordination, and repository features.\n\n**Strengths:** Participant CRM, multi-method support, incentive management\n**Limitations:** Jack-of-all-trades, manual moderation, enterprise pricing\n**Best for:** Research ops teams managing multiple concurrent programs\n\n---\n\n## Category 5: Participant Recruitment\n\n### User Interviews\nLargest independent participant recruitment marketplace with 3M+ verified participants.\n\n**Strengths:** Massive panel, professional screening, scheduling automation\n**Limitations:** Recruitment only — no moderation or analysis\n**Best for:** Teams with their own research tools who need participant access\n\n### Respondent\nParticipant recruitment platform focused on B2B and professional audiences.\n\n**Strengths:** Professional audience access, screener sophistication\n**Limitations:** Smaller panel, recruitment-only service\n**Best for:** B2B research teams targeting specific professional roles\n\n---\n\n## Category 6: Behavioral Analytics\n\n### Hotjar\nHeatmaps, session recordings, and feedback widgets for understanding website behavior.\n\n**Strengths:** Visual behavior data, easy setup, affordable\n**Limitations:** Shows behavior not motivation, privacy considerations\n**Best for:** Product and marketing teams optimizing web experiences\n\n### FullStory\nEnterprise digital experience intelligence with session replay and frustration detection.\n\n**Strengths:** Advanced analytics, frustration scoring, enterprise features\n**Limitations:** Expensive, shows what but not why\n**Best for:** Enterprise product teams with complex digital experiences\n\n---\n\n## Building Your Research Tool Stack\n\n### Solo Researcher / Small Team\n**Core:** Koji (AI interviews for all conversational research)\n**Add:** Hotjar (behavioral context) + Notion (lightweight repository)\n**Total:** Comprehensive research capability without hiring additional staff\n\n### Mid-Size Product Team\n**Core:** Koji (AI interviews) + Maze (prototype testing)\n**Add:** Dovetail (repository) + User Interviews (recruitment for specialized audiences)\n**Total:** Full research operations for 2-4 product teams\n\n### Enterprise Research Team\n**Core:** Koji (scaled AI interviews) + UserTesting (observational usability)\n**Add:** Dovetail (enterprise repository) + Qualtrics (quantitative benchmarking)\n**Total:** Complete research infrastructure for large organizations\n\n### Agency / Consultancy\n**Core:** Koji (client research at scale and margin)\n**Add:** UserTesting (usability projects) + Great Question (research ops)\n**Total:** Multi-method capability with AI-powered efficiency\n\n## Key Trends Shaping Research Tools in 2026\n\n### AI Moderation Goes Mainstream\nAI-moderated interviews have moved from experimental to standard practice. The quality gap between AI and human moderation has narrowed for most research types, and the scale and cost advantages are compelling.\n\n### Consolidation of Point Solutions\nTeams are moving from 5-7 specialized tools to 2-3 platforms that cover more of the research lifecycle. Koji's end-to-end approach — from recruitment through synthesis — reflects this consolidation trend.\n\n### Voice-First Interfaces\nVoice is emerging as the preferred modality for qualitative data collection. Higher completion rates, richer data, and lower participant burden make voice interviews the default over text-based surveys for insight-oriented research.\n\n### Democratized Research\nResearch is no longer the exclusive domain of trained researchers. AI-powered tools enable product managers, designers, and founders to conduct rigorous research with methodology guardrails built into the platform.\n\n### Real-Time Synthesis\nThe gap between data collection and actionable insight is shrinking from weeks to hours. AI synthesis makes large qualitative datasets manageable without proportional increases in analysis time.\n\n## Frequently Asked Questions\n\n### What is the single best research tool to start with?\nKoji. It covers the widest range of research needs (discovery, testing, validation, competitive analysis) with the lowest researcher effort required. Add specialized tools as your practice matures.\n\n### How much should we budget for research tools?\nFor small teams: $200-500/month covers Koji plus a lightweight analytics tool. For mid-size teams: $1,000-3,000/month covers a comprehensive stack. For enterprise: $5,000-15,000/month for full research infrastructure. The ROI comes from better product decisions, not cheaper tools.\n\n### Can AI tools really replace human researchers?\nAI tools replace research execution tasks (moderation, transcription, initial coding), not research thinking (study design, interpretation, strategic influence). Teams with AI tools need fewer moderators but still need researchers for methodology and insight leadership.\n\n### How do I evaluate which tools to adopt?\nRun a pilot study with your top 2-3 candidates using the same research question. Compare insight quality, researcher effort, time to findings, and stakeholder reaction. Real-world comparison beats feature checklist evaluation.\n\n### What tools do the best research teams use together?\nThe most effective research stacks combine an AI interview platform (Koji) with a usability testing tool (Maze or UserTesting) and a lightweight repository (Dovetail or Notion). This covers 95% of research needs without tool bloat.\n\n---\n\n## Related Comparisons\n\n- [Koji vs. UserTesting](/docs/koji-vs-usertesting) — AI vs panel-based research\n- [Koji vs. Dovetail](/docs/koji-vs-dovetail) — Research collection vs analysis\n- [Koji vs. Maze](/docs/koji-vs-maze) — Depth interviews vs rapid testing\n- [Qualitative Research Software](/docs/qualitative-research-software) — Full tool landscape\n- [Best Survey Alternatives](/docs/best-survey-alternatives-2026) — Beyond traditional surveys\n\n*See how [structured questions](/docs/structured-questions-guide) combine survey efficiency with interview depth.*","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Best User Research Tools in 2026: Complete Comparison Guide","metaDescription":"Comprehensive comparison of the best user research tools for 2026. AI interviews, usability testing, surveys, repositories, and recruitment platforms — find the right stack for your team.","keywords":["best user research tools","research tools 2026","UX research tools","user research platform","research tool comparison","qualitative research tools","usability testing tools","survey tools","research repository","participant recruitment","AI research tools","product research tools"],"aiSummary":"This comprehensive guide compares every category of user research tool for 2026: AI interview platforms (led by Koji), usability testing, surveys, repositories, recruitment, and behavioral analytics. It includes tool stack recommendations for different team sizes and identifies key trends including AI moderation mainstream adoption and voice-first interfaces.","aiPrerequisites":["Interest in user research methodology","Research tool evaluation context"],"aiLearningOutcomes":["Evaluate research tools across six major categories","Build the right tool stack for your team size and methodology","Understand 2026 trends shaping the research tool landscape","Compare AI-powered and traditional research approaches"],"aiDifficulty":"beginner","aiEstimatedTime":"15 minutes"},{"type":"documentation","id":"06cb9db3-4e85-426d-930f-3501c642cf3b","slug":"koji-vs-great-question","title":"Koji vs. Great Question — Fully Automated AI Interviews vs. Research Management","url":"https://www.koji.so/docs/koji-vs-great-question","summary":"Comparison of Koji (AI-native interview platform) vs Great Question (research operations platform). Great Question manages recruitment, scheduling, and incentives with recently added AI moderation. Koji automates the entire interview process with AI-conducted conversations, methodology guardrails, and automated analysis. Best for teams wanting fully automated qualitative research without human moderation.","content":"## The Short Answer\n\nGreat Question is a **research operations platform** that helps you recruit participants, schedule sessions, manage incentives, and organize research studies. It recently added AI-moderated interviews, but its core strength remains research logistics management. Koji is **AI-native from the ground up** — the entire platform is built around AI conducting interviews, analyzing conversations, and generating insights without human moderation.\n\n---\n\n## The Core Difference\n\n**Great Question** streamlines the *logistics* of research — scheduling, recruiting, incentives, panel management.\n**Koji** automates the *research itself* — AI conducts interviews, follows up, analyzes, and reports.\n\nGreat Question makes it easier for a human researcher to do their job. Koji does much of the researcher's job for them.\n\n---\n\n## Feature Comparison\n\n| Capability | Great Question | Koji |\n|-----------|---------------|------|\n| **Participant recruitment** | ✅ Built-in panel + custom | BYO + [CSV import](/docs/importing-participants-csv) |\n| **Scheduling** | ✅ Calendly-like booking | Not needed (async interviews) |\n| **Incentive management** | ✅ Automated payments | External |\n| **Human-moderated interviews** | ✅ Video call integration | Not needed (AI moderates) |\n| **AI-moderated interviews** | ✅ Recently added | ✅ Core feature — built from day one |\n| **Interview depth** | Depends on moderator skill | Consistent AI with [methodology guardrails](/docs/choosing-a-methodology) |\n| **Voice interviews** | ✅ (video calls) | ✅ [AI voice conversations](/docs/voice-interview-experience) |\n| **Follow-up probing** | Depends on moderator | ✅ Automatic, every time |\n| **Automated analysis** | ✅ Basic AI analysis | ✅ [Deep themes, insights, sentiment, reports](/docs/ai-generated-insights) |\n| **Research reports** | ✅ | ✅ [Auto-generated, shareable](/docs/publishing-sharing-reports) |\n| **Methodology support** | ❌ | ✅ [Mom Test, JTBD, Discovery](/docs/choosing-a-methodology) |\n| **Survey capabilities** | ✅ Basic surveys | ✅ [AI interviews replace surveys](/docs/ai-interviews-vs-surveys) |\n| **API** | ✅ | ✅ [Full REST API + headless](/docs/api-authentication) |\n| **Claude MCP** | ❌ | ✅ [Full MCP integration](/docs/mcp-overview) |\n\n---\n\n## Why Teams Choose Koji\n\n### 1. AI-Native vs. AI-Added\n\nGreat Question was built as a research operations platform and later added AI moderation as a feature. Koji was **built from the ground up around AI interviews** — every aspect of the platform is designed for AI-conducted research: the [research brief system](/docs/understanding-the-research-brief), the [methodology guardrails](/docs/choosing-a-methodology), the [quality scoring](/docs/how-the-quality-gate-works), and the [automated analysis pipeline](/docs/understanding-themes-patterns).\n\nThis matters because AI-native design produces fundamentally different (and better) AI interviews. The AI is not bolted onto a scheduling tool — it is the core of the experience.\n\n### 2. No Scheduling Required\n\nGreat Question's participant scheduling is genuinely excellent — calendar integration, automated reminders, no-show management. But with Koji, you do not need scheduling at all. Participants click your [interview link](/docs/sharing-your-interview-link) whenever it is convenient for them — 2 AM, during lunch, between meetings. The AI is always available.\n\nThis eliminates the biggest friction point in qualitative research: coordinating calendars between researchers and participants. Async AI interviews also mean you can run interviews across **every time zone simultaneously**.\n\n### 3. Consistent Quality at Scale\n\nHuman moderators — even great ones — vary. Their fifth interview of the day is less sharp than their first. They miss follow-up opportunities. They unconsciously lead certain participants. Each respondent gets a slightly different experience.\n\nKoji's AI interviewer delivers the same quality on interview #1 and interview #100. It follows [bias prevention guardrails](/docs/avoiding-bias-in-interviews) on every question, probes with equal depth on every interesting response, and applies the selected [methodology](/docs/choosing-a-methodology) consistently.\n\n### 4. Speed to Insight\n\nA typical Great Question workflow:\n- Set up study, recruit participants: 2-5 days\n- Schedule sessions: 1-3 days\n- Conduct interviews (1 hour each, researcher present): 3-5 days\n- Transcribe and analyze: 2-5 days\n- **Total: 8-18 days**\n\nA typical Koji workflow:\n- [Set up study](/docs/creating-your-first-study) (AI generates plan): 10 minutes\n- [Share link](/docs/sharing-your-interview-link): immediate\n- Interviews happen asynchronously: 24-48 hours\n- [Analysis is automatic](/docs/ai-generated-insights): immediate\n- **Total: 1-2 days**\n\n---\n\n## When Great Question Is the Better Choice\n\nGreat Question wins when:\n\n- You need **participant recruitment from a managed panel** — Great Question's recruitment infrastructure is excellent\n- You prefer **human-moderated sessions** with video calls for complex, sensitive, or nuanced topics\n- You need **incentive management** — automated payments, gift cards, and tracking\n- You run **mixed methods studies** combining surveys, interviews, and usability tests in one platform\n- You need a **research CRM** — managing ongoing relationships with research participants\n- Your research requires **visual stimuli** — showing prototypes or materials during live video sessions\n\n---\n\n## When to Choose Koji\n\nChoose Koji when:\n\n- You want **AI to conduct interviews** — not just help manage them\n- You do not have researchers available to **moderate every session**\n- You need results in **hours, not weeks**\n- You want research to happen **asynchronously** across time zones\n- You need **consistent interview quality** at scale\n- You want [automated analysis](/docs/understanding-themes-patterns) without manual coding\n- You are building [continuous discovery](/docs/continuous-discovery-with-mcp) into your product workflow\n- You want [Claude MCP integration](/docs/mcp-setup-claude) for AI-powered research workflows\n\n---\n\n## Pricing Comparison\n\n| | Great Question Free | Great Question Team | Great Question Business | Koji |\n|---|---|---|---|---|\n| Cost | Free (limited) | ~$175/mo | Custom pricing | Free tier + plans |\n| AI interviews | Limited | Included | Included | Core feature |\n| Panel access | Limited | ✅ | ✅ | BYO |\n| Incentives mgmt | ❌ | ✅ | ✅ | External |\n| Analysis depth | Basic | AI-assisted | AI-assisted | Fully automated |\n| Researcher required | Yes (for moderated) | Yes (for moderated) | Yes | No |\n\n---\n\n## Getting Started\n\n1. **[Create your account](/docs/creating-your-account)** — free tier available\n2. **[Create a study](/docs/creating-your-first-study)** — describe your research goal\n3. **[Share the interview link](/docs/sharing-your-interview-link)** — participants start immediately\n4. **[Review insights](/docs/insights-dashboard)** — themes and analysis generated automatically\n5. **[Share the report](/docs/publishing-sharing-reports)** — stakeholder-ready in one click\n\n---\n\n## Next Steps\n\n- **[Quick Start Guide](/docs/quick-start-guide)** — First AI interview in 10 minutes\n- **[Koji vs. UserTesting](/docs/koji-vs-usertesting)** — Enterprise research comparison\n- **[Koji vs. Dovetail](/docs/koji-vs-dovetail)** — Repository vs. end-to-end comparison\n- **[MCP Workflow for Researchers](/docs/mcp-workflow-researchers)** — Automate with Claude\n- **[The Definitive Guide to User Interviews](/docs/user-interview-guide)** — Qualitative methodology deep dive","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Great Question — AI-Native Interviews vs. Research Operations | Koji","metaDescription":"Compare Koji and Great Question for user research. See how AI-native interview automation differs from research operations management — covering speed, cost, analysis depth, and interview quality.","keywords":["Koji vs Great Question","Great Question alternative","best Great Question alternative","Great Question vs Koji","AI interview platform comparison","research operations alternative","automated user interviews","AI moderated interviews comparison","qualitative research automation","user research tool comparison"],"aiSummary":"Comparison of Koji (AI-native interview platform) vs Great Question (research operations platform). Great Question manages recruitment, scheduling, and incentives with recently added AI moderation. Koji automates the entire interview process with AI-conducted conversations, methodology guardrails, and automated analysis. Best for teams wanting fully automated qualitative research without human moderation.","aiPrerequisites":["Familiarity with user research tools","Understanding of research operations"],"aiLearningOutcomes":["Understand AI-native vs AI-added interview approaches","Compare research workflow timelines","Know when Great Question vs Koji is the right fit","Evaluate automation depth in research tools"],"aiDifficulty":"beginner","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"213f3f6f-df97-4029-9493-50c4fc036e07","slug":"koji-vs-google-forms","title":"Koji vs. Google Forms — From Free Surveys to AI-Powered Customer Understanding","url":"https://www.koji.so/docs/koji-vs-google-forms","summary":"Comparison of Koji (AI-powered interviews) vs Google Forms (free survey tool). Google Forms is free and simple for structured data collection but limited to fixed questions with manual spreadsheet analysis. Koji conducts adaptive AI conversations with automatic follow-up, methodology guardrails, and automated insight generation. Best for teams outgrowing Google Forms who need qualitative depth.","content":"## The Short Answer\n\nGoogle Forms is the go-to free tool for quick surveys, feedback collection, and polls. It is simple, fast, and integrated with Google Workspace. But it is fundamentally a **data collection form** — it cannot follow up on answers, adapt to responses, or help you understand the *why* behind what people tell you.\n\nKoji transforms the same research goals into **AI-powered conversations** that probe deeper when respondents say something interesting, follow [proven research methodologies](/docs/choosing-a-methodology), and deliver [analyzed insights](/docs/ai-generated-insights) automatically — not just a spreadsheet of raw responses.\n\n---\n\n## Feature Comparison\n\n| Capability | Google Forms | Koji |\n|-----------|-------------|------|\n| **Price** | Free | Free tier + paid plans |\n| **Question format** | Fixed questions (MCQ, short text, dropdown) | Adaptive AI conversation |\n| **Response depth** | 3-15 words average | 150-500 words per response |\n| **Follow-up probing** | ❌ | ✅ AI follows up automatically |\n| **Voice interviews** | ❌ | ✅ [Natural voice conversations](/docs/voice-interview-experience) |\n| **Methodology support** | ❌ | ✅ [Mom Test, JTBD, Discovery](/docs/choosing-a-methodology) |\n| **Analysis** | Google Sheets (manual) | [Automated themes, insights, reports](/docs/ai-generated-insights) |\n| **Research reports** | ❌ (manual spreadsheet work) | ✅ [Auto-generated, shareable](/docs/publishing-sharing-reports) |\n| **Branching logic** | Basic skip logic | Full AI adaptation |\n| **Google Workspace integration** | ✅ Native | ❌ (standalone) |\n| **Templates** | ✅ Many templates | AI generates the plan for you |\n| **Completion rates** | 10-30% | 60-80% |\n| **API & embed** | ✅ Basic | ✅ [Full REST API + embed widget](/docs/api-authentication) |\n| **AI integration** | ❌ | ✅ [Claude MCP](/docs/mcp-overview) |\n\n---\n\n## Where Google Forms Hits Its Ceiling\n\n### 1. The Spreadsheet Dead End\n\nEvery Google Forms response flows into Google Sheets. For 50 responses with multiple-choice answers, this is fine — you can see the charts and percentages. But the moment you add open-text fields to understand *why* people chose what they chose, you are staring at a spreadsheet of unstructured text with no way to identify patterns, themes, or priorities.\n\nKoji eliminates the spreadsheet entirely. Every conversation is automatically analyzed: [themes are identified](/docs/understanding-themes-patterns), sentiment is tagged, [quality is scored](/docs/how-the-quality-gate-works), and a [shareable research report](/docs/generating-research-reports) is generated — ready for your team and stakeholders.\n\n### 2. No Follow-Up Capability\n\nA Google Forms respondent writes: *\"The pricing page was confusing.\"* Great — but confusing how? Was it the pricing tiers? The feature comparison? The currency display? You will never know because you cannot ask follow-up questions.\n\nKoji's AI interviewer automatically probes: *\"You mentioned the pricing page was confusing. What specifically were you trying to figure out when you visited it?\"* — turning a vague complaint into an actionable insight.\n\n### 3. Zero Methodology Guardrails\n\nGoogle Forms gives you a blank canvas with no research methodology guidance. This leads to the most common mistake in customer research: asking leading and hypothetical questions.\n\n*\"Would you use our product if we added Feature X?\"* gets a 70% \"Yes\" in a Google Form — but that is a hypothetical answer to a leading question. Koji's [Mom Test methodology](/docs/mom-test-methodology) would instead explore: *\"Tell me about the last time you tried to accomplish [task]. What happened?\"* — surfacing whether Feature X solves a real problem or a hypothetical one.\n\n### 4. Completion Rates Are Declining\n\nGoogle Forms surveys suffer from the same fatigue affecting all surveys. Response rates have dropped below 5% for email-distributed surveys in many industries. The familiar Google Forms interface signals \"another survey\" — and people close the tab.\n\nKoji's conversational format achieves **60-80% completion rates** because it feels like a dialogue, not a checkbox exercise. Respondents feel heard, which produces both higher participation and more honest, detailed responses.\n\n---\n\n## The Upgrade Path: From Google Forms to Koji\n\nIf you are currently using Google Forms for research, the transition is natural:\n\n| What You Do in Google Forms | What Koji Does Instead |\n|---|---|\n| Write 15 questions manually | [Describe your research goal](/docs/writing-a-research-question), AI generates the interview plan |\n| Share a form link via email | [Share an interview link](/docs/sharing-your-interview-link) the same way |\n| Wait for spreadsheet responses | Conversations happen in real-time |\n| Manually read and code open-text fields | [Themes extracted automatically](/docs/understanding-themes-patterns) |\n| Build charts in Google Sheets | [Research reports generated automatically](/docs/generating-research-reports) |\n| Copy-paste findings into a slide deck | [Share publishable reports](/docs/publishing-sharing-reports) directly |\n\n---\n\n## When Google Forms Is Still the Right Choice\n\nGoogle Forms wins when:\n\n- You need a **completely free** solution with no limits\n- You are collecting **structured data** — event RSVPs, contact info, order forms\n- You need **Google Workspace integration** — responses flowing directly to Sheets, Drive, and Calendar\n- Your questions have **predefined answer categories** and you need simple aggregation\n- You are running a **quick poll** where depth does not matter\n- You need to **collaborate on form design** with your team in real-time via Google Workspace\n\n---\n\n## When to Upgrade to Koji\n\nMake the switch when:\n\n- You are adding open-text fields to every question because multiple choice is not enough\n- You are spending hours reading Google Sheets responses trying to find patterns\n- You want to understand **why** — not just what percentage chose Option B\n- Stakeholders keep asking follow-up questions your survey data cannot answer\n- Your response rates are dropping and you need a more engaging format\n- You want [voice interviews](/docs/voice-interview-experience) — people share 5-10x more when talking vs. typing in a form\n- You need [research methodology](/docs/choosing-a-methodology) to prevent biased questions\n\n---\n\n## Pricing Reality\n\n| | Google Forms | Koji Free | Koji Paid |\n|---|---|---|---|\n| Cost | Free forever | Free | See [plans](/pricing) |\n| Depth per response | Shallow | Deep (AI conversation) | Deep (AI conversation) |\n| Analysis included | ❌ (manual Sheets work) | ✅ Automated | ✅ Automated |\n| Time to insights | Hours of manual work | Minutes | Minutes |\n| Researcher required | You are the researcher | AI is the researcher | AI is the researcher |\n\nGoogle Forms is free in dollars but expensive in time. If you spend 4 hours analyzing Google Forms responses (a common scenario), that is $200-400 of researcher time at typical loaded costs. Koji's automated analysis makes the total cost of research — not just the tool cost — dramatically lower.\n\n---\n\n## Getting Started\n\n1. **[Create your account](/docs/creating-your-account)** — free, no credit card needed\n2. **[Quick Start Guide](/docs/quick-start-guide)** — first AI interview in 10 minutes\n3. **[Describe your research goal](/docs/writing-a-research-question)** — like you would to a colleague\n4. **[Share the link](/docs/sharing-your-interview-link)** — same distribution as your Google Form\n5. **[Review automated insights](/docs/insights-dashboard)** — no spreadsheet required\n\n---\n\n## Next Steps\n\n- **[AI Interviews vs. Surveys](/docs/ai-interviews-vs-surveys)** — Why conversations outperform forms\n- **[Koji vs. Typeform](/docs/koji-vs-typeform)** — Compare with another popular form tool\n- **[Koji vs. SurveyMonkey](/docs/koji-vs-surveymonkey)** — Compare with the survey market leader\n- **[The Mom Test](/docs/mom-test-methodology)** — How to ask questions that produce reliable answers\n- **[How to Write Great Interview Questions](/docs/writing-interview-questions)** — From survey questions to research conversations","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Google Forms — AI Interviews vs. Free Survey Forms | Koji","metaDescription":"Compare Koji and Google Forms for customer research. See how AI-powered interviews deliver 10x deeper insights than free survey forms — with automatic analysis, follow-up probing, and no spreadsheet work required.","keywords":["Koji vs Google Forms","Google Forms alternative","Google Forms alternative for research","best Google Forms alternative","Google Forms vs Koji","free survey tool alternative","upgrade from Google Forms","better than Google Forms for research","AI interview vs Google Forms","customer research beyond Google Forms"],"aiSummary":"Comparison of Koji (AI-powered interviews) vs Google Forms (free survey tool). Google Forms is free and simple for structured data collection but limited to fixed questions with manual spreadsheet analysis. Koji conducts adaptive AI conversations with automatic follow-up, methodology guardrails, and automated insight generation. Best for teams outgrowing Google Forms who need qualitative depth.","aiPrerequisites":["Experience using Google Forms or basic survey tools"],"aiLearningOutcomes":["Understand limitations of form-based research vs AI interviews","Compare the total cost of research (tool cost + analysis time)","Know when Google Forms is sufficient vs when to upgrade","Plan a transition from surveys to AI-powered interviews"],"aiDifficulty":"beginner","aiEstimatedTime":"10 minutes"},{"type":"documentation","id":"7c72a943-77b8-470b-a8e1-74d882454fed","slug":"koji-vs-outset","title":"Koji vs. Outset — Two AI Interview Platforms, Different Philosophies","url":"https://www.koji.so/docs/koji-vs-outset","summary":"Comparison of Koji (AI-native end-to-end research platform) vs Outset (enterprise AI moderation platform). Both conduct AI-powered interviews, but Koji offers AI-generated study design, named methodology guardrails, Claude MCP integration, developer APIs, and accessible pricing. Outset targets enterprise research teams with proven Fortune 500 deployments. Best for teams wanting fully automated, methodology-guided research.","content":"## The Short Answer\n\nKoji and Outset are both AI-powered interview platforms, but they take different approaches. **Outset** positions itself as an enterprise AI moderation layer for traditional research workflows — it augments human researchers with AI capabilities. **Koji** is AI-native end-to-end — from study creation through analysis, designed so anyone (PMs, founders, designers, researchers) can run deep qualitative research without specialized training or a research team.\n\n---\n\n## Philosophy Differences\n\n### Outset's Approach: AI-Assisted Research\n- Built for **enterprise research teams** who already have research infrastructure\n- AI moderates interviews but fits into existing research workflows\n- Focus on **augmenting** human researchers, not replacing the need for them\n- Enterprise pricing model (custom quotes, annual contracts)\n- Used by Fortune 500 companies (HubSpot, Nestlé, Glassdoor)\n\n### Koji's Approach: AI-Native Research\n- Built for **anyone who needs customer insights** — PMs, founders, researchers, designers\n- The [AI consultant](/docs/understanding-the-ai-consultant) creates the entire research plan from a plain-language goal\n- Focus on **democratizing** deep qualitative research\n- Accessible pricing with a free tier\n- [Claude MCP integration](/docs/mcp-overview) for AI-workflow-native research\n\n---\n\n## Feature Comparison\n\n| Capability | Outset | Koji |\n|-----------|--------|------|\n| **AI-moderated interviews** | ✅ | ✅ |\n| **Voice interviews** | ✅ | ✅ [AI voice conversations](/docs/voice-interview-experience) |\n| **Text interviews** | ✅ | ✅ [Text-based interviews](/docs/text-interview-experience) |\n| **Study design** | Manual (researcher designs) | [AI consultant generates plan](/docs/understanding-the-ai-consultant) |\n| **Methodology guardrails** | General moderation | [Mom Test, JTBD, Discovery, more](/docs/choosing-a-methodology) |\n| **Automated analysis** | ✅ AI analysis tools | ✅ [Themes, insights, sentiment, reports](/docs/ai-generated-insights) |\n| **Quality scoring** | Not detailed publicly | ✅ [Quality gate system](/docs/how-the-quality-gate-works) |\n| **Research reports** | ✅ | ✅ [Auto-generated, shareable](/docs/publishing-sharing-reports) |\n| **Embed widget** | ❌ Not detailed | ✅ [Embeddable on any website](/docs/using-the-embed-widget) |\n| **Headless API** | ❌ Not detailed | ✅ [Full headless mode](/docs/headless-api-overview) |\n| **MCP integration** | ❌ | ✅ [Claude MCP with 17 tools](/docs/mcp-tool-reference) |\n| **Participant panel** | ❌ BYO | BYO + [CSV import](/docs/importing-participants-csv) |\n| **Pricing** | Enterprise custom (est. $20K+/yr) | Free tier + accessible plans |\n| **Target user** | Enterprise research teams | Anyone — PMs, founders, researchers |\n| **Learning curve** | Moderate (research expertise helpful) | [10 minutes to first interview](/docs/quick-start-guide) |\n\n---\n\n## Where Koji Differentiates\n\n### 1. AI-Generated Study Design\n\nOutset requires researchers to design their interview guides, define question flows, and set up moderation parameters manually. This assumes research expertise.\n\nKoji's [AI consultant](/docs/understanding-the-ai-consultant) generates the entire [research brief](/docs/understanding-the-research-brief) from a plain-language description of what you want to learn. You say *\"I want to understand why enterprise customers churn after 3 months\"* — and Koji creates the methodology, question themes, probing guidelines, and interview flow. You can [edit the brief manually](/docs/editing-the-brief-manually) or let the AI handle it.\n\nThis makes qualitative research accessible to product managers, founders, and designers who may not have formal research training.\n\n### 2. Named Methodology Guardrails\n\nOutset offers general AI moderation that adapts to the conversation. Koji lets you [choose a specific research methodology](/docs/choosing-a-methodology) — and the AI enforces its principles throughout:\n\n- **[Mom Test](/docs/mom-test-methodology):** Focuses on past behavior, avoids hypothetical and leading questions\n- **[Jobs-to-be-Done](/docs/jobs-to-be-done-interviews):** Surfaces the progress users are trying to make and the forces driving switching behavior\n- **Discovery:** Open exploration of problem spaces\n- **Validation:** Structured evaluation of specific hypotheses\n\nThis is not just a label — the AI's questioning strategy, follow-up approach, and probing depth all change based on the selected methodology.\n\n### 3. Claude MCP Integration\n\nKoji is the only AI interview platform with a full [Model Context Protocol integration](/docs/mcp-overview) — [17 tools](/docs/mcp-tool-reference) that let you run research entirely through Claude:\n\n- Create studies conversationally\n- Import participants\n- Monitor interview progress\n- Analyze results and generate reports\n- Build [continuous discovery pipelines](/docs/continuous-discovery-with-mcp)\n\nWorkflow guides exist for [product managers](/docs/mcp-workflow-product-managers), [researchers](/docs/mcp-workflow-researchers), and [founders](/docs/mcp-workflow-founders-gtm).\n\n### 4. Developer-Friendly Infrastructure\n\nKoji offers a full [REST API](/docs/api-authentication), [embed widget](/docs/using-the-embed-widget), [headless mode](/docs/headless-api-overview), and [webhook system](/docs/webhook-setup) — making it possible to integrate AI interviews into any product, app, or workflow programmatically. This opens use cases like:\n\n- In-app user research triggered by events\n- Post-purchase feedback flows\n- Continuous NPS replacement\n- Customer onboarding research at scale\n\n### 5. Accessible Pricing\n\nOutset targets enterprise buyers with custom pricing (estimated $20,000+/year based on market positioning). Koji offers a **free tier** with paid plans that make qualitative research accessible to startups, solo founders, and lean teams — not just enterprise research departments.\n\n---\n\n## When Outset May Be the Better Choice\n\nOutset may win when:\n\n- You are an **enterprise research team** with existing workflows that need AI augmentation\n- You want a platform with **proven Fortune 500 case studies** (HubSpot, Nestlé, Glassdoor)\n- You prefer to **design your own interview guides** from scratch with full manual control\n- Your organization requires **enterprise procurement processes** with dedicated account management\n- You value being established — Outset has been in market longer with more enterprise deployments\n\n---\n\n## When to Choose Koji\n\nChoose Koji when:\n\n- You want **AI to handle the entire workflow** — from study design to analysis\n- You need **non-researchers** (PMs, founders, designers) to run quality research\n- You want to start **today** with a free tier, not after an enterprise procurement cycle\n- You need **named methodology guardrails** — not just general AI moderation\n- You want [Claude MCP integration](/docs/mcp-setup-claude) for AI-native workflows\n- You need **developer tools** — API, embed widget, headless mode, webhooks\n- You are building [continuous discovery](/docs/continuous-discovery-with-mcp) into your product process\n- **Budget accessibility** matters — free tier + affordable plans vs. enterprise pricing\n\n---\n\n## Getting Started\n\n1. **[Create your account](/docs/creating-your-account)** — free, instant access\n2. **[Quick Start Guide](/docs/quick-start-guide)** — first AI interview in 10 minutes\n3. **[Choose a methodology](/docs/choosing-a-methodology)** — Mom Test, JTBD, or Discovery\n4. **[Share your interview link](/docs/sharing-your-interview-link)** — participants start immediately\n5. **[Review AI insights](/docs/insights-dashboard)** — themes, quality scores, and reports\n\n---\n\n## Next Steps\n\n- **[AI Interviews vs. Surveys](/docs/ai-interviews-vs-surveys)** — Why conversations beat forms\n- **[Koji vs. UserTesting](/docs/koji-vs-usertesting)** — Compare with the enterprise usability platform\n- **[Koji vs. Qualtrics](/docs/koji-vs-qualtrics)** — Compare with the enterprise survey suite\n- **[MCP Tool Reference](/docs/mcp-tool-reference)** — All 17 Claude integration tools\n- **[Continuous Discovery](/docs/continuous-discovery-with-mcp)** — Always-on research pipeline","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Outset — AI-Native Research vs. Enterprise AI Moderation | Koji","metaDescription":"Compare Koji and Outset for AI-powered interviews. See how Koji's AI-native approach with methodology guardrails, Claude MCP integration, and accessible pricing differs from Outset's enterprise-focused AI moderation platform.","keywords":["Koji vs Outset","Outset alternative","best Outset alternative","Outset vs Koji","AI interview platform comparison","Outset.ai alternative","AI moderated research comparison","best AI interview tool","AI qualitative research platform","automated interview tool comparison"],"aiSummary":"Comparison of Koji (AI-native end-to-end research platform) vs Outset (enterprise AI moderation platform). Both conduct AI-powered interviews, but Koji offers AI-generated study design, named methodology guardrails, Claude MCP integration, developer APIs, and accessible pricing. Outset targets enterprise research teams with proven Fortune 500 deployments. Best for teams wanting fully automated, methodology-guided research.","aiPrerequisites":["Familiarity with AI interview tools","Understanding of qualitative research"],"aiLearningOutcomes":["Understand AI-native vs AI-augmented research approaches","Compare methodology support and guardrails","Evaluate Claude MCP integration capabilities","Choose between enterprise and accessible pricing models"],"aiDifficulty":"intermediate","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"8e1a80d9-f226-4eab-af2a-25009e572ab0","slug":"koji-vs-sprig","title":"Koji vs. Sprig — Deep Conversational Interviews vs. In-Product Micro-Surveys","url":"https://www.koji.so/docs/koji-vs-sprig","summary":"Koji and Sprig are both AI-powered research platforms serving different use cases. Sprig captures in-product micro-survey responses and video snippets from active users; Koji conducts in-depth voice and text interviews with anyone, including prospects and churned customers. This article compares research depth, setup complexity, voice capabilities, and ideal use cases to help teams choose the right platform.","content":"Koji and Sprig are both AI-powered research platforms, but they solve different problems. Sprig is built for in-product research — intercepting users while they're actively using your software to collect micro-survey responses and video snippets. Koji is built for in-depth conversational interviews — AI-powered voice and text conversations that explore the *why* behind user behavior, conducted asynchronously with anyone, anywhere.\n\n## Quick Comparison\n\n| | **Koji** | **Sprig** |\n|---|---|---|\n| **Research type** | In-depth AI conversations | In-product micro-surveys + video |\n| **Depth per response** | High (full conversation, 15–30 min) | Low–medium (2–5 question intercept) |\n| **Interview modes** | Voice + text conversations | In-app surveys + video snippets |\n| **Follow-up questions** | AI adapts in real-time to each response | Fixed question flow |\n| **Who can participate** | Anyone (customers, prospects, churned users) | Active product users only |\n| **Report generation** | Full AI research reports with themes + quotes | AI synthesis of survey and replay data |\n| **Technical setup** | No-code, zero installation | SDK or JavaScript snippet required |\n| **Best for** | Deep discovery, qualitative insight | In-product feedback, usage patterns |\n\n## What Sprig Does Well\n\nSprig excels at in-the-moment feedback from active users. Because it intercepts participants while they're inside your product, responses reflect real-time experience rather than recalled behavior — which reduces memory bias for task-specific questions.\n\nSprig's session replay integration is a genuine differentiator: you can watch a user's actual product interactions alongside their survey responses, directly connecting behavioral data with attitudinal feedback. For teams with mature data stacks, Sprig's product analytics integrations surface patterns that would take days to identify manually.\n\nFor teams that need a continuous stream of lightweight feedback on specific product areas — onboarding flows, new features, feature discovery — Sprig's in-product model makes collection frictionless. Users don't have to leave the product to participate.\n\n## What Koji Does Better\n\n**Depth of insight.** Sprig's intercept model is built for short surveys — 2–5 quick questions designed not to disrupt the user experience. That constraint is fundamental to the product. Koji's AI conducts full 15–30 minute conversational interviews where the AI asks follow-up questions based on what each participant says. The difference in qualitative depth is substantial.\n\n**Reaching beyond your active user base.** Sprig can only reach users who are actively inside your product. Koji can interview anyone: churned customers, prospects who evaluated but didn't buy, competitors' users, people who've never heard of you. When you need to understand why people didn't adopt, what competitors are missing, or what an entirely new segment needs, Koji reaches audiences that Sprig cannot.\n\n**Voice interviews.** Koji supports voice mode — AI-powered conversations that participants join like a phone call, speaking naturally while the AI listens, follows up, and probes. Voice interviews tend to produce richer qualitative data than text because people express nuance and emotion more freely in speech. Sprig is a text-only platform.\n\n**Zero technical installation.** Koji is a no-code platform — you describe your research goal to the AI consultant, review the generated brief, publish the study, and share a link. No engineering involvement, no SDK, no product deployment cycle. For non-technical teams or early-stage products, Koji means getting to first insight in under 10 minutes.\n\n**Research methodology support.** Koji is purpose-built for methodology-driven research. It supports Jobs-to-Be-Done, Mom Test, Customer Discovery, empathy interviews, and other structured frameworks. The AI consultant guides you through setting up a methodologically sound study. Sprig's model is closer to a product analytics layer than a research platform.\n\n**Shareable research artifacts.** Koji generates full research reports designed for stakeholder communication: themes, patterns, representative quotes, and AI-synthesized recommendations. These are research deliverables in the traditional sense. Sprig's outputs are analytics dashboards — useful for internal monitoring, but not designed for the \"here's what we learned from 50 customer conversations\" report a research team would present.\n\n## Who Should Use Which\n\n**Choose Sprig if:**\n- Your primary need is in-product feedback captured at the moment of behavior\n- You want to connect survey responses to session replays and product analytics\n- You're focused on understanding active users inside a specific product flow\n- Your team has engineering resources to manage SDK installation and maintenance\n\n**Choose Koji if:**\n- You need deep qualitative insight into user motivations, decision processes, or unmet needs\n- You want to interview people outside your product (prospects, churned users, competitive users)\n- You're running Customer Discovery, Jobs-to-Be-Done, or another structured research methodology\n- You want voice interview capability alongside text\n- You need research-ready reports designed for stakeholder communication\n- You don't have engineering resources and need a no-code setup\n- You're an early-stage team that needs to talk to customers before you have a product\n\n**Use both if:**\n- You're a mature research team that uses in-product feedback for continuous quantitative signal and depth interviews for strategic qualitative understanding\n- Sprig tells you *what* users are doing; Koji tells you *why* they make the decisions they do\n\n## Pricing and Accessibility\n\nSprig's pricing is enterprise-oriented and typically requires direct contact for most plans. Koji offers a free tier for getting started and transparent self-serve plans at multiple price points — making it accessible for solo researchers, startups, and teams without enterprise research budgets.\n\nFor teams evaluating AI research platforms, Koji's no-code study design, voice interview capability, and full research report generation offer a broader capability set for qualitative discovery — while Sprig's in-product model serves a more specific (but genuinely valuable) use case in behavioral product research.\n\n## Tips & Best Practices\n\n- **For early-stage teams**: Start with Koji. Before you have an active user base to intercept, Koji lets you interview prospects, competitors' customers, and early adopters to shape your product direction.\n- **For growth-stage teams**: Combine both. Use Sprig's in-product surveys for ongoing quantitative signal; use Koji for deep discovery on your most important strategic questions.\n- **For enterprise research teams**: Koji's methodology support and report generation fit naturally into existing research workflows. Sprig integrates better with product analytics stacks.\n\n## Related Articles\n\n- [AI Interviews vs. Surveys — Why Conversations Beat Forms](/docs/ai-interviews-vs-surveys)\n- [Koji vs. Typeform — When You Need Depth](/docs/koji-vs-typeform)\n- [Koji vs. UserTesting](/docs/koji-vs-usertesting)\n- [Voice Interview Experience](/docs/voice-interview-experience)\n- [Headless API Overview](/docs/headless-api-overview)\n\n## Frequently Asked Questions\n\n**Q: Do Koji and Sprig compete directly?**\nA: Partially. Both are AI research platforms, but they occupy different niches. Sprig focuses on in-product micro-surveys and behavioral data; Koji focuses on in-depth conversational interviews. Many mature research teams use both — Sprig for continuous product feedback, Koji for deep discovery.\n\n**Q: Does Koji offer in-product surveys like Sprig?**\nA: Koji offers an embed widget and headless API to initiate interviews from within your product, but these are full conversations (15–30 minutes) rather than micro-surveys. If you need lightweight in-product feedback with minimal user disruption, Sprig is purpose-built for that.\n\n**Q: Can Koji replace Sprig?**\nA: For in-the-moment product feedback with session replay, no. But for customer discovery, strategic qualitative insight, and research outside your active user base, Koji offers capabilities that Sprig does not match.\n\n**Q: Which platform is easier to set up?**\nA: Koji is significantly faster for most teams. You can design a study and start collecting responses in under 10 minutes with no technical installation. Sprig requires SDK installation involving engineering time and product deployment cycles.\n\n**Q: Does Koji have voice research like Sprig video snippets?**\nA: Yes, but differently. Sprig captures short video snippets of users in your product. Koji offers full voice interviews where the AI adapts follow-up questions in real time. Sprig shows what users do; Koji reveals what they think and why.","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Koji vs. Sprig — Koji Docs","metaDescription":"Koji vs Sprig: in-depth AI interviews vs in-product micro-surveys. Compare research depth, setup, voice mode, and pricing to choose the right tool.","keywords":["Koji vs Sprig","Sprig alternative","in-product research tool","AI user research comparison","Sprig competitor","user research platform comparison","AI interview tool"],"aiSummary":"Koji and Sprig are both AI-powered research platforms serving different use cases. Sprig captures in-product micro-survey responses and video snippets from active users; Koji conducts in-depth voice and text interviews with anyone, including prospects and churned customers. This article compares research depth, setup complexity, voice capabilities, and ideal use cases to help teams choose the right platform.","aiPrerequisites":["ai-interviews-vs-surveys"],"aiLearningOutcomes":["Understand the core differences between in-product micro-surveys and in-depth AI interviews","Identify which platform fits your research objective and technical context","Decide when to use Koji, Sprig, or both in a research program"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"6ad02ef2-4004-4667-814c-9f54c7050c1d","slug":"qualitative-research-software","title":"Qualitative Research Software: How to Choose the Right Tool for Your Team","url":"https://www.koji.so/docs/qualitative-research-software","summary":"This guide maps every major category of qualitative research software — research repositories, moderated platforms, unmoderated testing tools, survey platforms, AI-moderated interview platforms, and CAQDAS — explaining what each covers in the research pipeline and how to evaluate them. It includes a feature comparison matrix and an evaluation framework based on identifying your team's actual research bottleneck.","content":"Choosing the right qualitative research software depends on where your research bottleneck actually is. Some teams need better analysis tools. Others need help collecting data at scale. Still others need to run more research with fewer researchers. Each bottleneck points to a different type of tool — and buying the wrong one doesn't solve the real problem.\n\nThis guide maps the major categories of qualitative research software, what each type does, and how to evaluate them against your actual research workflow.\n\n## What Is Qualitative Research Software?\n\nQualitative research software helps teams collect, organize, analyze, and share insights from non-numerical data: interviews, focus groups, ethnographic observation, open-ended surveys, and usability sessions.\n\nThe category has expanded significantly in recent years. Traditional tools focused on analysis — helping researchers code transcripts after interviews were already completed. Modern AI-native tools now cover the full research lifecycle — from study design and participant recruitment to AI-moderated interviews and automatic report generation.\n\nUnderstanding which part of the pipeline a tool covers is essential to making the right choice.\n\n## The Qualitative Research Pipeline\n\nEvery qualitative research project moves through five stages:\n\n1. **Study Design**: Defining objectives, methodology, and interview questions\n2. **Participant Recruitment**: Finding and screening the right participants\n3. **Data Collection**: Conducting interviews, sessions, or observations\n4. **Analysis**: Coding, theming, and synthesizing findings\n5. **Reporting**: Sharing insights with stakeholders\n\nMost software categories address 1-2 of these stages. Only a handful of AI-native platforms cover all five end-to-end.\n\n## Categories of Qualitative Research Software\n\n### 1. Research Repositories\n\n**What they do**: Store and organize research artifacts (transcripts, recordings, insights) for team access and cross-study analysis.\n\n**Best for**: Teams doing high-volume research who need a shared source of truth accessible to the whole organization.\n\n**Examples**: Dovetail, Aurelius, EnjoyHQ\n\n**Key features**: Tagging, search, insight boards, highlight reels, team collaboration, cross-study pattern detection\n\n**Limitation**: Repositories assume you're already doing research. They don't help you collect it faster, conduct interviews, or generate analysis automatically.\n\n### 2. Moderated Research Platforms\n\n**What they do**: Facilitate live, human-moderated research sessions — video interviews, usability tests, focus groups.\n\n**Best for**: Complex exploratory research requiring a skilled moderator to follow unexpected threads in real-time.\n\n**Examples**: Lookback, Grain, UserZoom\n\n**Key features**: Screen recording, live annotation, session replay, participant management, AI transcription\n\n**Limitation**: Requires scheduling, a trained moderator, and significant researcher time per session. Doesn't scale beyond 8-10 sessions per researcher per week.\n\n### 3. Unmoderated Testing Tools\n\n**What they do**: Run self-guided usability tests without a live moderator — participants complete tasks on their own.\n\n**Best for**: Testing specific UI flows or prototypes at scale, when you have structured tasks to evaluate.\n\n**Examples**: Maze, Lyssna, Optimal Workshop\n\n**Key features**: Task-based flows, click tracking, short-answer questions, quantitative completion metrics\n\n**Limitation**: Limited to structured tasks. Can't explore open-ended \"why\" questions or follow unexpected threads. More survey than interview.\n\n### 4. Survey Platforms\n\n**What they do**: Collect structured, quantitative feedback from large samples at low cost per response.\n\n**Best for**: Measuring satisfaction at scale, quantifying behaviors, validating hypotheses with statistical significance.\n\n**Examples**: Typeform, SurveyMonkey, Qualtrics, Google Forms\n\n**Key features**: Question logic and branching, response analytics, panel access, integrations\n\n**Limitation**: Fixed-format questions can't follow up on interesting answers. High completion rates, low insight depth. You get what you asked for — nothing more.\n\n### 5. AI-Moderated Interview Platforms\n\n**What they do**: Conduct AI-powered conversations that combine the depth of human interviews with the scalability of surveys.\n\n**Best for**: Teams that need conversational depth at scale, without requiring a trained moderator for every session.\n\n**Examples**: Koji, Outset.ai\n\n**Key features**: Voice + text interview modes, dynamic follow-up questions, automatic transcription, AI-powered theme extraction, report generation, study design AI\n\n**Why this category is growing**: Traditional research tools force a tradeoff between scale (surveys) and depth (interviews). AI interview platforms eliminate that tradeoff — you get rich, exploratory conversations from every participant, automatically analyzed into aggregate insights.\n\nKoji is the leading end-to-end platform in this category. It covers the full research pipeline: an AI consultant helps you design your study, AI interviewers conduct voice or text conversations according to your research brief, and automatic analysis generates themes, sentiment scores, and a shareable report — all without researcher involvement in the actual interview.\n\n### 6. CAQDAS (Computer-Assisted Qualitative Data Analysis Software)\n\n**What they do**: Provide advanced coding and analysis tools for academic and enterprise qualitative research requiring methodological rigor.\n\n**Best for**: Academic researchers, large qualitative datasets, peer-reviewed publications, mixed-methods studies.\n\n**Examples**: NVivo, ATLAS.ti, MAXQDA, Dedoose\n\n**Key features**: Open/axial/selective coding, memos, matrix analysis, mixed-methods support, audit trails\n\n**Limitation**: Steep learning curve and high cost. Designed for academic rigor rather than business velocity. Not practical for teams needing weekly insights.\n\n## How to Choose Qualitative Research Software\n\n### Start with your bottleneck\n\n| If your team struggles with... | You need... |\n|-------------------------------|-------------|\n| Running enough research | AI-moderated interviews (scale without moderators) |\n| Analyzing large volumes of transcripts | AI analysis + repository |\n| Sharing insights with stakeholders | Repository + reporting tools |\n| Testing specific UI flows | Unmoderated testing tool |\n| Academic-grade rigor | CAQDAS |\n| Large-scale quantitative validation | Survey platform |\n\n### Ask these evaluation questions\n\n**1. Does it cover my full pipeline or just one stage?**\nFull-pipeline tools reduce context-switching and the data loss that happens when you export from one tool and import into another.\n\n**2. How does it handle follow-up questions?**\nIf the tool uses static questions, you're doing a survey, not an interview. Dynamic follow-ups are what separate conversational insight from form-based data collection.\n\n**3. What analysis does it automate?**\nCount the hours your team spends manually coding transcripts. That's the time you're buying back with AI analysis.\n\n**4. Can stakeholders access findings without a research presentation?**\nShareable, interactive reports reduce the researcher's presentation burden and increase the frequency with which research actually influences decisions.\n\n**5. Does it support voice interviews?**\nVoice captures tone, hesitation, and emotion that text cannot. For sensitive or complex topics, voice interviews consistently produce richer qualitative data.\n\n## Comparing Qualitative Research Software\n\n| Capability | Koji | Dovetail | NVivo | Maze | Typeform |\n|------------|------|----------|-------|------|----------|\n| AI interview moderation | ✓ | — | — | — | — |\n| Voice interviews | ✓ | — | — | — | — |\n| Dynamic follow-up questions | ✓ | — | — | — | — |\n| Auto-transcription | ✓ | ✓ | — | — | — |\n| AI theme extraction | ✓ | ✓ | Partial | — | — |\n| Auto-generated reports | ✓ | — | — | — | — |\n| Research repository | — | ✓ | ✓ | — | — |\n| Usability testing | — | — | — | ✓ | — |\n| Study design AI | ✓ | — | — | — | — |\n| Shareable reports | ✓ | ✓ | — | ✓ | ✓ |\n| No scheduling required | ✓ | — | — | ✓ | ✓ |\n\nFor teams focused on conversational depth, AI analysis, and research velocity, Koji is the strongest option. For teams primarily managing and organizing existing research artifacts, Dovetail is a strong complement. For academic research requiring methodological audit trails, CAQDAS tools remain the standard.\n\n## The Shift to AI-Native Research\n\nThe qualitative research software market is undergoing a fundamental shift. Legacy tools were built around the assumption that researchers would conduct all interviews manually and needed software to help organize and analyze the results afterward.\n\nAI-native tools like Koji are built around a different assumption: that AI can conduct the interviews themselves — at any scale, at any time — while researchers focus on study design and insight interpretation.\n\nAccording to a 2024 analysis, AI-augmented research workflows are significantly faster than traditional methods while producing comparable or higher-quality insights for structured research questions. Research teams adopting AI moderation report running 5-10x more studies per researcher per year without increasing headcount.\n\nThe practical implication: teams that were doing 2-3 research studies per quarter can now run continuous research pipelines generating weekly insights. The bottleneck shifts from \"we don't have time to do research\" to \"we need to make sure we're acting on what we learn.\"\n\n## Pricing Considerations\n\nQualitative research software pricing models vary significantly:\n\n- **Per-seat**: Charged per researcher account (repositories, CAQDAS)\n- **Per-interview**: Charged per completed participant session (AI interview platforms)\n- **Per-response**: Charged per survey completion (survey platforms)\n- **Usage-based**: Charged by feature use, transcription minutes, or storage\n\nWhen evaluating total cost, factor in the hidden costs of manual work: researcher time spent on moderation, transcription, coding, and report writing. AI platforms that eliminate these steps often have lower total cost than cheaper tools that leave the work to the researcher.\n\nKoji offers a free tier to get started, with paid plans scaling by interview volume — so you pay for research you actually run, not seats that may go unused.\n\n## Tips & Best Practices\n\n- **Audit your current workflow before buying** — map out where researcher time actually goes before selecting a tool\n- **Run a pilot study before committing** — most platforms offer trials; test with a real research question, not a demo scenario\n- **Involve your team in the decision** — tools that researchers don't use don't generate ROI\n- **Consider integration requirements** — does the tool connect to your existing stack (Slack, Notion, Jira, CRM)?\n- **Think about participant experience** — the best tool for researchers is useless if participants find it confusing or intrusive\n\n## Frequently Asked Questions\n\n**What is the best qualitative research software for startups?**\nStartups typically need to move fast, run research with small or no research teams, and get insights into product decisions quickly. Koji is ideal for this scenario — its AI consultant accelerates study design, AI moderation removes the need for trained interviewers, and automatic reports mean insights are ready in hours rather than weeks. The free tier supports enough studies to run continuous customer discovery from day one.\n\n**Is qualitative research software worth the cost?**\nThe ROI depends on the cost of bad decisions, not the cost of the software. A single product decision made without user input that requires a 6-week engineering sprint to reverse costs far more than a year of research software. Teams typically find positive ROI within the first study when research prevents or meaningfully improves a significant product decision.\n\n**Can qualitative research software replace human researchers?**\nAI handles the mechanical work of research — moderation, transcription, analysis — effectively. Human researchers add value in study design, hypothesis formation, contextual interpretation, and organizational navigation. The best teams use software to multiply researcher capacity, not eliminate researchers.\n\n**How does qualitative research software handle data privacy?**\nStandards vary by vendor. Enterprise-grade platforms like Koji offer GDPR-compliant consent flows, participant anonymization options, and data deletion on request. Always verify compliance requirements (GDPR, HIPAA, SOC2) before selecting a platform for regulated industries.\n\n**What is the difference between qualitative and quantitative research software?**\nQuantitative research software handles numerical data: percentages, ratings, statistics, significance tests. Qualitative research software handles non-numerical data: words, themes, narratives, emotional context. The best research programs use both — quantitative tools tell you what is happening; qualitative tools tell you why.\n\n---\n\n## Related Comparisons\n\n- [Best User Research Tools](/docs/best-user-research-tools-2026) — Full tool landscape\n- [Koji vs. Dovetail](/docs/koji-vs-dovetail) — Analysis tool comparison\n- [Koji vs. Qualtrics](/docs/koji-vs-qualtrics) — Enterprise platform comparison\n- [Best Survey Alternatives](/docs/best-survey-alternatives-2026) — Beyond traditional surveys\n- [How to Analyze Qualitative Data](/docs/how-to-analyze-qualitative-data) — Analysis methodology\n\n*See how [structured questions](/docs/structured-questions-guide) give you both collection and analysis in one platform.*","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Qualitative Research Software Guide — Koji","metaDescription":"Compare every category of qualitative research software — from CAQDAS to AI interview platforms. Find the right tool based on your actual research bottleneck.","keywords":["qualitative research software","best qualitative research tools","qualitative data analysis software","user research software","qualitative analysis tools","research software comparison","AI research platform"],"aiSummary":"This guide maps every major category of qualitative research software — research repositories, moderated platforms, unmoderated testing tools, survey platforms, AI-moderated interview platforms, and CAQDAS — explaining what each covers in the research pipeline and how to evaluate them. It includes a feature comparison matrix and an evaluation framework based on identifying your team's actual research bottleneck.","aiPrerequisites":["Basic familiarity with user research workflows"],"aiLearningOutcomes":["Identify which stage of the research pipeline is your team's biggest bottleneck","Understand the key differences between major software categories","Evaluate tools using the right criteria for your workflow","Understand the tradeoffs between depth (interviews) and scale (surveys)","Make an informed qualitative research software decision for your team"],"aiDifficulty":"beginner","aiEstimatedTime":"10 min read"},{"type":"documentation","id":"444f575f-8c97-4dba-8f8e-d537fd2a8efe","slug":"focus-group-alternatives","title":"The Best Focus Group Alternatives in 2026: Why AI Interviews Are Replacing Traditional Group Research","url":"https://www.koji.so/docs/focus-group-alternatives","summary":"Focus groups cost $4,000–$12,000 per session, take 3–6 weeks, and suffer from groupthink bias. AI-moderated interview platforms like Koji offer a superior alternative: individual interviews at scale, conducted asynchronously, with automatic thematic analysis and quantitative aggregation. Other alternatives include async user interviews, online research communities, human-moderated depth interviews, and diary studies. Focus groups retain value only for group dynamics research, brand perception in social contexts, and creative co-creation sessions.","content":"\n# The Best Focus Group Alternatives in 2026: Why AI Interviews Are Replacing Traditional Group Research\n\nFocus groups had a good run.\n\nFrom their origins in 1940s market research to their peak in the 1990s consumer insights boom, focus groups became the default way to understand what groups of people think about products, brands, and experiences. The format seemed logical: get 8–12 people in a room, have a skilled moderator guide a discussion, and watch as group dynamics surface insights you could never get one-on-one.\n\nThe problem is that's not actually how it works.\n\n## Why Focus Groups Are Falling Short\n\nFocus groups are expensive, slow, and riddled with the exact biases they're supposed to prevent.\n\n**Cost:** A professional focus group facility, moderator, recruiting, and participant incentives typically costs between $4,000 and $12,000 per session. For global companies, that multiplies per market. For startups and scale-ups, it's simply inaccessible.\n\n**Time:** From recruiting to report delivery, a single focus group round takes 3–6 weeks. By then, the product decision has often already been made.\n\n**Groupthink:** The most well-documented problem with focus groups is that group dynamics systematically bias individual responses. Dominant participants shape the conversation. Social conformity pressure causes people to align with the group rather than share genuine views. Quiet participants — often the ones with valuable dissenting opinions — stay silent. You end up with the opinions of the loudest three people, dressed up as group consensus.\n\n**Geographic and scheduling limitations:** Assembling a specific type of participant in a specific place at a specific time is logistically brutal. B2B focus groups in particular fail here — getting senior buyers from different companies into a room simultaneously is nearly impossible.\n\n**Moderator dependency:** The quality of a focus group is almost entirely a function of the moderator's skill. Even experienced moderators ask leading questions, follow interesting tangents at the expense of research objectives, and project their own assumptions onto participant responses. A bad moderator can invalidate an entire study.\n\nThis is not an argument that focus groups are always wrong — there are specific contexts where they remain valuable. But for most product research, UX research, and customer insight work, there are better alternatives that are faster, cheaper, and less biased.\n\n## The 5 Best Focus Group Alternatives in 2026\n\n### 1. AI-Moderated Individual Interviews (The Modern Default)\n\nAI-moderated research platforms conduct one-on-one interviews at scale, using AI to ask questions, probe follow-ups, adapt to participant responses, and generate automated analysis — without scheduling overhead, geographic constraints, or moderator dependency.\n\nWhat makes this the top alternative to focus groups:\n\n**No groupthink.** Each participant speaks independently without social pressure. You get what they actually think, not what the group made them think. Research consistently shows that individual interviews surface more honest, nuanced responses than group settings — and AI-moderated platforms like Koji make running 30 individual interviews as easy as running one.\n\n**Scale without proportional cost.** Running 50 AI interviews costs a fraction of one focus group session and gives you exponentially more individual data points. Where a focus group gives you the diluted consensus of 10 people, 50 AI interviews give you 50 genuine perspectives you can analyze for patterns.\n\n**24/7 availability.** Participants complete interviews on their own schedule — removing the single biggest focus group friction point. A busy B2B buyer who could never clear two hours for an in-person session can complete a 15-minute AI interview between meetings on a Tuesday morning.\n\n**Automatic analysis.** Themes, patterns, and representative quotes are surfaced automatically. No transcription cost, no affinity mapping sessions, no synthesis workshops.\n\n**Both voice and text.** With Koji, participants choose whether to speak their answers or type them — making the format accessible to different participant preferences, devices, and contexts.\n\nIn Koji specifically, you can run text interviews (chat-based with interactive widgets for structured questions) or voice interviews (fully conversational, no typing required). The AI uses your research brief — which includes your problem statement, target participant criteria, methodology framework, and key questions — to conduct every interview with consistent rigor that would be impossible to maintain across a human moderator team.\n\nStructured question types (scale, single choice, multiple choice, ranking, yes/no, and open-ended) let you capture both quantitative data and qualitative depth in the same session. This is something focus groups simply cannot do: aggregate numeric NPS scores across 40 participants while simultaneously capturing each individual's reasoning in their own words.\n\n### 2. Asynchronous User Interviews\n\nAsync interviews give participants a set of questions to respond to on their own time, typically via video or audio recording. Platforms in this category allow researchers to review responses without scheduling live sessions.\n\nThe advantage over focus groups: async research works across time zones, for busy participants who cannot commit to a 2-hour session, and eliminates social influence entirely. Participants reflect before answering, often producing more considered responses than they would in a time-pressured group setting.\n\nThe limitation compared to AI-moderated interviews: async video interviews require more editing effort, lack adaptive follow-up probing, and can feel impersonal. AI-moderated platforms combine the async convenience with the adaptive depth of a live interview.\n\n### 3. Online Research Communities and Panels\n\nOnline communities — whether purpose-built research panels or existing customer communities — allow longitudinal research (tracking the same participants over time) and large-scale surveys embedded in natural environments.\n\nThe advantage: high participant engagement from existing community members; great for ongoing feedback loops where you want consistent respondents over months.\n\nThe limitation: community members are often your most engaged customers, introducing significant selection bias. You may miss the perspective of churned users, passive users, or non-customers who represent your growth opportunity.\n\n### 4. One-on-One Depth Interviews (Human-Moderated)\n\nTraditional 1:1 interviews with a human moderator remain one of the most powerful research methods. Without group dynamics, participants speak more freely, follow unexpected threads, and share personal details they would never voice in a group.\n\nThe limitation: human-moderated interviews are expensive (researcher time, scheduling, transcription), slow (typically 6–8 per week at scale), and nearly impossible to run without a dedicated research team. They also introduce moderator bias in ways that are difficult to detect or control.\n\nAI-moderated interview platforms resolve most of these limitations — giving you the depth of 1:1 interviews with the speed and scalability of a digital survey, and the consistency of a standardized protocol.\n\n### 5. Diary Studies\n\nDiary studies ask participants to log their behaviors, thoughts, or feelings over an extended period — usually 1–2 weeks. This captures behavior in context rather than in recall, which is especially valuable for understanding daily habits or usage patterns that participants struggle to articulate in a one-off session.\n\nThe limitation: diary studies require significant participant motivation to complete consistently over time, and analysis is time-intensive without automation tools.\n\n## When Focus Groups Still Make Sense\n\nFocus groups are not always the wrong tool — they are often misapplied. There are contexts where group dynamics are the research objective rather than a confound:\n\n**Group decision-making research.** If you are studying how buying committees make enterprise purchasing decisions, observing actual group dynamics is the point, not a problem.\n\n**Brand perception in social contexts.** When you need to understand how a brand is discussed socially — shared meanings, tribal associations, cultural resonance — group settings replicate that social context in a way individual interviews cannot.\n\n**Creative co-creation sessions.** Brainstorming sessions where group energy is productive (ideation workshops, product naming sessions, concept generation) can leverage focus group formats effectively.\n\nEven in these cases, combining a focus group with individual AI interviews conducted before or after often produces stronger insights than the group session alone — giving you both the social dynamic and the individual baseline.\n\n## Comparing Focus Groups vs. AI Interviews: Head to Head\n\n| Dimension | Traditional Focus Group | AI-Moderated Interview (Koji) |\n|---|---|---|\n| **Cost per round** | $4,000–$12,000 | €30–150 for 30 interviews |\n| **Time to insights** | 3–6 weeks | 24–72 hours |\n| **Groupthink risk** | High | None (individual sessions) |\n| **Geographic reach** | Local or expensive | Global, any language |\n| **Scalability** | 1 session at a time | Hundreds simultaneously |\n| **Quantitative data** | Limited | Built-in (scale, choice, ranking, yes/no) |\n| **Qualitative depth** | Moderate (group dilution) | High (individual + AI probing) |\n| **Automated analysis** | No | Yes |\n| **Participant burden** | 2-hour commitment, travel | 15 minutes, any device, any time |\n| **Moderator skill dependency** | Critical | None |\n\n## How to Replace Your Next Focus Group with AI Interviews\n\nGetting started with AI-moderated research as a focus group replacement takes less than an hour:\n\n**1. Create a study in Koji.** Define your problem context — what decision are you trying to inform? What do you already know? What hypothesis are you testing?\n\n**2. Set your methodology.** Koji supports built-in research frameworks including Customer Discovery, Jobs to Be Done, and the Mom Test. Choose the one that fits your research goal, or customize the interview principles directly in your brief.\n\n**3. Write your key questions.** Mix structured question types to capture both quantitative and qualitative data. A well-designed 8-question study might include 2 scale questions (for satisfaction or NPS), 2 yes/no questions (to check specific hypotheses), and 4 open-ended questions (for discovery and narrative). Koji's AI handles sequencing, probing, and transitions naturally.\n\n**4. Share your interview link.** Participants click a link, choose voice or text mode, and complete the interview in 15–20 minutes on any device. No scheduling required.\n\n**5. Collect 20–50 responses.** Koji's quality gate automatically filters low-quality responses — too short, incomplete, or clearly disengaged — so your report reflects genuine participant input.\n\n**6. Generate and review your report.** Koji's AI synthesizes themes, surfaces representative quotes, and aggregates structured question data into charts — giving you a shareable research report in minutes.\n\nWhat 30 AI interviews give you that one focus group cannot: individual opinions uncontaminated by group dynamics, aggregated quantitative data across all 30 participants, automatic thematic analysis with quotes, and a publishable report — all for less than the cost of catering a single focus group session.\n\n## The Groupthink Problem Is Bigger Than You Think\n\nOne of the most compelling reasons to switch from focus groups is the research on conformity bias. Studies consistently show that individual opinions shift significantly toward group consensus in a group setting — meaning the data you collect in a focus group reflects social dynamics as much as genuine beliefs.\n\nThis matters most for sensitive topics (pricing sensitivity, competitor preferences, loyalty drivers), where participants actively conceal their true opinions to avoid social judgment. AI-moderated interviews, conducted in private, eliminate this dynamic entirely. Participants tell the AI things they would never say in front of eight strangers.\n\nWith platforms like Koji, AI interviews also surface participant voices that are systematically silenced in focus groups: the introverted participant who has a contrarian but accurate view, the customer who churned for an embarrassing reason, the user who finds your product confusing in ways they are reluctant to admit publicly.\n\n## Frequently Asked Questions\n\n**Q: Can AI interviews capture the group dynamics that focus groups reveal?**\nNot in the same way — and that is usually a feature, not a limitation. AI interviews capture individual truth without social contamination. If understanding group dynamics is your specific research objective, pairing AI interviews (for individual baselines) with an ethnographic group session (for social context) gives you more signal than a focus group alone.\n\n**Q: Are AI interview insights as rich as focus group insights?**\nRicher, in most cases. Without social pressure, participants share more candid opinions and personal details. AI probing follows threads that a human moderator managing a group often cannot pursue. With 30+ individual interviews rather than 8–12 participants in one session, your insight base is also significantly wider.\n\n**Q: What about body language and non-verbal cues that a human moderator picks up?**\nVoice-mode AI interviews do capture tone, hesitation, and emotional cues. For research where non-verbal body language is critical — such as product usability testing — supplementing AI interviews with a small number of video-observed sessions covers the gap effectively.\n\n**Q: How do I recruit participants for AI interviews?**\nKoji integrates with your CRM so you can import existing contacts directly. For external recruitment, Koji's shareable interview links work with any recruitment platform — or simply share them via email, Slack, or a website popup. Personalized links let you pre-fill participant names and context for a tailored experience that increases response rates.\n\n**Q: How quickly can I run 20 interviews?**\nWith Koji, you can launch a study and begin collecting responses within an hour. Getting 20 completions typically takes 24–72 hours depending on your participant pool. That is faster than scheduling a single focus group participant.\n\n**Q: Is AI-moderated research accepted by enterprise research and insights teams?**\nAI-moderated research is now standard practice at product and research teams across SaaS, fintech, healthcare, and ecommerce. The primary historical concern was interview quality — addressed in Koji by a quality gate that automatically filters responses scoring below a threshold on conversation depth, coherence, and completion rate.\n\n## Related Resources\n\n- [Structured Questions Guide: All 6 Koji Question Types](/docs/structured-questions-guide)\n- [AI Interviews vs. Surveys: Which is Right for Your Research?](/docs/ai-interviews-vs-surveys)\n- [How Koji's AI Follow-Up Probing Works](/docs/ai-probing-guide)\n- [Asynchronous User Interviews: The Complete Guide](/docs/async-user-interviews)\n- [How Many User Interviews Do You Need?](/docs/how-many-user-interviews)\n- [Setting Up Voice Interviews in Koji](/docs/setting-up-voice-interviews)\n","category":"Comparisons","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Best Focus Group Alternatives in 2026: AI Interviews vs. Group Research | Koji","metaDescription":"Focus groups are expensive, slow, and full of groupthink. Discover the best focus group alternatives in 2026 — including AI-moderated interviews that deliver richer insights at a fraction of the cost.","keywords":["focus group alternatives","alternative to focus groups","focus group replacement","online focus group alternative","focus group vs interview","ai interviews vs focus groups"],"aiSummary":"Focus groups cost $4,000–$12,000 per session, take 3–6 weeks, and suffer from groupthink bias. AI-moderated interview platforms like Koji offer a superior alternative: individual interviews at scale, conducted asynchronously, with automatic thematic analysis and quantitative aggregation. Other alternatives include async user interviews, online research communities, human-moderated depth interviews, and diary studies. Focus groups retain value only for group dynamics research, brand perception in social contexts, and creative co-creation sessions.","aiPrerequisites":["No prerequisites — this is an evaluative comparison guide"],"aiLearningOutcomes":["Understand the core limitations of focus groups","Compare five focus group alternatives on cost, speed, and depth","Know when focus groups still make sense","Set up an AI-moderated research study to replace a focus group"],"aiDifficulty":"beginner","aiEstimatedTime":"10 min read"},{"type":"documentation","id":"3319e10e-77ee-4c14-b365-b13b158af741","slug":"quick-start-guide","title":"Quick Start Guide","url":"https://www.koji.so/docs/quick-start-guide","summary":"A 10-minute walkthrough from creating your Koji account to publishing your first study with structured questions and sharing an interview link with participants. Covers every step of the core workflow.","content":"# Quick Start Guide\n\nYou can go from signing up to collecting your first interview response in about 10 minutes. This guide walks you through every step so you know exactly what to expect.\n\n## The Big Picture\n\nBefore we dive in, here's the overall flow:\n\n1. **Create your account** — sign up with Google or email.\n2. **Describe your research goal** — type what you want to learn on the dashboard.\n3. **Work with the AI Consultant** — refine your research brief, interview plan, and structured questions.\n4. **Publish your study** — lock in your research design.\n5. **Share the interview link** — send participants a URL and let Koji handle the rest.\n\nThat's it. No scheduling calendars, no transcription tools, no manual note-taking. Koji's AI conducts each interview, adapts follow-up questions in real time, captures structured data through interactive question widgets, and delivers analysis once responses come in.\n\nLet's walk through each step.\n\n---\n\n## Step 1: Create Your Account\n\nHead to [/signup](/signup) and create your account. You can sign up with your Google account for one-click access, or use an email and password if you prefer. For email signup, you'll also need to provide your full name.\n\nOnce you're in, you'll land on your [dashboard](/dashboard) — the home base for all your research.\n\nFor a deeper look at the signup process and profile setup, see [Creating Your Account](/docs/creating-your-account).\n\n## Step 2: Describe Your Research Goal\n\nOn your dashboard, you'll see a greeting — **\"What do you want to learn?\"** — with a text input below it. Type your research goal in plain language. Something like:\n\n> \"I want to understand why customers cancel their subscription in the first 30 days.\"\n\nor\n\n> \"I'm exploring how freelance designers find new clients.\"\n\nClick **Start study** to begin. The AI Consultant takes it from here.\n\nYou can also browse the template library for pre-built research frameworks to jumpstart your study design.\n\nFor detailed guidance on crafting your research question, check out [Creating Your First Study](/docs/creating-your-first-study).\n\n## Step 3: Work with the AI Consultant\n\nThis is where Koji really shines. Once you've entered your research goal, the AI Consultant — your built-in research design partner — will:\n\n- **Ask clarifying questions** to understand the nuance of what you're trying to learn.\n- **Suggest a methodology** that fits your research goal (like the Mom Test for validating product ideas, or Jobs-to-Be-Done for understanding user motivations).\n- **Draft a research brief** that outlines the study's objectives, target audience, and key themes.\n- **Build an interview guide** with a logical flow of questions, follow-up probes, and conversation structure.\n- **Design structured questions** — interactive widgets like NPS scales, single/multiple choice, ranking, and yes/no — each with configurable probing depth so the AI automatically asks follow-up questions to understand the \"why\" behind every response.\n\nYou can go back and forth with the Consultant as many times as you need. Ask it to adjust the tone, add questions, change the methodology, or focus on different areas. It's a collaborative process, not a one-shot generator.\n\nYou can also upload context documents — PDFs, text files, Word docs, JSON files, or markdown — to give the Consultant more background on your product, market, or previous research.\n\nLearn more in [Understanding the AI Consultant](/docs/understanding-the-ai-consultant).\n\n## Step 4: Publish Your Study\n\nOnce you're happy with the research brief and interview plan, hit **Publish**. This locks in your study design and generates a unique interview link that you can share with participants.\n\nPublishing doesn't mean you can't iterate later — but it does mean your study is live and ready to collect responses. You can also [customize the branding](/docs/customizing-branding) of your interview landing page before sharing.\n\nFor tips on when and how to publish, see [Publishing Your Study](/docs/publishing-your-study).\n\n## Step 5: Share Your Interview Link\n\nEvery published study gets a shareable link that looks like:\n\n```\nhttps://koji.so/i/your-study-slug\n```\n\nSend this link to anyone you want to interview. When they open it, Koji's AI will conduct the interview — either through [voice](/docs/voice-interview-experience) or [text](/docs/text-interview-experience), depending on your study settings. Participants don't need an account. They just click the link and start talking.\n\nYou can share this link via:\n- Email\n- Slack or Teams messages\n- Social media posts\n- Embedded on your website\n- QR codes\n- Anywhere a URL works\n\nFor distribution strategies, see [Sharing Your Interview Link](/docs/sharing-your-interview-link).\n\n---\n\n## What Happens Next?\n\nAs interviews come in, Koji automatically:\n\n- **Transcribes and structures** every conversation.\n- **Captures structured data** from interactive question widgets (scale ratings, choices, rankings).\n- **Identifies themes and patterns** across responses.\n- **Scores interview quality** so you know which responses are most insightful.\n- **Generates a research report** that aggregates findings across all interviews.\n\nYou can check progress anytime from your [dashboard](/dashboard). Each study shows the number of completed interviews and a summary of findings so far.\n\n## Quick Reference\n\n| Step | What You Do | Time |\n|------|-------------|------|\n| Sign up | Create account at [/signup](/signup) | 1 min |\n| Describe goal | Type your research question on the dashboard | 2 min |\n| AI Consultant | Refine brief, interview plan, and structured questions | 5 min |\n| Publish | Lock in your study design | 30 sec |\n| Share | Send the interview link | 1 min |\n\n**Total: ~10 minutes** from signup to a live study collecting real responses.\n\n---\n\n## Tips for Your First Study\n\n- **Start focused.** A specific question like \"Why do users abandon onboarding at step 3?\" will produce sharper insights than \"Tell me about the user experience.\"\n- **Trust the Consultant.** It's trained on established qualitative research methods. Let it suggest structure, then adjust to your needs.\n- **Use structured questions strategically.** Add a scale question for NPS or satisfaction scores, then let the AI probe deeper with follow-ups to understand the reasoning behind each rating. Learn more in the [Structured Questions Guide](/docs/structured-questions-guide).\n- **Start with 5-8 interviews.** You don't need 50 responses to find meaningful patterns. Qualitative research is about depth, not volume.\n- **Review as responses come in.** Don't wait until every interview is done. Reading early responses can help you spot patterns and decide if you need to adjust your approach.\n\nReady to dive deeper? Start with [Creating Your Account](/docs/creating-your-account) or jump straight to [Creating Your First Study](/docs/creating-your-first-study).","category":"Getting Started","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Quick Start Guide — Koji Docs","metaDescription":"Go from signup to your first AI-powered qualitative research interview in about 10 minutes. Step-by-step walkthrough.","keywords":["quick start","getting started","first study","setup guide","onboarding","qualitative research"],"aiSummary":"A 10-minute walkthrough from creating your Koji account to publishing your first study with structured questions and sharing an interview link with participants. Covers every step of the core workflow.","aiPrerequisites":[],"aiLearningOutcomes":["Set up a Koji account and navigate the dashboard","Create and publish a study from a research question","Share an interview link with participants","Understand the end-to-end Koji workflow"],"aiDifficulty":"beginner","aiEstimatedTime":"5 min read"},{"type":"documentation","id":"41d41ee6-3c46-4ee3-bdc2-980c39c4d66b","slug":"creating-your-account","title":"Creating Your Account","url":"https://www.koji.so/docs/creating-your-account","summary":"How to create a Koji account using Google or email signup (including full name for email), set up your profile, and understand the credit-based free plan with unlimited studies.","content":"# Creating Your Account\n\nSigning up for Koji takes less than a minute. You can use your Google account or create an account with your email address — both get you to the same place.\n\n## How to Sign Up\n\nHead to [/signup](/signup) to get started. You'll see two options:\n\n### Option 1: Sign Up with Google\n\nClick **Continue with Google** and select your Google account. That's it — your account is created instantly using your Google profile information. This is the fastest way to get started, and it means one fewer password to remember.\n\nIf you use Google Workspace for your organization, signing up with your work Google account keeps everything connected to your professional identity.\n\n### Option 2: Sign Up with Email\n\nPrefer not to use Google? No problem. Enter your **full name**, **email address**, and choose a **password**. You'll receive a verification email — click the link to confirm your account, and you're in.\n\nA few things to keep in mind about email signup:\n\n- **Full name is required.** This is used as your display name across the platform.\n- **Use an email you check regularly.** Koji sends study notifications and interview completion alerts to this address.\n- **Choose a strong password.** At least 8 characters with a mix of letters, numbers, and symbols.\n- **Check your spam folder** if you don't see the verification email within a couple of minutes.\n\n---\n\n## Setting Up Your Profile\n\nOnce you're signed in, you'll land on your [dashboard](/dashboard). Before diving into research, it's worth taking 30 seconds to check your profile settings.\n\nYou can access your profile from the sidebar navigation. When you switch to Profile mode, you'll see options for:\n\n- **Profile** — update your display name and profile picture.\n- **Billing** — manage your subscription and payment method.\n- **Usage** — see your credit usage for the current billing period.\n\nYour profile information is visible to team members if you're on a plan that supports collaboration, but it's never shown to interview participants. Participants interact only with Koji's AI interviewer.\n\n---\n\n## Understanding Your Free Plan\n\nEvery new account starts on the **Free plan**, which includes:\n\n- **10 one-time credits** — enough to run several text interviews or a few voice interviews.\n- **Unlimited studies** — create as many research projects as you need.\n- **Full AI analysis** — themes, insights, and quality scores on every interview.\n- **Access to all features** — voice and text interviews, structured questions, report generation, and more. Everything is available on every plan.\n\nCredits are the only gate on Koji. A text interview costs 1 credit, a voice interview costs 3 credits, and refreshing a report costs 5 credits. There's no expiration date on free plan credits, and you won't be asked for a credit card to start.\n\nWhen you're ready to scale up with monthly recurring credits, you can upgrade from your dashboard. Koji offers three paid plans:\n\n| Plan | Price | Monthly Credits |\n|------|-------|-----------------|\n| **Insights** | EUR 29/mo | 29 credits |\n| **Interviews** | EUR 79/mo | 79 credits |\n| **Enterprise** | Custom | 500+ credits |\n\nAll plans include unlimited studies and access to every feature. The difference is simply how many credits you receive each month. See the [Plan Comparison Guide](/docs/plan-comparison-guide) for a full breakdown.\n\n---\n\n## Your Dashboard: Home Base\n\nAfter signing up, the [dashboard](/dashboard) is where you'll spend most of your time. Here's a quick orientation:\n\n- **Research goal input** — the main dashboard greets you with \"What do you want to learn?\" and a text input where you type your research goal to start a new study.\n- **Template library** — browse pre-built research frameworks to jumpstart study design.\n- **Sidebar navigation** — in the overview section, you'll find **New study**, **Templates**, and **Studies**. Switch to profile mode for **Profile**, **Billing**, and **Usage**.\n\nThe studies list lives at a separate page accessible via the **Studies** link in the sidebar. There you can see all your research projects with their status, interview counts, and more.\n\nFor a detailed walkthrough of everything on the dashboard, see [Dashboard Overview](/docs/dashboard-overview).\n\n---\n\n## Choosing the Right Signup Method\n\nBoth signup methods give you the same access and features. Here's a quick comparison to help you decide:\n\n| | Google | Email |\n|---|---|---|\n| **Speed** | One click | 2 minutes (with email verification) |\n| **Password management** | Uses your Google credentials | Separate password to manage |\n| **Organization alignment** | Great for Google Workspace teams | Works with any email provider |\n| **Privacy** | Shares basic Google profile info | Only shares what you enter |\n\nIf you start with email and want to connect Google later (or vice versa), you can manage your authentication methods from your account settings.\n\n---\n\n## Security and Privacy\n\nA few things worth knowing about how Koji handles your account:\n\n- **Your data is yours.** Koji processes interview data to generate analysis and insights for your studies. Your data is never used to train AI models or shared with other customers.\n- **Encryption in transit and at rest.** All communication with Koji is encrypted via HTTPS, and stored data is encrypted at rest.\n- **Session management.** You can sign out from all devices via your account settings if you ever need to.\n- **No hidden data sharing.** Koji doesn't sell or share your personal information with third parties.\n\n---\n\n## Troubleshooting Signup Issues\n\nIf you run into problems creating your account, here are the most common fixes:\n\n**\"I didn't receive the verification email.\"**\nCheck your spam or junk folder first. If it's not there, try requesting a new verification email from the signup page. Make sure you entered the correct email address.\n\n**\"Google sign-in isn't working.\"**\nMake sure pop-ups aren't blocked in your browser — the Google authentication flow uses a pop-up window. Try disabling browser extensions temporarily if the issue persists.\n\n**\"I already have an account but can't sign in.\"**\nIf you signed up with Google, make sure you're selecting the same Google account. If you signed up with email, try the password reset flow. You can reset your password from the sign-in page.\n\n**\"I want to change my email address.\"**\nYou can update your email from your account settings after signing in. If you can't sign in with your current email, reach out to support.\n\n---\n\n## Next Steps\n\nYour account is set up and you're ready to go. Here's what to do next:\n\n1. **Explore the dashboard** — familiarize yourself with the layout. See [Dashboard Overview](/docs/dashboard-overview).\n2. **Create your first study** — jump right in with [Creating Your First Study](/docs/creating-your-first-study).\n3. **Follow the full walkthrough** — if you prefer a guided path, the [Quick Start Guide](/docs/quick-start-guide) covers everything in order.\n\nWelcome to Koji. Let's do some research.","category":"Getting Started","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Creating Your Account — Koji Docs","metaDescription":"Sign up for Koji with Google or email in under a minute. Set up your profile and start your first research project.","keywords":["sign up","create account","registration","Google login","profile setup","free plan"],"aiSummary":"How to create a Koji account using Google or email signup (including full name for email), set up your profile, and understand the credit-based free plan with unlimited studies.","aiPrerequisites":[],"aiLearningOutcomes":["Create a Koji account using Google or email","Set up a profile with display name and preferences","Understand what the free plan includes","Troubleshoot common signup issues"],"aiDifficulty":"beginner","aiEstimatedTime":"4 min read"},{"type":"documentation","id":"ea39d6b5-3f4b-489e-8afe-7c5659c1e69e","slug":"dashboard-overview","title":"Dashboard Overview","url":"https://www.koji.so/docs/dashboard-overview","summary":"A complete guide to the Koji dashboard, covering the study creation page, studies list, study statuses (including Paused/Closed), sidebar navigation, credit-based system, and accessing analysis reports.","content":"# Dashboard Overview\n\nThe Koji dashboard is your home base for all research activity. It's where you start new studies, access your existing research, and manage your account — all in one place.\n\nYou can access your dashboard at any time by visiting [/dashboard](/dashboard) or clicking the Koji logo in the navigation bar.\n\n---\n\n## The Main Dashboard: Start Your Research\n\nWhen you open the dashboard, the central element is the study creation prompt. You'll see:\n\n> **\"What do you want to learn?\"**\n\nBelow this greeting is a text input where you type your research goal in plain language. Click **Start study** to begin working with the AI Consultant on your research design.\n\nThis is the primary entry point for creating new research. You can also browse the **template library** for pre-built research frameworks that jumpstart your study design across categories like product research, customer discovery, HR, marketing, and more.\n\nFor a step-by-step walkthrough of the study creation process, see [Creating Your First Study](/docs/creating-your-first-study).\n\n---\n\n## The Studies Page\n\nYour list of all research projects lives at **/dashboard/studies**, accessible via the **Studies** link in the sidebar. Each study card shows key information at a glance:\n\n- **Study title** — the name of your research project.\n- **Status badge** — the current state of the study (see statuses below).\n- **Interview count** — how many quality-filtered interviews have been completed.\n- **Relative time** — when the study was last updated.\n\nClick on any study card to open it and see the full details: research brief, interview plan, individual interview transcripts, and the aggregated analysis report.\n\n### Study Statuses\n\nStudies move through several stages:\n\n| Status | What It Means |\n|--------|---------------|\n| **Draft** | You're still working on the study design with the AI Consultant. Not yet published. |\n| **Active** | The study is published and the interview link is live. Participants can respond. |\n| **Paused** | The study has been closed. The interview link is deactivated but the study can be reopened. Shown as \"Paused\" in the interface. |\n| **Archived** | The study has been archived for long-term storage. |\n| **Completed** | The study is finished. The final report is available. |\n\nDraft studies are where you spend time with the AI Consultant refining your research brief, interview plan, and [structured questions](/docs/structured-questions-guide). Once you're satisfied, publishing moves the study to Active status. You can learn more about this process in [Creating Your First Study](/docs/creating-your-first-study).\n\n---\n\n## Navigating the Dashboard\n\nThe dashboard layout is designed to keep things simple, even as your research activity grows.\n\n### Top Navigation\n\nThe top navigation bar gives you quick access to:\n\n- **Dashboard home** — click the Koji logo to return here from anywhere.\n- **Account menu** — your avatar in the top-right corner for quick access.\n\n### Sidebar Navigation\n\nThe sidebar provides two modes of navigation:\n\n**Overview mode:**\n- **New study** — start a new research project.\n- **Templates** — browse the template library.\n- **Studies** — view all your research projects.\n\n**Profile mode:**\n- **Profile** — update your display name and profile picture.\n- **Billing** — manage your subscription and payment method.\n- **Usage** — see your credit usage for the current billing period.\n\nIf you have API access, you'll also see an **API Keys** option in profile mode.\n\nThe interface is responsive, so it works well whether you're on a desktop, tablet, or phone. That said, designing research briefs is most comfortable on a larger screen, while checking interview progress works great from mobile.\n\n---\n\n## Studies and Credits\n\nYou can create **unlimited studies** on every plan — including the free plan. The only limit is credits, which are consumed when interviews are conducted or reports are refreshed.\n\n| Plan | Monthly Credits | Price |\n|------|----------------|-------|\n| **Free** | 10 one-time credits | Free |\n| **Insights** | 29 credits/month | EUR 29/mo |\n| **Interviews** | 79 credits/month | EUR 79/mo |\n| **Enterprise** | 500+ credits/month | Custom |\n\nAll features are available on every plan. See the [Plan Comparison Guide](/docs/plan-comparison-guide) for details.\n\n---\n\n## Accessing Study Details\n\nClicking into a study from the studies page opens the full study view, which includes several key sections:\n\n### Research Brief\nThe finalized brief you created with the AI Consultant. This includes your research objectives, target audience, methodology, and key topics. Learn more in [Understanding the Research Brief](/docs/understanding-the-research-brief).\n\n### Interview Plan\nThe structured interview guide that Koji's AI follows during each conversation. This shows the question flow, probing areas, conversation structure, and any [structured questions](/docs/structured-questions-guide) you've configured.\n\n### Interviews\nA list of all completed interviews. Each interview entry shows:\n- Participant identifier (anonymized)\n- Duration\n- Quality score\n- Key takeaways extracted by the AI\n- Full transcript\n\n### Analysis & Report\nThe aggregated analysis across all interviews, including:\n- **Theme identification** — recurring patterns and topics across responses.\n- **Structured data aggregations** — averages, distributions, and breakdowns from scale ratings, choice selections, and rankings.\n- **Key insights** — the most significant findings distilled from the data.\n- **Participant quotes** — notable quotes that illustrate key themes.\n- **Summary report** — a structured writeup of the research findings.\n\nThe analysis improves as more interviews come in. With just 2-3 interviews you'll start seeing initial patterns. By 5-8 interviews, the analysis typically becomes rich enough to drive decisions.\n\n---\n\n## Dashboard Tips\n\nHere are a few things that experienced Koji users find helpful:\n\n- **Start reviewing after 2-3 interviews.** Don't wait until all interviews are done. Early reads can tell you if your questions are landing well or if you need to adjust.\n- **Pay attention to quality scores.** A lower-than-expected quality score might mean your interview questions need refining, or that the participant pool isn't quite right.\n- **Use the study title wisely.** Give studies descriptive names like \"Q1 Churn Analysis — Enterprise Segment\" rather than \"Study 1.\" Your future self will thank you when the studies list grows.\n- **Bookmark your dashboard.** If you run research regularly, keeping [/dashboard](/dashboard) in your browser bookmarks saves a few seconds every time.\n- **Customize your interview landing page.** Before sharing your study link, consider [customizing the branding](/docs/customizing-branding) to match your organization's identity.\n\n---\n\n## What's Next\n\nNow that you know your way around the dashboard, the next step is to create your first study:\n\n- [Creating Your First Study](/docs/creating-your-first-study) — the step-by-step guide to going from research question to live interviews.\n- [Creating Your Account](/docs/creating-your-account) — if you haven't signed up yet, start here.\n- [Plan Comparison Guide](/docs/plan-comparison-guide) — understand the differences between Free, Insights, Interviews, and Enterprise plans.","category":"Getting Started","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Dashboard Overview — Koji Docs","metaDescription":"Navigate the Koji dashboard to manage studies, monitor interviews in real time, and access AI-powered research analysis.","keywords":["dashboard","navigation","studies list","study management","interview progress","research overview"],"aiSummary":"A complete guide to the Koji dashboard, covering the study creation page, studies list, study statuses (including Paused/Closed), sidebar navigation, credit-based system, and accessing analysis reports.","aiPrerequisites":["creating-your-account"],"aiLearningOutcomes":["Navigate the Koji dashboard confidently","Understand study statuses and lifecycle","Monitor interview progress and quality scores","Access study analysis and reports"],"aiDifficulty":"beginner","aiEstimatedTime":"5 min read"},{"type":"documentation","id":"524ceaa5-41f3-40d4-8f47-d1493b856452","slug":"creating-your-first-study","title":"Creating Your First Study","url":"https://www.koji.so/docs/creating-your-first-study","summary":"Step-by-step guide to creating a study in Koji, from typing a research goal on the dashboard to working with the AI Consultant to build a research brief, interview plan with structured questions, and tips on uploading context documents.","content":"# Creating Your First Study\n\nCreating a study in Koji means turning your research question into a structured interview plan — and the AI Consultant does most of the heavy lifting. You describe what you want to learn, and together you build a research brief that guides every interview.\n\nThe whole process typically takes about 5 minutes.\n\n---\n\n## Starting a New Study\n\nOn your [dashboard](/dashboard), you'll see a greeting:\n\n> **\"What do you want to learn?\"**\n\nType your research question or goal in the text input below. There's no special format required — just describe what you want to learn in plain language. Here are some examples of good starting points:\n\n- \"Why are users dropping off during our onboarding flow?\"\n- \"How do small business owners currently manage their invoicing?\"\n- \"What factors influence a parent's choice of after-school programs?\"\n- \"I want to validate whether remote workers would pay for a co-working day pass.\"\n\nClick **Start study** to begin. The AI Consultant opens a conversation to help you design your research.\n\nThe more specific you are, the better the AI Consultant can help. But even a vague starting point works — the Consultant will ask follow-up questions to sharpen the focus.\n\nYou can also start from the **Studies** page at /dashboard/studies, where a **New study** button is available, or choose a template from the template library on the main dashboard.\n\n### What Makes a Good Research Question?\n\nThe best research questions are:\n\n- **Specific** — focused on a particular behavior, decision, or experience rather than a broad topic.\n- **Open-ended** — designed to explore \"how\" and \"why,\" not just \"what\" or \"how many.\"\n- **Actionable** — tied to a decision you need to make or a problem you need to solve.\n\nFor example, \"Tell me about user experience\" is too broad. \"Why do first-time users abandon the checkout flow before completing a purchase?\" gives the AI Consultant much more to work with.\n\nFor more guidance, see [Writing a Research Question](/docs/writing-a-research-question).\n\n---\n\n## The AI Consultant Conversation\n\nOnce you've entered your research goal and clicked **Start study**, the AI Consultant takes over as your research design partner. Think of it as working with an experienced qualitative researcher who's helping you plan your study.\n\nHere's what typically happens:\n\n### 1. Clarifying Questions\n\nThe Consultant will ask you a few questions to understand the context:\n\n- Who is your target audience?\n- What decisions will this research inform?\n- Have you done any prior research on this topic?\n- Are there specific hypotheses you want to test?\n\nAnswer in as much or as little detail as you like. The Consultant adapts to whatever you provide.\n\n### 2. Methodology Suggestion\n\nBased on your research goal and context, the Consultant will recommend a methodology. Koji supports several established qualitative frameworks:\n\n- **Mom Test** — great for validating product ideas without leading the participant.\n- **Jobs to Be Done (JTBD)** — ideal for understanding what \"job\" users are hiring your product to do.\n- **Customer Discovery** — structured exploration of problems, needs, and existing solutions.\n- **Exploratory** — open-ended conversations for early-stage research.\n- **Lead Magnet** — designed to gather quotable statistics and insights for public reports.\n- **And more** — the Consultant draws from a range of methodologies and can blend approaches.\n\nYou don't need to know these frameworks in advance. The Consultant explains why it's recommending a particular approach and how it fits your goal. If you have a preference, just say so — \"I'd like to use the Mom Test approach\" — and it'll adjust.\n\nLearn more in [Understanding the AI Consultant](/docs/understanding-the-ai-consultant) and [Choosing a Methodology](/docs/choosing-a-methodology).\n\n### 3. Research Brief Creation\n\nThe Consultant drafts a **research brief** — a structured document that defines:\n\n- **Research objectives** — what you're trying to learn, stated clearly.\n- **Target audience** — who you should be interviewing.\n- **Key themes** — the main topics the interviews will explore.\n- **Success criteria** — what a \"good\" answer looks like for this research.\n\nThis brief becomes the foundation for the interview plan. Review it carefully — this is where you can make adjustments before the interview questions are generated. For a deeper look, see [Understanding the Research Brief](/docs/understanding-the-research-brief).\n\n### 4. Interview Plan and Structured Questions\n\nBased on the approved brief, the Consultant builds an **interview plan** — the actual conversation flow that Koji's AI will use during interviews. The plan includes:\n\n- **Opening questions** — warm-up questions that ease the participant into the conversation.\n- **Core exploration questions** — the main questions that dig into your research themes.\n- **Structured questions** — interactive question widgets that capture quantifiable data during the conversation.\n- **Follow-up probes** — adaptive questions the AI will ask based on what the participant says.\n- **Closing questions** — wrap-up questions that capture any final thoughts.\n\n#### Structured Question Types\n\nUnlike traditional survey platforms that only collect checkbox data, Koji combines conversational AI interviews with interactive structured questions — giving you both qualitative depth and quantitative rigor in a single session. The available question types are:\n\n- **Scale** — numeric ratings like NPS (0-10), satisfaction (1-5), or likelihood (1-7). Configure min, max, and endpoint labels.\n- **Single choice** — pick one option from a list. Great for segmentation and preferences.\n- **Multiple choice** — select all that apply. Useful for feature usage, pain points, and tools used.\n- **Ranking** — drag to order items by preference or priority.\n- **Yes/No** — binary questions for quick screening or confirmation.\n- **Open-ended** — free-form conversational questions for qualitative depth.\n\nEach structured question can be configured with **probing depth** (0-3 follow-ups) so the AI automatically asks follow-up questions to understand the \"why\" behind every rating or selection. For scale questions, you can enable **anchor probing** — after a participant gives a rating, the AI asks what would change that score.\n\nLearn more about designing effective structured questions in the [Structured Questions Guide](/docs/structured-questions-guide).\n\nThe interview plan isn't a rigid script. Koji's AI uses it as a guide but adapts in real time based on each participant's responses, following up on interesting threads and probing deeper when it detects valuable insights.\n\n---\n\n## Uploading Context Documents\n\nWant to give the AI Consultant more background? You can upload context documents during the study creation process. Supported file types include:\n\n- **PDF** — research reports, product specs, strategy documents.\n- **TXT** — plain text notes, interview transcripts from previous research.\n- **DOCX / DOC** — Word documents with any relevant background.\n- **JSON** — structured data like survey results or analytics exports.\n- **MD** — Markdown files with notes or documentation.\n\nThe Consultant reads these documents and uses them to craft more relevant questions and better-targeted research. For example, uploading your product's feature documentation helps the Consultant understand what specific features to ask about.\n\nYou don't have to upload anything — it's entirely optional. But if you have relevant context, sharing it produces noticeably better interview plans.\n\n---\n\n## Reviewing and Iterating\n\nBefore publishing, take time to review both the research brief and the interview plan. Ask yourself:\n\n- **Does the brief accurately capture my research goals?** If not, tell the Consultant what's missing or off-track.\n- **Are the interview questions going to surface the insights I need?** Read through the plan and imagine a participant answering each question.\n- **Are the structured questions well-calibrated?** Check that scale ranges, choice options, and probing settings match what you want to measure.\n- **Is the methodology right for my situation?** If you're not sure, ask the Consultant to explain the trade-offs.\n- **Is the target audience well-defined?** Vague audience definitions lead to unfocused interviews.\n\nYou can go back and forth with the Consultant as many times as you need. Say things like:\n\n- \"Can you add a scale question for NPS with a 0-10 range?\"\n- \"Add a multiple choice question about which features they use most.\"\n- \"The tone feels too formal — make it more conversational.\"\n- \"I want to focus more on the switching behavior and less on general satisfaction.\"\n- \"Switch to a Jobs to Be Done framework instead.\"\n\nThe Consultant will revise the brief and plan based on your feedback. There's no limit to how many iterations you can do.\n\n---\n\n## Publishing Your Study\n\nOnce you're happy with everything, hit **Publish**. This:\n\n- Locks in your research brief and interview plan.\n- Generates a unique, shareable interview link.\n- Moves the study from Draft to Active status.\n\nYour study is now live. Share the interview link with participants and start collecting responses. You can [customize the branding](/docs/customizing-branding) of your interview landing page to match your organization's identity.\n\nFor more on publishing and what happens next, see [Publishing Your Study](/docs/publishing-your-study).\n\n---\n\n## First Study Checklist\n\nBefore you publish, make sure:\n\n- [ ] Your research question is specific and actionable.\n- [ ] The research brief accurately reflects your goals.\n- [ ] The interview plan covers all the themes you care about.\n- [ ] Structured questions have appropriate scales, options, and probing settings.\n- [ ] The target audience is clearly defined.\n- [ ] You've uploaded any relevant context documents (optional but recommended).\n- [ ] You've read through the interview questions and imagined participant responses.\n\n---\n\n## Next Steps\n\n- [Understanding the AI Consultant](/docs/understanding-the-ai-consultant) — learn how to get the most out of your research design partner.\n- [Structured Questions Guide](/docs/structured-questions-guide) — design effective structured questions for quantitative data capture.\n- [Publishing Your Study](/docs/publishing-your-study) — what happens when you go live.\n- [Choosing a Methodology](/docs/choosing-a-methodology) — a deeper look at the qualitative frameworks Koji supports.\n- [Sharing Your Interview Link](/docs/sharing-your-interview-link) — distribution strategies to reach your target audience.","category":"Getting Started","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Creating Your First Study — Koji Docs","metaDescription":"Create your first Koji study: describe your research question, work with the AI Consultant, and build an interview plan.","keywords":["create study","research question","AI consultant","interview plan","research brief","methodology","first study"],"aiSummary":"Step-by-step guide to creating a study in Koji, from typing a research goal on the dashboard to working with the AI Consultant to build a research brief, interview plan with structured questions, and tips on uploading context documents.","aiPrerequisites":["creating-your-account"],"aiLearningOutcomes":["Create a new study from a research question","Work with the AI Consultant to design a research brief","Upload context documents to improve research quality","Review and iterate on interview plans before publishing"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"14d2bbff-376b-4f2d-8c96-8e571abb425e","slug":"understanding-the-ai-consultant","title":"Understanding the AI Consultant","url":"https://www.koji.so/docs/understanding-the-ai-consultant","summary":"An in-depth guide to Koji's AI Consultant: how it helps design qualitative research with structured questions, the conversation flow, supported methodologies including Exploratory and Lead Magnet, and tips for effective collaboration.","content":"# Understanding the AI Consultant\n\nThe AI Consultant is your research design partner inside Koji. It takes your research question and helps you build a rigorous, well-structured interview plan — much like working with an experienced qualitative researcher, except it's available instantly and never gets tired of revisions.\n\nIf you've ever felt unsure about how to structure a research study, which questions to ask, or what methodology to use, the AI Consultant is built for you.\n\n---\n\n## What the AI Consultant Does\n\nAt its core, the AI Consultant handles the part of qualitative research that usually requires the most expertise: **research design**. Specifically, it:\n\n1. **Reads and interprets your research goal** — understands what you're trying to learn, even from a casually written description.\n2. **Asks clarifying questions** — identifies gaps in the brief and asks targeted follow-ups to fill them.\n3. **Recommends a methodology** — suggests a qualitative framework that fits your research type.\n4. **Drafts a research brief** — creates a structured document defining objectives, audience, themes, and success criteria.\n5. **Builds an interview plan** — generates a complete conversation flow with opening questions, core exploration, follow-up probes, and closing.\n6. **Designs structured questions** — creates interactive question widgets (scales, choices, rankings, yes/no) with configurable probing depth for quantitative data capture alongside qualitative conversation.\n7. **Incorporates context** — reads any documents you upload (PDFs, text files, DOCX, JSON, MD) and weaves that knowledge into the research design.\n8. **Iterates on feedback** — revises any part of the plan based on your input, as many times as you need.\n\nThe result is a research study that follows established qualitative methods, with questions designed to surface genuine insights rather than surface-level opinions — and structured data capture that gives you quantifiable metrics alongside conversational depth.\n\n---\n\n## How the Conversation Works\n\nWhen you create a new study, the AI Consultant opens a chat-style conversation. Here's the typical flow:\n\n### Phase 1: Understanding Your Goal\n\nYou start by describing what you want to learn. The Consultant reads your input and responds with a combination of:\n\n- **Acknowledgment** — confirming it understands your goal.\n- **Clarifying questions** — asking for details that will make the research more focused.\n- **Initial suggestions** — early thoughts on approach and methodology.\n\nFor example, if you write \"I want to understand why users churn,\" the Consultant might ask:\n\n- \"Which user segment are you most concerned about — new users, long-time customers, or a specific plan tier?\"\n- \"Do you have any hypotheses about why they're leaving?\"\n- \"What time frame are we looking at — users who churned recently, or over the past year?\"\n\nThese questions aren't random. They're designed to narrow the research scope so the interviews produce actionable insights, not generic feedback.\n\n### Phase 2: Methodology Selection\n\nOnce the Consultant has enough context, it recommends a qualitative methodology. Koji's AI is trained on several established frameworks:\n\n- **The Mom Test** — focuses on asking about real past behavior rather than hypothetical futures. Perfect for product validation because it avoids the trap of participants telling you what you want to hear. The Consultant structures questions around concrete experiences and specific examples.\n\n- **Jobs to Be Done (JTBD)** — explores the \"job\" a user is \"hiring\" your product to do. The Consultant designs questions that uncover the situation, motivation, and desired outcome that drive adoption and switching decisions.\n\n- **Customer Discovery** — a structured approach to understanding problems, needs, and existing solutions in a market. Great for early-stage research when you're still defining the problem space.\n\n- **Exploratory** — open-ended conversation designed to deeply understand a lived experience. Used when you're researching a topic you know little about and need to build foundational understanding.\n\n- **Lead Magnet** — designed to gather quotable statistics and data points for public reports, blog posts, or marketing content. Heavy on structured questions (scales and choices) for chartable data, with open-ended questions for pull quotes.\n\nYou don't need to be familiar with any of these. The Consultant explains why it's recommending a particular approach and what it means for the interview structure. If you have a preference — maybe you've used JTBD before and want to stick with it — just say so.\n\nFor a deeper comparison, see [Choosing a Methodology](/docs/choosing-a-methodology).\n\n### Phase 3: Research Brief\n\nThe Consultant drafts a research brief that captures everything agreed upon:\n\n- **Research objectives** — clearly stated goals for the study.\n- **Target audience** — who should be interviewed and why.\n- **Key themes** — the main areas the interviews will explore.\n- **Methodology** — the chosen framework and how it applies.\n- **Success criteria** — what good interview data looks like for this study.\n\nThis brief is important because it becomes the guiding document for the entire study. The interview questions, the AI interviewer's behavior, and the analysis framework all flow from this brief.\n\nReview it carefully. If something feels off, tell the Consultant. It's much easier to adjust the brief now than to realize after 10 interviews that the questions were pointed in the wrong direction.\n\nFor more on the brief structure, see [Understanding the Research Brief](/docs/understanding-the-research-brief).\n\n### Phase 4: Interview Plan and Structured Questions\n\nWith the brief finalized, the Consultant generates the interview plan — the actual conversation structure that Koji's AI will follow during interviews.\n\nThe plan includes:\n\n- **Warm-up section** — easy, conversational questions to build rapport and set the participant at ease.\n- **Core questions** — the main research questions, sequenced to build on each other logically.\n- **Structured questions** — interactive widgets that capture quantifiable data mid-conversation (see below).\n- **Probing prompts** — follow-up directions the AI should explore based on participant responses.\n- **Transition logic** — how the conversation moves between topics naturally.\n- **Closing section** — questions that capture final thoughts, reflections, and anything the participant wants to add.\n\n#### Structured Questions: Conversational Depth Meets Quantitative Rigor\n\nKoji combines the depth of conversational AI interviews with interactive structured questions — giving you both qualitative richness and quantitative data in a single session. During an interview, participants interact with visual widgets embedded in the conversation:\n\n- **Scale questions** — numeric ratings like NPS (0-10), satisfaction (1-5), or Likert scales (1-7). You configure the range and endpoint labels.\n- **Single choice** — select one option from a list. Ideal for segmentation and clear preferences.\n- **Multiple choice** — select all that apply. Great for feature usage, pain points, or tools.\n- **Ranking** — drag items to order by preference or priority.\n- **Yes/No** — binary questions for screening or confirmation.\n\nWhat makes this different from a traditional survey is the **probing depth**. Each structured question can be configured with 0 to 3 follow-up probes. After a participant gives a rating or selects an option, the AI automatically asks follow-up questions to understand the reasoning. For scale questions, **anchor probing** asks what would change the score — turning a simple number into a rich insight.\n\nThis means you get clean, aggregatable data (averages, distributions, charts) alongside the conversational context that explains the \"why\" behind every data point. Learn more in the [Structured Questions Guide](/docs/structured-questions-guide).\n\nThe interview plan is adaptive. During an actual interview, Koji's AI uses this plan as a guide but responds dynamically to what the participant says. If a participant shares something unexpected and insightful, the AI will follow that thread before returning to the planned questions.\n\n---\n\n## Giving Effective Feedback\n\nThe quality of your study design depends on how well you collaborate with the Consultant. Here are tips for making the most of the conversation:\n\n### Be Specific About Changes\n\nInstead of \"make it better,\" try:\n- \"Add a question about how they evaluated alternatives before choosing us.\"\n- \"Include an NPS scale question with a 0-10 range and anchor probing enabled.\"\n- \"The opening questions are too formal — make them feel more like a casual conversation.\"\n- \"I want to spend more time on the post-purchase experience and less on the discovery phase.\"\n\n### Challenge the Recommendations\n\nThe Consultant is knowledgeable, but you know your business better. If something doesn't feel right, push back:\n- \"I don't think JTBD is right for this — we already know the job. I want to understand satisfaction, not motivation.\"\n- \"Our users wouldn't respond well to that question. Can you rephrase it to be less direct?\"\n\n### Share Context Generously\n\nThe more the Consultant knows, the better the research design. Share:\n- Previous research findings that are relevant.\n- Internal hypotheses your team has about the topic.\n- Constraints (e.g., \"Our participants will only have 10 minutes\").\n- Business context (e.g., \"We're deciding whether to sunset this feature\").\n\nYou can type this information directly in the chat or upload documents (PDF, TXT, DOCX, DOC, JSON, MD) for the Consultant to reference.\n\n### Iterate Without Hesitation\n\nThere's no penalty for multiple iterations. The Consultant doesn't get frustrated, and the quality of the output genuinely improves with each round of feedback. Most users find that 2-3 rounds of revision produce an excellent interview plan.\n\n---\n\n## What the AI Consultant Is Not\n\nTo set expectations clearly:\n\n- **It's not a standalone survey tool.** While Koji supports structured question types like scales and multiple choice, the platform is designed around conversational depth. Structured questions are embedded within AI-led conversations, not delivered as static forms. The result is richer data than either approach alone.\n- **It's not the interviewer.** The Consultant designs the research. A separate AI system conducts the actual interviews with participants. They're optimized for different things.\n- **It's not a replacement for human judgment.** The Consultant makes recommendations based on methodology expertise, but you should always review and adjust based on your specific context and knowledge of your audience.\n\n---\n\n## Getting the Most from the Consultant\n\nExperienced Koji users develop a rhythm with the Consultant that consistently produces strong research designs. Here are the patterns that work best:\n\n1. **Start with the decision, not the topic.** Instead of \"I want to research onboarding,\" try \"We need to decide whether to simplify or add more steps to onboarding. I want to understand what users actually experience.\" This gives the Consultant a decision anchor.\n\n2. **Share what you think you know.** Telling the Consultant your existing hypotheses helps it design questions that test those assumptions rigorously rather than just confirming them.\n\n3. **Mix question types intentionally.** Use open-ended questions for depth and exploration, scale questions for benchmarkable metrics (NPS, satisfaction), choice questions for segmentation, and ranking for prioritization. The Consultant can help you find the right mix.\n\n4. **Read the interview plan out loud.** Before publishing, read through the questions as if you were a participant. Do they flow naturally? Do any feel awkward or leading? Share that feedback.\n\n5. **Upload context.** Even a simple one-page document with product context helps the Consultant generate significantly more relevant questions.\n\n---\n\n## Next Steps\n\n- [Creating Your First Study](/docs/creating-your-first-study) — put this knowledge into practice by creating a study.\n- [Structured Questions Guide](/docs/structured-questions-guide) — learn how to design effective structured questions for quantitative data capture.\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — deep dive into the brief structure and how it guides your research.\n- [Choosing a Methodology](/docs/choosing-a-methodology) — compare qualitative frameworks to pick the right one for your next study.\n- [Voice Interview Experience](/docs/voice-interview-experience) — understand how voice interviews work for participants.\n- [Text Interview Experience](/docs/text-interview-experience) — understand how text interviews work for participants.","category":"Getting Started","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"AI Consultant Guide — Koji Docs","metaDescription":"Learn how Koji's AI Consultant helps you design rigorous qualitative research studies, even without formal training.","keywords":["AI consultant","research design","qualitative research","methodology","interview plan","research brief","Mom Test","JTBD"],"aiSummary":"An in-depth guide to Koji's AI Consultant: how it helps design qualitative research with structured questions, the conversation flow, supported methodologies including Exploratory and Lead Magnet, and tips for effective collaboration.","aiPrerequisites":["creating-your-account"],"aiLearningOutcomes":["Understand the AI Consultant's role in research design","Navigate the four phases of the Consultant conversation","Know which qualitative methodology fits your research goal","Give effective feedback to improve your interview plan"],"aiDifficulty":"beginner","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"adcdd08d-8b4e-4289-9d76-12f8532f934b","slug":"ai-interview-questions-generator","title":"AI Interview Question Generator: Build Better Research Guides Instantly","url":"https://www.koji.so/docs/ai-interview-questions-generator","summary":"AI interview question generators help researchers design better studies faster by applying research methodology best practices, avoiding leading and closed questions, and building in follow-up probes. Koji goes beyond question generation — its AI consultant generates a complete research brief and then conducts every interview autonomously, asking real-time follow-ups based on participant responses. This guide covers what makes a good AI question generator, how to use Koji to build and run a complete interview study, and includes 20 ready-to-use research questions.","content":"# AI Interview Question Generator: Build Better Research Guides Instantly\n\n**The short answer:** The best AI interview question generators don't just produce a list of questions — they help you build a complete research guide tailored to your specific goal, your target participants, and the depth of insight you need. Koji takes this one step further: it generates the guide *and* conducts every interview automatically.\n\nThis guide explains how AI transforms interview question creation, what to look for in an AI research tool, and how to generate interview questions that actually surface the insights you're after.\n\n---\n\n## Why Interview Question Design Is Harder Than It Looks\n\nWriting good research interview questions is a skill that takes years to develop. Most people write questions that sound reasonable but generate shallow data:\n\n- **Leading questions**: \"Don't you find it frustrating when software is slow?\" pushes the participant toward agreement.\n- **Closed questions**: \"Did you like the feature?\" gets yes/no when you need a story.\n- **Hypothetical questions**: \"What would you want in an ideal product?\" generates wishful thinking, not behavioral data.\n- **Double-barreled questions**: \"How easy was it to use and did it meet your needs?\" is two questions masquerading as one.\n\nEven experienced researchers make these mistakes under pressure. And if you're not a trained researcher at all — you're a founder, a product manager, or a customer success lead running research as part of your job — the risk is higher.\n\nAI changes this. A well-designed AI research assistant can catch these patterns, suggest better alternatives, and help you build a guide calibrated to your specific research goal.\n\n---\n\n## What a Good AI Interview Question Generator Does\n\nNot all AI question generators are created equal. Here's what separates genuinely useful tools from ones that just produce a generic list:\n\n### 1. It Understands Your Research Goal First\nA generic \"generate interview questions\" prompt produces generic questions. A good AI question generator starts by understanding:\n- What are you trying to learn?\n- Who are your participants?\n- What decisions will this research inform?\n\nKoji's AI consultant asks you these questions in a conversational setup flow before generating any interview content. The questions it produces are calibrated to your specific research goal — not a template pulled from a question bank.\n\n### 2. It Applies Research Methodology\nDifferent research goals call for different question frameworks:\n- **Jobs-to-Be-Done** — Questions focused on what progress the customer was trying to make when they hired your product\n- **The Mom Test** — Questions about the past and specific behaviors (not opinions and hypotheticals)\n- **Discovery interviews** — Broad, exploratory questions to understand uncharted territory\n- **Usability probing** — Observation + follow-up questions during or after task completion\n\nKoji supports multiple methodologies and selects the most appropriate one based on your goal — or lets you specify which framework to apply.\n\n### 3. It Builds in Probing Follow-Ups\nThe most revealing moments in a research interview happen when an interviewer hears something surprising and asks \"tell me more about that.\" An AI question generator that only produces top-level questions misses this.\n\nKoji's interview design includes configurable follow-up depth per question (0-3 probes), and the AI actually executes these probes in real time during the interview. When a participant says something unexpected, Koji asks the follow-up — just like a skilled human moderator would.\n\n### 4. It Produces a Complete Research Brief, Not Just a Question List\nThe best research interview guides include:\n- A research objective statement\n- A participant definition (who you're interviewing and why)\n- An interview structure with timing\n- Core questions with follow-up probes\n- Optional context-setting intro and closing\n\nKoji generates the full brief — a structured artifact that captures the entire research plan — and lets you edit any part of it before publishing.\n\n---\n\n## How to Generate Research Interview Questions with Koji\n\n### Step 1: Describe What You Want to Learn\n\nStart by telling Koji what you're trying to understand. Be specific:\n- \"I want to understand why users who sign up for our free plan don't upgrade after 30 days\"\n- \"I want to learn how procurement managers evaluate new software purchases\"\n- \"I want to understand what triggered our best customers to seek out a solution like ours\"\n\nVague goals produce vague interviews. The more specific your framing, the better Koji can design questions that get to the answer.\n\n### Step 2: Define Your Participants\n\nKoji asks you to describe who you're interviewing. The participant profile shapes the question vocabulary, complexity, and framing. Questions for a CTO sound different from questions for a frontline customer service rep — even if you're exploring the same topic.\n\n### Step 3: Review and Refine the Generated Guide\n\nKoji produces a full interview guide with:\n- Research objective statement\n- Suggested methodology\n- 4-8 core questions with follow-up probes\n- Estimated interview duration\n\nYou can edit any part of the guide directly or ask Koji to adjust specific questions. Common refinements:\n- \"Make this question more neutral — it's a bit leading\"\n- \"Add a question about the competitive alternatives they evaluated\"\n- \"Remove the scale question — I want this to be fully conversational\"\n\n### Step 4: Publish and Start Collecting\n\nOnce you're happy with the guide, Koji generates a shareable interview link. Participants click the link, answer your questions in a conversational AI chat or voice call, and Koji handles the follow-up probing automatically.\n\nYou don't moderate anything. You review the synthesized results.\n\n---\n\n## 20 Research Interview Questions You Can Use Right Now\n\nThese questions work across most research contexts and follow best-practice design principles. They're open, past-focused, and designed to elicit stories rather than opinions.\n\n### Discovery & Context\n1. \"Walk me through how [problem area] fits into your typical week.\"\n2. \"Tell me about the last time you had to deal with [problem]. What happened?\"\n3. \"What were you doing before you found [product/solution]?\"\n4. \"How did you end up looking for a solution to this?\"\n\n### Decision Making & Evaluation\n5. \"What made you decide to [try/buy/switch]?\"\n6. \"What were the alternatives you considered? What drove you to choose this one?\"\n7. \"Who else was involved in the decision? How did that process work?\"\n8. \"What were your biggest concerns before you started?\"\n\n### Usage & Experience\n9. \"Walk me through what you actually do when you [use the product/complete this task].\"\n10. \"What parts of this process feel smooth? What feels like friction?\"\n11. \"Tell me about a time it worked exactly as you needed it to.\"\n12. \"Tell me about a time it didn't. What happened?\"\n\n### Outcome & Impact\n13. \"What's changed since you started using this?\"\n14. \"How do you measure whether this is working for you?\"\n15. \"If this disappeared tomorrow, what would you do?\"\n\n### Improvement & Wishes\n16. \"If you could change one thing, what would it be?\"\n17. \"What do you wish existed that doesn't?\"\n18. \"What do you tell other people when you recommend this?\"\n\n### Closing\n19. \"Is there anything important about your experience that I haven't asked about?\"\n20. \"If you were designing the perfect solution for this problem, what would it look like?\"\n\n---\n\n## What to Avoid When Generating Interview Questions\n\nEven with AI assistance, these patterns creep in and weaken your data:\n\n**Future-focused questions**: \"What would you want?\" generates aspirational answers, not behavioral truth. Reframe to past: \"Tell me about the last time you needed something like this.\"\n\n**Opinion questions**: \"What do you think about AI in research?\" generates opinion. \"How have you actually used AI tools in your research work?\" generates experience.\n\n**Compound questions**: \"How easy was it to set up, and did it integrate with your existing stack?\" Split these — each question deserves its own answer.\n\n**Scale questions too early**: Don't open with \"On a scale of 1-10, how satisfied are you?\" Start with narrative questions to build context first. Save scales for later if you need them at all.\n\n**Questions about your solution**: Avoid asking about your product's features directly. Ask about the problem, the behavior, and the outcome. Let participants bring up your product unprompted — that's more valuable data.\n\n---\n\n## AI Interview Questions vs. AI That Conducts the Interview\n\nThere's a meaningful difference between tools that *generate* interview questions and platforms that *conduct* the interviews.\n\n**Question generators** (like ChatGPT with a research prompt):\n- Produce a list of questions you then use manually\n- Can't probe follow-ups in real time\n- Can't analyze responses across dozens of participants\n- Require you to moderate, transcribe, and code manually\n\n**AI interview platforms** (like Koji):\n- Generate the questions AND conduct every interview\n- Ask follow-up probes based on what participants actually say\n- Analyze themes, sentiment, and patterns automatically across all responses\n- Generate a synthesized report without manual coding\n\nIf you're running research at scale — more than 5-10 interviews — the difference is enormous. AI question generation saves you an hour upfront. An AI interview platform saves you 10-20 hours per study.\n\n---\n\n## Common Research Questions by Use Case\n\n### Product Research\n- Why do users churn after trial?\n- What features do users most rely on?\n- What's blocking users from upgrading?\n- How are users actually using a new feature?\n\n### Customer Discovery (Startups)\n- What problem are early customers really hiring you to solve?\n- Who is the ideal customer and what defines them?\n- What triggers the search for a solution like this?\n\n### Marketing & Messaging\n- What language do customers use to describe their problem?\n- How do customers describe the value they get from the product?\n- What made the product stand out vs. alternatives?\n\n### Employee Research\n- Why are people leaving?\n- What's creating friction in day-to-day work?\n- What would make the company a place where top performers stay?\n\nEach of these research goals maps to a distinct interview structure. Koji's AI consultant helps you match your goal to the right questions — so you don't have to start from a blank page.\n\n---\n\n## The Bottom Line\n\nGenerating good research interview questions is the foundation of useful qualitative research. AI makes it dramatically faster and reduces the common mistakes that weaken interview quality — leading questions, closed questions, hypothetical questions.\n\nBut the biggest leverage isn't in question generation alone. It's in platforms like Koji that go end-to-end: generate the questions, conduct every interview, probe for depth automatically, and synthesize the results into an actionable report.\n\nIf you've been struggling to run research consistently because the process takes too long, AI-powered interview generation and execution is the unlock. You describe what you want to learn, and Koji handles the rest.\n\n---\n\n## Related Resources\n\n- [Writing Interview Questions](/docs/writing-interview-questions) — Manual question design\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — How Koji generates questions\n- [Structured Questions Guide](/docs/structured-questions-guide) — Add quantitative elements\n- [AI Moderated Interviews](/docs/ai-moderated-interviews) — How AI moderation works\n- [Creating Your First Study](/docs/creating-your-first-study) — Get started with Koji\n\n*See how [structured questions](/docs/structured-questions-guide) enhance AI-generated interview guides with scales and choices.*","category":"guides","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"AI Interview Question Generator: Build Better Research Guides | Koji","metaDescription":"Learn how AI generates better research interview questions — and how Koji goes further by generating and conducting the full interview automatically. Includes 20 ready-to-use questions.","keywords":["ai interview questions generator","generate interview questions ai","ai research questions","interview guide generator","research interview question generator","how to write interview questions","ai interview guide","research question generator"],"aiSummary":"AI interview question generators help researchers design better studies faster by applying research methodology best practices, avoiding leading and closed questions, and building in follow-up probes. Koji goes beyond question generation — its AI consultant generates a complete research brief and then conducts every interview autonomously, asking real-time follow-ups based on participant responses. This guide covers what makes a good AI question generator, how to use Koji to build and run a complete interview study, and includes 20 ready-to-use research questions.","aiPrerequisites":["Basic familiarity with research interviews"],"aiLearningOutcomes":["Write research questions that avoid leading, closed, and hypothetical patterns","Use AI to generate a complete interview guide tailored to your research goal","Understand the difference between AI question generation and AI interview platforms","Apply 20 ready-to-use interview questions across common research scenarios"],"aiDifficulty":"beginner","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"95912610-7d0d-4d8f-89d9-341d4ebb4fa9","slug":"multilingual-research-guide","title":"Multi-Language User Research: How to Interview Participants in Any Language","url":"https://www.koji.so/docs/multilingual-research-guide","summary":"Koji supports AI voice and text interviews in 15+ languages including Spanish, French, German, Japanese, Hindi, Portuguese, and more. This guide covers setting up single-language studies, running multi-market research with separate links, using URL language parameters for embedded research, localizing research briefs, and synthesizing findings across language groups. Platforms like Koji make multilingual research as easy and affordable as single-language research.","content":"# Multi-Language User Research: How to Interview Participants in Any Language\n\nMost research teams run their user interviews in English — even when their customers are not. The result is systematic blind spots: non-English-speaking markets receive less research, their needs are underrepresented in product decisions, and entire customer segments become invisible in qualitative data.\n\nAI-powered interview platforms like Koji change the economics of multilingual research. Instead of hiring bilingual moderators, coordinating cross-timezone sessions, or outsourcing translation, teams can run voice and text interviews in 15+ languages with the same quality, depth, and automated analysis they get from English-language studies.\n\nThis guide covers how to set up multilingual studies in Koji, best practices for cross-language research, and how to synthesize findings across language groups.\n\n## Why Multilingual Research Matters\n\nThe business case for multilingual research is clear:\n\n- **51% of internet users prefer content in their native language** (W3Techs), and this preference extends strongly to research participation\n- Non-English speakers are consistently underrepresented in qualitative research, even at global companies with international customer bases\n- Critical cultural nuances — how users describe problems, what emotional language they use for pain points, how they frame success — get lost when participants are interviewed in a second language\n- GDPR and data localization requirements make European market research increasingly important for any team selling into the EU\n\nTraditional barriers to multilingual research are real: hiring bilingual moderators costs 2–4x standard rates, translation workflows add 1–2 weeks to research timelines, and most analysis tools struggle with non-English transcripts. Platforms like Koji eliminate all three barriers by running the entire interview workflow natively in the target language.\n\n## Languages Supported\n\nKoji's AI interviewer supports the following languages in voice mode:\n\n- English (US, UK, Australian)\n- Spanish (Latin American and Castilian)\n- French\n- German\n- Dutch\n- Japanese\n- Hindi\n- Portuguese (Brazilian and European)\n- Italian\n- Polish\n- Turkish\n- Korean\n- Mandarin Chinese\n- Arabic\n- Swedish\n\n**Text mode** supports all of the above plus dozens of additional languages through Google Gemini's multilingual capabilities — covering virtually all major world languages.\n\nWhen you set a language on your study, Koji automatically:\n1. Generates the AI interviewer's system prompt in that language\n2. Selects an appropriate native-speaker voice profile (voice mode)\n3. Configures speech-to-text transcription for that language\n4. Translates the interview UI, buttons, and participant-facing instructions\n\n## Setting Up a Multilingual Study\n\n### Option A: Single-Language Study\n\nIf all your participants speak the same language, create one study and set the language in **Customize → Interaction Mode → Default Language**.\n\nSteps:\n1. Create a new study in Koji\n2. Describe your research goal in the target language — Koji's AI will generate the brief in that language\n3. Go to **Customize → Interaction Mode**\n4. Set **Default Language** to your target language (e.g., Spanish, French, German)\n5. Update your landing page headline and description in the same language\n6. Run a test interview to verify the AI speaks naturally\n7. Launch and share your interview link with your target participants\n\n**Pro tip:** Write your research brief in the same language as the interview. The AI uses your brief as its source of truth. A brief written natively in Spanish produces more natural Spanish interview flow than an English brief that the system has to bridge across languages.\n\n### Option B: Multi-Market Study with Separate Links\n\nFor research spanning multiple language markets, the recommended approach is to create one study per language. This gives you:\n\n- Clean, separate data per language for market-by-market analysis\n- Language-specific landing pages with culturally appropriate copy\n- Separate transcript analysis per language group\n- Cleaner report generation per market with no cross-language noise\n\nSetup:\n1. Create your base study in your primary language\n2. Create a duplicate study for each additional language\n3. Set the language for each duplicate in the Customize tab\n4. Customize each landing page in the appropriate language\n5. Distribute language-specific interview links to the right audiences\n\nYou can import participants for each language study via CSV, and personalized links will take each participant directly to the correct language study. See [Importing Participants via CSV](/docs/importing-participants-csv) for the import workflow.\n\n### Option C: Language Parameter in the URL\n\nFor embed or in-product use cases where you know a user's locale, you can pass a language parameter in the interview URL to override the default:\n\n- `koji.so/i/your-study?lang=es` → Spanish interview\n- `koji.so/i/your-study?lang=fr` → French interview\n- `koji.so/i/your-study?lang=de` → German interview\n\nThis is particularly useful when embedding Koji in your product — you can detect the user's locale and pass the appropriate `?lang=` parameter so they always receive the interview in their language without needing separate studies.\n\n## Writing Research Briefs for Multilingual Studies\n\nThe research brief is the most important document in any Koji study. For multilingual work, a few additional considerations apply:\n\n**Localize your brief, do not just translate it.**\nDirect translation of English research questions often sounds unnatural in other languages. Idioms, phrasing, and conceptual framing vary significantly across languages and cultures. Where possible, use Koji's AI assistant to generate the brief natively in the target language, or have a native speaker review the translated brief before launch.\n\n**Be explicit about cultural context.**\nIf you are researching a behavior that differs across cultures — how different markets approach financial decisions, privacy expectations, or relationship-driven buying behavior — add that context to the Problem Context section of your brief.\n\n**Consider formality register.**\nSeveral languages have formal and informal registers: Japanese (keigo vs. casual), German (Sie vs. du), French (vous vs. tu), Korean (formal vs. informal speech levels). Specify the appropriate register in your brief. B2B research in Japanese typically requires formal keigo; consumer research for a younger demographic may be more casual.\n\n**Localize screening questions.**\nIntake form fields and screening questions should be in the local language. A French participant filling out an English screening form has a worse experience and may provide less accurate responses.\n\n## Analyzing Multilingual Results\n\n### Transcripts in Native Language\n\nAll transcripts in Koji are captured in the original interview language. If you run a Spanish study, you will see Spanish transcripts. AI analysis — quality scores, themes, sentiment, insights — is generated in the same language as the interview.\n\n### Cross-Language Synthesis\n\nFor research spanning multiple language markets, Koji's report generation can synthesize findings across studies. When you generate a report that draws on multiple studies, the AI identifies common themes across language groups and surfaces market-specific differences.\n\nFor more targeted cross-language comparison, use the AI Consultant in your Insights Dashboard:\n- \"What themes appeared in the German study but not the French study?\"\n- \"Are the same pain points appearing consistently across all markets?\"\n- \"How do Japanese participants describe this problem differently from US participants?\"\n\n### English Summaries from Non-English Research\n\nIf your primary working language is English but your participants are not, you can request that Koji generate report summaries and executive findings in English even when the underlying transcripts are in other languages. Configure this preference at the report generation stage.\n\nThis workflow is common for global research teams: run interviews natively in local languages for authentic responses, then synthesize and report in English for internal stakeholders.\n\n## Use Cases for Multilingual Research\n\n**International market expansion:**\nBefore entering a new market, run discovery interviews with potential customers in their native language. Understand their current alternatives, pain points, and buying criteria without the filter of a second language distorting your data.\n\n**Localization validation:**\nAfter translating your product or marketing materials, interview users in the local language to verify whether the translation resonates naturally or sounds awkward and foreign.\n\n**Global employee research:**\nFor organizations with international workforces, run employee experience research in employees' native languages. Non-English speakers are consistently underrepresented in HR, culture, and engagement research — with significant consequences for team health and retention.\n\n**Multilingual customer feedback programs:**\nBuild a continuous research pipeline that automatically interviews customers in their language after key touchpoints (onboarding, support interactions, renewals), then synthesizes findings across markets for product and strategy decisions.\n\n**Academic and institutional research:**\nFor research institutions studying cross-cultural phenomena, Koji enables standardized interview protocols to be deployed across multiple language markets simultaneously, with consistent methodology and comparable data quality.\n\n## Audio Quality Tips for Non-English Voice Interviews\n\nVoice interview quality varies somewhat by language. A few practical notes:\n\n- **Test each language before launch.** Run a self-test interview in the target language to verify naturalness of speech and appropriateness of the voice profile.\n- **Some accents and dialects may reduce transcription accuracy.** For highly regional accents, enabling text mode as a fallback is prudent.\n- **Encourage headphones.** This applies universally — headphones improve audio quality in any language.\n- **Consider time zones when monitoring.** If you are watching response rates in real time, remember that your participants across different markets may be active at very different hours.\n\n## Getting Started with Multilingual Research\n\nTo run your first non-English study in Koji:\n\n1. Create a new study and describe your research goal in the target language\n2. Set the language in **Customize → Interaction Mode → Default Language**\n3. Write your landing page headline and intake form in the target language\n4. Run a test interview to verify the AI sounds natural and covers your research topics\n5. Share your language-specific link with your target participants\n\nWith tools like Koji, multilingual research is no longer a specialized, resource-intensive effort reserved for large enterprise research teams. It is a standard capability available to any team that cares about understanding customers wherever they are in the world.","category":"guides","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Multi-Language User Research: How to Run Interviews in Any Language — Koji Docs","metaDescription":"How to configure Koji for multilingual user research. Covers supported languages, brief localization, single vs. multi-market study setup, cross-language synthesis, and best practices for international research.","keywords":["multilingual user research","international user interviews","multi-language research","user research in Spanish French German","global user research","non-English research"],"aiSummary":"Koji supports AI voice and text interviews in 15+ languages including Spanish, French, German, Japanese, Hindi, Portuguese, and more. This guide covers setting up single-language studies, running multi-market research with separate links, using URL language parameters for embedded research, localizing research briefs, and synthesizing findings across language groups. Platforms like Koji make multilingual research as easy and affordable as single-language research.","aiDifficulty":"intermediate","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"69fbabc9-50c5-46de-bade-b4dea90529bb","slug":"setting-up-voice-interviews","title":"How to Set Up AI Voice Interviews: A Researcher's Complete Guide","url":"https://www.koji.so/docs/setting-up-voice-interviews","summary":"A complete researcher-facing guide to setting up AI voice interview studies in Koji. Covers creating the research brief, enabling voice mode, selecting interview language, configuring the landing page, setting up intake forms, adding context documents, customizing the interview slug, testing before launch, and monitoring responses. Voice interviews produce 3–5x richer qualitative data than typed answers, and platforms like Koji automate the entire workflow from moderation to analysis.","content":"# How to Set Up AI Voice Interviews: A Researcher's Complete Guide\n\nAI-powered voice interviews are the fastest way to run deep qualitative research at scale — no scheduling, no moderator burnout, and no transcript cleanup required. Koji's voice interview mode lets participants speak naturally with an AI interviewer that listens, probes, and adapts in real time, while you review rich transcripts and auto-generated insights in your dashboard.\n\nThis guide walks through everything you need to configure before launching a voice interview study: from research brief setup to interview mode selection, language settings, landing page branding, and quality optimization.\n\n## Why Voice? The Case for AI-Powered Spoken Research\n\nTraditional user research methods require scheduling live sessions, training moderators, and manually transcribing recordings — a process that can take weeks and cost thousands of dollars per research cycle. Voice interviews with AI eliminate nearly all of that friction.\n\nWhen participants speak rather than type, they express themselves more naturally, use richer language, and tend to share more emotional and contextual detail. Research shows that spoken responses average 3–5x more words than typed answers to the same question. Combined with Koji's AI interviewer — which probes for depth, asks follow-up questions, and guides the conversation across all your research topics — voice mode produces the kind of nuanced qualitative data that typically requires a skilled human moderator.\n\nThe key difference: Koji's AI runs 24/7, handles unlimited concurrent interviews, and never gets tired or introduces moderator bias. You can launch a voice study on Monday and have 50 rich, analyzed interviews by Wednesday.\n\n## Step 1: Create Your Study and Define the Research Brief\n\nBefore configuring voice settings, you need a clear research brief. Koji generates this automatically when you describe your research goal in natural language, but you can also edit it manually in the canvas editor.\n\nA strong brief for a voice study includes:\n\n**Problem Context**\n- What question are you trying to answer?\n- What decision will this research inform?\n- What is your current hypothesis?\n- What does it cost your team if this remains unanswered?\n\n**Target Participant**\n- Who should participate? (role, behavior, relevant experience)\n- What is the screening question that qualifies them?\n\n**Methodology**\n\nKoji supports several built-in methodologies that shape how the AI probes follow-up questions:\n\n- **The Mom Test** — great for validating ideas without getting false positives; focuses on past behavior rather than hypotheticals\n- **Jobs to be Done (JTBD)** — ideal for understanding switching behavior and purchase triggers\n- **Customer Discovery** — broad, exploratory research for early-stage products\n- **Exploratory** — open-ended discovery when you don't know what you don't know\n- **Lead Magnet** — designed to produce chartable, shareable insights for public reports\n\nThe methodology shapes how deeply the AI probes and what kinds of follow-up questions it asks. See [Choosing a Methodology](/docs/choosing-a-methodology) for guidance on which to use for your research goals.\n\n## Step 2: Enable Voice Mode\n\nOnce your brief is ready, go to your study's **Customize** tab to configure the interview experience.\n\nUnder **Interaction Mode**, you will find three settings:\n\n- **Enable Voice** — toggle to allow participants to choose voice mode\n- **Enable Text** — toggle to allow text mode as an option or fallback\n- **Default Mode** — set whether the landing page defaults to voice or text\n\n**Best practices:**\n- If voice data quality is a priority, set Default Mode to **Voice** and keep text enabled as a fallback. Participants who cannot use a microphone will not be blocked.\n- If you want exclusively voice data, you can disable text mode entirely — but this will reduce completion rates for participants in noisy environments or on mobile devices.\n- For most studies, leaving both enabled with voice as default produces the best combination of data quality and completion rate.\n\n## Step 3: Configure Your Interview Language\n\nKoji supports voice interviews in over 15 languages, including English, Spanish, French, German, Dutch, Japanese, Hindi, Portuguese, Italian, Polish, Turkish, Korean, Mandarin Chinese, Arabic, and Swedish.\n\nSet your study's language in the **Interaction Mode** section under **Default Language**. This controls:\n- The language the AI interviewer speaks\n- The language of speech-to-text transcription\n- The language of the interview UI and on-screen instructions\n\nWhen you set a non-English language, Koji automatically configures the underlying conversational agent for that language and selects an appropriate voice profile. Transcripts are captured in the native language, and AI analysis (quality scores, themes, insights) is generated in the same language.\n\nFor multilingual research spanning multiple markets, create separate studies per language with individual interview links. See [Multi-Language Research](/docs/multilingual-research-guide) for a full guide.\n\n## Step 4: Configure Your Landing Page\n\nThe landing page is the first thing participants see. A well-designed landing page significantly increases completion rates — the difference between a generic link and a well-crafted landing page is often a 2–3x lift in participation.\n\nIn the **Customize** tab, configure:\n\n**Headline and Description**\n\nWrite a clear, welcoming headline that explains what the interview is about. Participants convert better when they understand the purpose upfront.\n\n- ❌ \"Research Study Q1 2025\"\n- ✓ \"Share your experience with our checkout process — takes about 10 minutes\"\n\n**Duration Badge**\n\nEnable the duration badge to show the estimated interview length. This is the single highest-impact trust element on the landing page. Participants who know the interview takes 10 minutes are far more likely to start than those who have no idea what they are committing to.\n\n**Anonymity Badge**\n\nAdd an anonymity or privacy badge if participants might hesitate to share candidly. Common messages: \"Your responses are anonymous\", \"This interview is confidential\", or custom text.\n\n**Accent Color**\n\nSet a brand color to match your organization's identity. The landing page, animated background orb, and UI accents use this color — making the interview feel native to your brand rather than a generic research tool.\n\n## Step 5: Set Up an Intake Form (Optional)\n\nAn intake form collects participant information before the interview begins. This is useful for:\n- Capturing email addresses for follow-up or incentive distribution\n- Screening participants based on role, company size, or behavior\n- Pre-populating participant records with CRM data\n\nConfigure up to 6 fields in the **Lead Collection Form** section. Supported field types include text, email, phone, select dropdown, textarea, and checkbox.\n\nIf you need to screen participants before they proceed, add a required Select field with your qualifying criteria. Participants who do not match can be shown a graceful redirect message.\n\nSee [Intake Forms and Consent](/docs/intake-forms-and-consent) for detailed configuration options.\n\n## Step 6: Add Context Documents (Optional)\n\nFor studies involving a specific product, workflow, or concept, you can upload context documents that the AI interviewer uses to inform its questions and follow-ups.\n\nContext documents are useful for:\n- Product specs or mockup descriptions (concept testing)\n- Customer journey maps (UX research)\n- Feature release notes (post-launch feedback research)\n- Competitive comparison data (win/loss research)\n\nUpload documents in your study's **Settings** tab. The AI uses these as background knowledge but does not read them aloud to participants — it references them only when relevant to probe deeper on a specific topic.\n\n## Step 7: Customize Your Interview Slug\n\nBy default, Koji generates a random URL slug for your interview. You can customize it to something memorable and professional:\n\n- `koji.so/i/checkout-research-q1`\n- `koji.so/i/enterprise-discovery`\n- `koji.so/i/post-launch-feedback`\n\nCustom slugs are available on paid plans and configured in the **Collect** tab under **Interview Link Settings**. A clean, recognizable URL improves click-through rates in email campaigns and feels more professional when sharing with customers.\n\n## Step 8: Test Before You Launch\n\nBefore sending links to real participants, run a test interview yourself:\n\n1. Open your interview link in a fresh browser window (or incognito mode)\n2. Grant microphone permission when prompted\n3. Complete the full interview, including all question types\n4. Check the transcript in your dashboard for accuracy\n5. Review the auto-generated quality score and AI insights\n\nListen and evaluate:\n- Does the greeting feel natural and appropriate for your audience?\n- Does the AI probe appropriately when you give short or vague answers?\n- Does the interview cover all your key research questions?\n- Is the transcript readable and accurate?\n\nIf the AI is not covering a particular topic, edit your research brief to be more explicit about that area. The brief is the source of truth for the AI's behavior — more specific briefs produce more targeted interviews.\n\nRemember to archive your test responses in the Recruit tab so they do not affect your real analysis.\n\n## Step 9: Launch and Monitor\n\nShare your interview link via:\n- Email outreach (manual or via your CRM using personalized links)\n- In-product prompts (embed or trigger after key user actions)\n- Customer success sequences\n- Research panels or recruitment platforms\n\nMonitor incoming responses in your **Results** tab. Koji's quality gate automatically filters out low-quality or incomplete interviews before they affect your analysis.\n\nVoice interviews are automatically transcribed, analyzed, and scored within minutes of completion. You do not need to wait for all responses before reviewing early themes — the Insights Dashboard updates in real time as new interviews arrive.\n\n## Voice Interview Best Practices\n\n**For study design:**\n- Keep your research brief focused on one core problem area. A tightly scoped brief produces more relevant voice data than a broad one.\n- For qualitative saturation, aim for 15–40 participants depending on your topic and audience homogeneity.\n- Set the interview duration expectation correctly — voice interviews typically run 8–15 minutes.\n\n**Tips to share with participants:**\n- Use Chrome or a Chromium-based browser for the best microphone support\n- Wear headphones or earbuds to prevent echo\n- Find a quiet space — background noise can interfere with speech detection\n- Plan for about 10 minutes of uninterrupted time\n\n## What Happens After the Interview\n\nEvery completed voice interview automatically generates:\n\n- **Full transcript** — timestamped, speaker-labeled text of the complete conversation\n- **Quality score** — 1–5 rating based on relevance, depth, coverage, and engagement\n- **AI-generated insights** — themes, sentiment, pain points, feature requests, and notable quotes\n- **Question coverage map** — which research questions were covered and with what confidence\n\nThese feed directly into Koji's [Insights Dashboard](/docs/insights-dashboard) and [Research Reports](/docs/generating-research-reports), giving you a complete analysis without any manual tagging, coding, or synthesis.\n\nWith platforms like Koji, the heavy lifting of voice research — scheduling, moderation, transcription, and analysis — is handled automatically. What once took weeks and a dedicated research team now takes days and a single researcher.","category":"guides","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"How to Set Up AI Voice Interviews: A Researcher's Complete Guide — Koji Docs","metaDescription":"Step-by-step guide to configuring and launching AI voice interview studies in Koji. Covers brief setup, voice mode configuration, language settings, landing pages, and testing.","keywords":["AI voice interviews","voice interview setup","automated voice research","how to run AI voice interviews","voice research platform","voice interview configuration"],"aiSummary":"A complete researcher-facing guide to setting up AI voice interview studies in Koji. Covers creating the research brief, enabling voice mode, selecting interview language, configuring the landing page, setting up intake forms, adding context documents, customizing the interview slug, testing before launch, and monitoring responses. Voice interviews produce 3–5x richer qualitative data than typed answers, and platforms like Koji automate the entire workflow from moderation to analysis.","aiDifficulty":"beginner","aiEstimatedTime":"15 minutes"},{"type":"documentation","id":"b3293ddc-237c-4571-848b-ab17ec7c721d","slug":"solo-researcher-toolkit-guide","title":"The Solo Researcher's Toolkit: Scaling Impact Without a Team","url":"https://www.koji.so/docs/solo-researcher-toolkit-guide","summary":"This guide helps solo researchers and research teams of one scale their impact 3-5x using Koji AI interviews. It covers research triage systems, template libraries, operational cadences, democratization strategies, and career growth paths — turning the research bottleneck into a scalable research practice.","content":"## The Bottom Line\n\nYou're the only researcher on the team — or you're a PM/designer who's been handed the research responsibility. Either way, you're expected to cover every product team's research needs with zero additional headcount. This guide shows you how to use Koji and smart operational practices to deliver 5x more research output without burning out.\n\n## The Solo Researcher Reality\n\nIf you're reading this, you probably recognize your situation:\n\n- You have 3-5 product teams asking for research, and you can only serve 1-2 at a time\n- Your backlog of research requests is 3-6 months deep\n- You spend more time on logistics (recruiting, scheduling, moderating) than on analysis and strategy\n- You've become a bottleneck, and teams are making decisions without evidence because they can't wait\n- When you do deliver research, the findings are powerful — but there's always more to do\n\n### The Math Problem\n\nA solo researcher working at maximum efficiency:\n- **Moderation**: 5-8 interviews per week (including prep and debrief)\n- **Analysis**: 2-3 days per study to synthesize and report\n- **Recruitment**: 4-6 hours per study coordinating participants\n- **Stakeholder management**: 5-8 hours per week on meetings, updates, and alignment\n\nThat leaves maybe 1-2 completed studies per month. With 4 product teams each needing 2-3 studies per quarter, you're at 25% capacity. The other 75% of research needs go unmet.\n\n### What Koji Changes\n\nWith Koji handling moderation at scale:\n- **Moderation**: 0 hours (AI handles it)\n- **Study design**: 2-3 hours per study (your expertise, applied more efficiently)\n- **Analysis**: 1-2 hours per study (AI synthesis + your interpretation)\n- **Recruitment**: 1-2 hours per study (streamlined through Koji)\n- **Stakeholder management**: Same 5-8 hours (this is where your impact lives)\n\nResult: 4-6 completed studies per month. You just tripled your output.\n\n## Building Your Solo Research Operating System\n\n### 1. Triage Research Requests\n\nNot all research is equal. Create a simple intake process:\n\n**Tier 1 — Must Do (you lead, Koji augments)**\n- Decisions with >$500K revenue impact\n- Strategic direction changes\n- New market entry or major pivot\n\n**Tier 2 — Should Do (Koji leads, you oversee)**\n- Feature prioritization research\n- Concept testing and validation\n- Usability evaluation\n- Post-launch feedback\n\n**Tier 3 — Nice to Have (Koji runs, you review)**\n- Exploratory/generative research\n- Competitive perception studies\n- Continuous pulse monitoring\n\n**Tier 4 — Self-Serve (teams run with your templates)**\n- Quick feedback on design options\n- Simple preference testing\n- Post-feature satisfaction checks\n\n### 2. Create Your Template Library\n\nBuild reusable Koji discussion guides for every common research need:\n\n**Discovery Template**\n- Workflow exploration (5 min)\n- Pain point identification (5 min)\n- Current solutions and workarounds (3 min)\n- Ideal state exploration (2 min)\n\n**Validation Template**\n- Context setting (3 min)\n- Concept/prototype reaction (5 min)\n- Comparative assessment (3 min)\n- Adoption intent (2 min)\n- Concerns and barriers (2 min)\n\n**Evaluation Template**\n- Task walkthrough (5 min)\n- Satisfaction assessment (3 min)\n- Improvement suggestions (3 min)\n- Recommendation likelihood (2 min)\n\n**Churn/Retention Template**\n- Usage history and expectations (3 min)\n- Satisfaction decline journey (5 min)\n- Decision factors (3 min)\n- Competitive comparison (2 min)\n- Win-back conditions (2 min)\n\n### 3. Establish Your Research Cadence\n\n**Weekly**\n- Monday: Launch any new studies, review completed analyses\n- Tuesday-Wednesday: Deep analysis sessions on active studies\n- Thursday: Stakeholder presentations and insight sharing\n- Friday: Study design for next week, recruitment setup\n\n**Monthly**\n- Week 1: Strategic research (Tier 1 projects)\n- Week 2: Product team research (Tier 2 projects)\n- Week 3: Exploratory/generative research (Tier 3)\n- Week 4: Repository updates, methodology refinement, planning\n\n**Quarterly**\n- Comprehensive research review with leadership\n- Research impact assessment (decisions influenced, outcomes tracked)\n- Template and methodology updates\n- Next quarter research agenda alignment\n\n## Maximizing Impact as a Team of One\n\n### Focus on Insight, Not Logistics\n\nYour highest-value activities as a researcher are:\n1. **Framing the right questions**: What should we learn, and why does it matter?\n2. **Interpreting patterns**: What do the findings mean for our product strategy?\n3. **Telling compelling stories**: How do we communicate insights so they change behavior?\n4. **Building research culture**: How do we make evidence-based decision-making the norm?\n\nEverything else — recruitment, moderation, transcription, initial coding — is execution work that Koji handles better at scale than you can manually.\n\n### Democratize Without Losing Control\n\nThe biggest leverage move for a solo researcher is enabling others to run research:\n\n**Step 1**: Create approved Koji templates for common questions\n**Step 2**: Train PMs and designers to launch studies using your templates\n**Step 3**: Review AI-generated analyses and add your interpretive layer\n**Step 4**: Coach teams on what the findings mean and how to act on them\n\nYou go from being a bottleneck to being an enabler. Your role shifts from \"person who does research\" to \"person who ensures research quality and translates insights into action.\"\n\n### Build a Research Repository\n\nAs a solo researcher, institutional memory lives in your head — a dangerous single point of failure. Build a lightweight repository:\n\n- Store every Koji study with key findings and recommendations\n- Tag findings by theme, product area, and decision type\n- Create a \"greatest hits\" deck of foundational insights everyone should know\n- Update quarterly with new findings that confirm, refine, or contradict prior insights\n\n### Measure and Report Your Impact\n\nSolo researchers often struggle to justify their role because research impact is invisible. Track:\n\n- **Studies completed per quarter**: Show volume and coverage\n- **Decisions influenced**: Which product decisions used research evidence?\n- **Outcomes validated**: Did research-informed decisions produce better results?\n- **Cost avoidance**: Features not built (or improved before launch) because of research\n- **Stakeholder satisfaction**: Do product teams feel supported by research?\n\n## The Solo Researcher's Tool Stack\n\n### Core Tools\n- **Koji**: AI-moderated voice interviews at scale (your research workhorse)\n- **Repository tool**: Notion, Dovetail, or Confluence for storing and sharing findings\n- **Communication**: Slack channels for sharing insights and fielding questions\n- **Project management**: Simple Kanban board for tracking research requests and status\n\n### How Koji Fits In\nKoji replaces 3-4 tools for a solo researcher:\n- **Recruitment coordination** (replaces Calendly + email juggling)\n- **Interview moderation** (replaces your calendar being full of back-to-back calls)\n- **Transcription** (replaces Otter.ai or Rev)\n- **Initial analysis** (replaces hours of manual coding and theme identification)\n\n### What You Still Need\n- Your brain for study design and interpretation\n- Stakeholder relationships for research impact\n- A simple way to share findings (Notion, Slides, Loom)\n- Occasionally, other methods (card sorting tools, prototype testing platforms)\n\n## Common Solo Researcher Challenges\n\n### \"Everyone wants research but nobody wants to wait\"\n**Solution**: Koji's speed advantage means most studies complete in 3-7 days. Set expectations: \"I can have initial findings by [date]\" is realistic when you're not manually moderating every interview.\n\n### \"I'm being asked to research things that don't need research\"\n**Solution**: Build a decision framework. Does this decision have high stakes? Is it reversible? Do we have existing data? Not every question needs a study — sometimes the answer is \"look at the analytics\" or \"just ship it and measure.\"\n\n### \"My findings get ignored\"\n**Solution**: Two strategies. First, involve stakeholders in study design so they're invested in the answers. Second, present findings in terms of business impact, not research methodology. \"23 of 50 customers described this as their #1 frustration\" hits harder than \"thematic analysis revealed a salient pattern.\"\n\n### \"I can't cover every team\"\n**Solution**: Tier your support (see above) and train teams to self-serve on Tier 3-4 research. Your job is not to do all the research — it's to ensure all research is done well.\n\n### \"I'm burning out\"\n**Solution**: If Koji removes 60-70% of the moderation and logistics work, you get that time back for analysis, strategy, and recovery. Research burnout usually comes from the volume of operational tasks, not from the intellectual work of understanding users.\n\n## Scaling From Solo to Team\n\nWhen you're ready to make the case for additional research headcount:\n\n### The Evidence\n- \"I'm running X studies per quarter, but the backlog has Y pending requests\"\n- \"Teams that get research support ship features with Z% better adoption\"\n- \"Research-informed decisions resulted in $X cost avoidance this year\"\n- \"With Koji handling moderation, each additional researcher can cover 4-6 product teams\"\n\n### The Model\nShow how a small research team (2-3 people) with Koji can cover the entire organization:\n- **Senior researcher** (you): Strategic research, methodology leadership, stakeholder influence\n- **Research ops/coordinator**: Recruitment, repository management, self-serve template creation\n- **Junior researcher**: Koji study execution, analysis, and reporting for Tier 2-3 projects\n\nThree people with Koji can deliver the research output of a traditional team of 8-10.\n\n## Frequently Asked Questions\n\n### I'm not a trained researcher — can I still use Koji effectively?\nYes. Koji's discussion guide templates and AI-powered synthesis make customer research accessible to PMs, designers, and other non-researchers. Start with the provided templates, review the AI analysis, and iterate your approach. You don't need a PhD in research methods to conduct valuable customer interviews.\n\n### How do I prioritize when everything feels urgent?\nAsk two questions: \"What's the cost of being wrong?\" and \"When does this decision need to be made?\" High-cost, soon-deadline decisions get priority. Everything else can wait or use a lighter-weight research approach.\n\n### Should I still do some manual interviews?\nYes — selectively. Manual interviews are valuable for building personal customer empathy, observing body language, and having strategic conversations with key accounts. Use manual interviews for Tier 1 strategic research and let Koji handle everything else.\n\n### How do I build credibility as a solo researcher?\nQuick wins build credibility fast. Pick the highest-visibility open question, run a Koji study, and deliver insights that change a decision. When a PM says \"we shipped X because of your research, and it outperformed expectations,\" your credibility is established.\n\n### What's the most common mistake solo researchers make?\nTrying to do everything perfectly instead of doing enough things well. An 80%-perfect study that influences a decision is infinitely more valuable than a 100%-perfect study that arrives after the decision is made. Koji helps you get to \"good enough, fast enough\" — then you add your expertise to elevate the output.\n\n---\n\n## Related Resources\n\n- [How to Automate Research](/docs/how-to-automate-user-research) — Build a research pipeline\n- [Continuous Discovery Guide](/docs/continuous-discovery-user-research) — Ongoing research practices\n- [Scaling User Research](/docs/scaling-user-research) — Grow your research impact\n- [Koji for UX Researchers](/docs/koji-for-ux-researchers) — UX researcher workflows\n- [AI-Generated Insights](/docs/ai-generated-insights) — Automated analysis\n\n*Use [structured questions](/docs/structured-questions-guide) to scale solo research with AI-powered interviews.*","category":"guides","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"The Solo Researcher's Toolkit: Scale Research Without a Team | Koji","metaDescription":"The complete guide for solo researchers and teams of one. Learn how to 3-5x your research output with AI-powered interviews, smart templates, and operational efficiency.","keywords":["solo researcher","research team of one","UX research scaling","research operations","research tools","research efficiency","research templates","research backlog","research democratization","AI research tools","research capacity","product research"],"aiSummary":"This guide helps solo researchers and research teams of one scale their impact 3-5x using Koji AI interviews. It covers research triage systems, template libraries, operational cadences, democratization strategies, and career growth paths — turning the research bottleneck into a scalable research practice.","aiPrerequisites":["Responsibility for customer research (formal or informal)","Basic understanding of user research goals"],"aiLearningOutcomes":["Build a scalable solo research operating system","Create reusable discussion guide templates for common research needs","Triage research requests by impact and effort","Democratize research across product teams while maintaining quality"],"aiDifficulty":"beginner","aiEstimatedTime":"16 minutes"},{"type":"documentation","id":"4d485b2e-ea32-47a7-abc2-3bc90d785a39","slug":"ai-voice-interviews-definitive-guide","title":"AI Voice Interviews: The Definitive Guide for 2026","url":"https://www.koji.so/docs/ai-voice-interviews-definitive-guide","summary":"AI voice interviews combine the depth of human-moderated interviews with survey-level scale through AI moderation. This guide covers the complete methodology: how they work, when to use them, discussion guide architecture, analysis workflows, and best practices for producing actionable insights from 50-500+ conversations.","content":"## The Bottom Line\n\nAI voice interviews are the most significant methodological innovation in qualitative research since the invention of the online survey. They combine the depth of human-moderated interviews with the scale of surveys and the consistency of automated data collection. This guide covers everything: how they work, when to use them, how to design them, and how they change the economics of customer research.\n\n## What Are AI Voice Interviews?\n\nAI voice interviews are structured research conversations conducted by an artificial intelligence interviewer rather than a human moderator. Participants speak naturally with the AI, which follows a researcher-designed discussion guide, asks intelligent follow-up questions based on responses, and captures the full audio and transcript for analysis.\n\n### How They Work\n\n1. **You design the study**: Define research objectives, create a discussion guide, set participant criteria\n2. **Participants receive an interview link**: No scheduling — they click and start when convenient\n3. **The AI conducts the interview**: It follows your guide, asks follow-ups, manages time, and maintains conversational flow\n4. **Audio is transcribed and analyzed**: Full transcripts, sentiment analysis, theme identification, and cross-interview synthesis happen automatically\n5. **You interpret and act**: Review AI-generated insights, add your strategic interpretation, share with stakeholders\n\n### What Makes Them Different from Chatbot Surveys\n\nAI voice interviews are not chatbot surveys with a microphone. The differences are fundamental:\n\n- **Conversational intelligence**: The AI understands context and asks relevant follow-up questions, not just predetermined branches\n- **Emotional capture**: Voice conveys tone, enthusiasm, hesitation, and frustration — data layers that text cannot provide\n- **Natural interaction**: Talking is the most natural form of human communication. Participants share more and share more honestly\n- **Adaptive probing**: When a participant says something interesting, the AI explores it deeper — just like a skilled human interviewer\n\n## The Science Behind AI Voice Interviews\n\n### Why Voice Produces Better Data Than Text\n\nResearch in cognitive psychology shows that verbal responses are:\n- **More detailed**: People speak 3-5x more content per minute than they type\n- **More honest**: Verbal responses show less social desirability bias than written ones\n- **More emotional**: Voice carries paralinguistic cues (tone, pace, volume) that reveal attitude\n- **More spontaneous**: Less time to self-edit produces more authentic responses\n- **More accessible**: Talking requires less cognitive effort than writing, especially for complex topics\n\n### Why AI Moderation Reduces Bias\n\nHuman moderators introduce systematic biases:\n- **Confirmation bias**: Unconsciously steering toward expected findings\n- **Rapport effects**: Different rapport with different participants produces inconsistent data\n- **Energy variation**: Interview quality degrades over a long day of back-to-back sessions\n- **Selective probing**: Following personal interests rather than research objectives consistently\n- **Social influence**: Participants modify responses based on perceived moderator reactions\n\nAI moderators eliminate all five. They apply your discussion guide with perfect consistency, probe based on predefined criteria rather than intuition, and maintain the same conversational quality whether it is the first interview or the five-hundredth.\n\n### The Scale-Depth Trade-off Resolved\n\nResearch has always forced a choice: go deep (interviews) or go wide (surveys). AI voice interviews resolve this:\n\n| Method | Depth | Scale | Speed |\n|--------|-------|-------|-------|\n| In-depth interviews | Very high | 10-30 | 4-8 weeks |\n| Focus groups | High | 24-48 | 3-6 weeks |\n| Surveys | Low | 500+ | 1-2 weeks |\n| **AI voice interviews** | **High** | **50-500+** | **3-7 days** |\n\n## When to Use AI Voice Interviews\n\n### Ideal Use Cases\n\n**Customer discovery**: Understanding problems, workflows, and unmet needs through conversation\n**Concept testing**: Capturing authentic reactions to new ideas, products, or features\n**Feature prioritization**: Learning why features matter, not just ranking them\n**Churn analysis**: Understanding the journey from satisfaction to cancellation\n**Win/loss analysis**: Learning why deals were won or lost from the buyer perspective\n**Competitive intelligence**: How customers perceive you versus alternatives\n**Employee experience**: Anonymous, honest feedback about workplace culture\n**Market validation**: Testing assumptions with real market participants at scale\n**Pricing research**: Exploring willingness to pay through nuanced conversation\n**Brand perception**: Understanding emotional brand associations\n\n### Less Ideal Use Cases\n\n**Usability testing**: Requires screen observation (use UserTesting or Maze)\n**Diary studies**: Requires longitudinal data capture (use dscout)\n**Card sorting**: Requires visual manipulation (use OptimalSort)\n**A/B testing**: Requires behavioral measurement (use Optimizely or VWO)\n**Large-scale demographic surveys**: Requires 10,000+ responses (use SurveyMonkey)\n\n## Designing Effective AI Voice Interviews\n\n### Discussion Guide Architecture\n\nA well-designed discussion guide is the foundation of a successful AI voice interview. Structure yours in five sections:\n\n**1. Warm-Up (2-3 minutes)**\n- Build comfort with the format\n- Establish context about the participant\n- Open-ended questions that get them talking\n\n*Example*: \"Tell me about your role and what a typical week looks like for you.\"\n\n**2. Context Setting (3-5 minutes)**\n- Understand current behavior and environment\n- Map the workflow or process you are researching\n- Identify existing tools and solutions\n\n*Example*: \"Walk me through how your team currently handles customer feedback.\"\n\n**3. Core Exploration (5-8 minutes)**\n- Dive deep into the central research question\n- Use open-ended questions that invite stories\n- Configure the AI to probe on specific topics\n\n*Example*: \"Tell me about a time when you felt frustrated with your current feedback process.\"\n\n**4. Targeted Probing (3-5 minutes)**\n- Test specific hypotheses or concepts\n- Present stimulus materials if applicable\n- Compare options or evaluate features\n\n*Example*: \"If you could change one thing about how you collect customer insights, what would it be?\"\n\n**5. Reflection and Close (2-3 minutes)**\n- Summary questions that capture overall assessment\n- Open invitation for topics not covered\n- Thank and close\n\n*Example*: \"Is there anything about your experience that we did not cover that you think is important?\"\n\n### Discussion Guide Best Practices\n\n**DO:**\n- Start broad, then narrow\n- Use \"tell me about a time when...\" questions to elicit stories\n- Include transition phrases between sections\n- Define probing rules for the AI (when to explore deeper)\n- Keep total interview time to 12-20 minutes\n- Pilot test with 3-5 participants before scaling\n\n**DO NOT:**\n- Ask leading questions (\"Do you agree that X is important?\")\n- Use jargon or internal terminology\n- Stack multiple questions in one prompt\n- Ask hypothetical questions when behavioral questions work better\n- Include more than 12-15 questions (quality over quantity)\n- Skip the warm-up (participants need to get comfortable talking to AI)\n\n### Configuring the AI Interviewer\n\nBeyond the discussion guide, configure:\n\n**Probing depth**: How aggressively should the AI follow up? For exploratory research, set high probing. For structured evaluation, set moderate probing.\n\n**Time management**: Set maximum interview duration and let the AI prioritize questions if time runs short.\n\n**Topic boundaries**: Define what the AI should and should not explore. Keep conversations focused on research objectives.\n\n**Sensitivity settings**: For employee research or sensitive topics, configure the AI to approach certain areas with appropriate care.\n\n**Language and tone**: Match the AI to your participant population — professional for B2B executives, conversational for consumers.\n\n## Analyzing AI Voice Interview Data\n\n### Automatic Analysis\n\nKoji produces several analysis layers automatically:\n\n**Transcription**: Full text of every interview, searchable and quotable\n**Theme identification**: Recurring topics and patterns across all interviews\n**Sentiment analysis**: Emotional tone mapping across topics and segments\n**Frequency analysis**: How often each theme appears across the dataset\n**Key quotes**: Representative and notable verbatims for each theme\n**Segment comparison**: How themes and sentiments differ across participant groups\n\n### Researcher Analysis Layer\n\nThe AI provides the scaffolding. Your expertise adds:\n\n**Pattern interpretation**: What do the themes mean for your business?\n**Causal reasoning**: Why are these patterns emerging?\n**Strategic implication**: What should we do differently based on these findings?\n**Cross-study synthesis**: How do these findings connect to previous research?\n**Stakeholder framing**: How do we present this to drive action?\n\n### Analysis Workflow\n\n1. **Read the AI synthesis** (30-60 minutes): Get the big picture\n2. **Review key themes** (60-90 minutes): Validate AI-identified patterns\n3. **Deep-dive transcripts** (60-120 minutes): Read 10-20 full transcripts for nuance\n4. **Segment analysis** (30-60 minutes): Compare findings across participant groups\n5. **Insight framing** (60-90 minutes): Translate findings into actionable recommendations\n6. **Stakeholder presentation** (30-60 minutes): Create shareable output\n\n**Total analysis time**: 4-8 hours for a 100-interview study\n**Compare to manual analysis**: 40-80 hours for the same study\n\n## AI Voice Interview Best Practices\n\n### 1. Pilot Everything\nRun 3-5 pilot interviews before scaling. Review transcripts to check:\n- Is the AI asking questions in a natural flow?\n- Are participants engaging authentically?\n- Is the probing going deep enough on key topics?\n- Are any questions confusing or poorly worded?\n\n### 2. Right-Size Your Sample\n- **Quick pulse**: 20-30 interviews for directional findings\n- **Standard study**: 50-75 interviews for reliable patterns\n- **Segmented analysis**: 25-30 per segment for comparison\n- **Comprehensive research**: 100-200+ for statistical confidence across multiple dimensions\n\n### 3. Recruit for Diversity\nDo not just interview your most engaged users. Include:\n- Power users and casual users\n- Satisfied and dissatisfied customers\n- Recent joiners and long-tenured users\n- Different company sizes, industries, and roles\n- Churned customers (often the most valuable)\n\n### 4. Combine with Other Data\nAI voice interviews are most powerful when triangulated with:\n- Product analytics (behavior + motivation)\n- Survey data (quant benchmarks + qual context)\n- Support tickets (issue tracking + understanding)\n- Sales conversations (pipeline context + buyer insight)\n\n### 5. Share Findings Widely\nResearch that sits in a report changes nothing. Share through:\n- Slack snippets with key quotes\n- Monthly insight digests\n- Stakeholder presentations with audio clips\n- Research repository for institutional memory\n- Roadmap documents with evidence links\n\n## The Future of AI Voice Interviews\n\n### Where the Technology Is Heading\n\n**Multi-modal interviews**: AI that can discuss images, prototypes, and documents during the conversation\n**Real-time translation**: Interviews in any language, analyzed in your preferred language\n**Emotional AI**: More sophisticated analysis of vocal patterns, detecting nuanced emotional states\n**Adaptive guides**: AI that adjusts the discussion guide in real-time based on emerging patterns across interviews\n**Continuous research**: Always-on interview channels embedded in product experiences\n**Predictive analysis**: AI that identifies emerging trends before they become obvious patterns\n\n### What Will Not Change\n\nDespite technological advances, the fundamentals remain:\n- Research quality depends on question quality\n- Interpretation requires human expertise\n- Insights are only valuable when they drive action\n- Ethical research practices remain non-negotiable\n- The goal is understanding people, not just collecting data\n\n## Frequently Asked Questions\n\n### How accurate is AI voice interview transcription?\nModern AI transcription achieves 95-98% accuracy across accents and speaking styles. Koji continuously improves its transcription models, and transcripts are available for manual review and correction if needed.\n\n### Do participants feel comfortable talking to an AI?\nResearch on AI interviewer acceptance shows that most participants adapt within the first 1-2 minutes. Many report feeling more comfortable than with a human interviewer because there is no social judgment. Participant satisfaction rates for AI interviews are consistently above 85%.\n\n### How does AI interviewing handle different languages and accents?\nAI voice interviews support multiple languages and are trained on diverse accent patterns. For global research, participants can interview in their preferred language, and transcripts can be translated for centralized analysis.\n\n### What happens if a participant goes off-topic?\nThe AI is trained to acknowledge off-topic contributions and gently redirect to the research objectives. You can configure how strictly the AI maintains topic focus versus allowing exploratory tangents.\n\n### Are AI voice interviews suitable for sensitive research topics?\nFor moderately sensitive topics (workplace satisfaction, product complaints, competitive perceptions), AI interviews are often better than human-moderated alternatives because participants are more honest without social pressure. For highly sensitive topics (trauma, health conditions, illegal behavior), human moderation with appropriate training may still be more appropriate.\n\n### How do AI voice interviews compare to focus groups?\nAI interviews capture individual perspectives without group influence. Focus groups are valuable when you specifically want to observe social dynamics and group decision-making. For most research objectives, AI interviews produce cleaner, less biased data at larger scale.\n\n---\n\n## Related Resources\n\n- [Voice Interview Experience](/docs/voice-interview-experience) — How voice interviews work\n- [AI Moderated Interviews](/docs/ai-moderated-interviews) — How AI moderation works\n- [Text Interview Experience](/docs/text-interview-experience) — Text interview comparison\n- [From Survey to Conversation](/docs/from-survey-to-conversation-guide) — Migration guide\n- [Setting Up Voice Interviews](/docs/setting-up-voice-interviews) — Practical setup guide\n\n*See how [structured questions](/docs/structured-questions-guide) add quantitative rigor to voice interviews.*","category":"guides","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"AI Voice Interviews: The Definitive Guide for 2026 | Koji","metaDescription":"Everything about AI-moderated voice interviews: how they work, when to use them, discussion guide design, analysis best practices, and comparison to every other research method.","keywords":["AI voice interviews","AI moderated interviews","voice interview guide","qualitative research methods","AI research methods","discussion guide design","interview methodology","research best practices","AI interviewer","voice research","conversational research","research methodology 2026"],"aiSummary":"AI voice interviews combine the depth of human-moderated interviews with survey-level scale through AI moderation. This guide covers the complete methodology: how they work, when to use them, discussion guide architecture, analysis workflows, and best practices for producing actionable insights from 50-500+ conversations.","aiPrerequisites":["Interest in qualitative research methodology","Basic understanding of research objectives"],"aiLearningOutcomes":["Understand how AI voice interviews work and when to use them","Design effective discussion guides for AI moderation","Analyze AI interview data efficiently","Compare AI interviews to other research methods for informed method selection"],"aiDifficulty":"beginner","aiEstimatedTime":"18 minutes"},{"type":"documentation","id":"2d6443af-3a96-428c-919a-f598b0720da7","slug":"from-survey-to-conversation-guide","title":"From Survey to Conversation: The Complete Migration Guide","url":"https://www.koji.so/docs/from-survey-to-conversation-guide","summary":"This guide walks teams through the practical transition from survey-dependent research to conversation-based insights using AI voice interviews. It includes a survey-to-conversation translation framework, parallel study methodology, change management playbook, and measurement plan for tracking transition success.","content":"## The Bottom Line\n\nYour surveys are producing diminishing returns — response rates are declining, insights are shallow, and stakeholders are making decisions despite the data, not because of it. This guide walks you through the practical transition from survey-dependent research to conversation-based insights using AI voice interviews, with frameworks for translating your existing surveys and managing organizational change.\n\n## Why the Transition Is Happening Now\n\n### The Survey Decline\n- **Average survey response rates** have dropped from 33% (2010) to under 15% (2025)\n- **Completion rates** for surveys longer than 10 questions are below 20%\n- **Data quality** is degrading as respondents satisfice and straight-line\n- **Insight depth** remains surface-level regardless of how many questions you add\n\n### The Conversation Advantage\n- **Completion rates** for AI voice interviews consistently exceed 85%\n- **Data richness** per participant is 10-20x higher than survey responses\n- **Honesty scores** are significantly higher in voice vs. text formats\n- **Time to insight** is comparable or faster than survey deployment\n\n### The Technology Unlock\nAI voice interviews were not practical five years ago. The combination of advanced speech recognition, natural language understanding, and conversational AI has created a modality that genuinely rivals human-moderated interviews for most research applications. Koji represents this maturation — making the transition feasible and practical.\n\n## The Survey-to-Conversation Translation Framework\n\n### Step 1: Audit Your Current Surveys\n\nList every active survey in your organization and classify each:\n\n**Category A — Replace with voice interviews**\n- Surveys where you consistently need follow-up to understand results\n- Surveys with open-text fields that produce the most valuable data\n- Surveys where stakeholders say \"interesting, but what does it mean?\"\n- Surveys with declining response rates or completion rates\n- Surveys that inform high-stakes decisions\n\n**Category B — Supplement with voice interviews**\n- NPS or CSAT tracking surveys (keep the quantitative benchmark, add voice depth)\n- Standardized benchmarking surveys required for compliance or comparison\n- High-volume transactional feedback (quick pulse + periodic voice deep-dive)\n\n**Category C — Keep as surveys**\n- Simple binary or multiple-choice feedback (yes/no, A/B preferences)\n- Demographic data collection\n- Event registration or logistics forms\n- Anything where the question genuinely has a finite set of clear answers\n\n### Step 2: Translate Survey Questions to Conversation Starters\n\nThe most common mistake in transitioning is treating voice interviews as verbal surveys — reading survey questions aloud. Voice interviews require fundamentally different questions.\n\n**Rating Scale → Experience Exploration**\n- Survey: \"Rate your satisfaction with our product: 1-5\"\n- Voice: \"Tell me about your experience using our product this past month. What has worked well? What has been frustrating?\"\n\n**Multiple Choice → Open Discovery**\n- Survey: \"What is your primary reason for using our product? (a) Save time (b) Reduce costs (c) Better quality (d) Other\"\n- Voice: \"What originally brought you to our product, and why do you keep using it?\"\n\n**Yes/No → Nuanced Understanding**\n- Survey: \"Would you recommend our product to a colleague? Yes/No\"\n- Voice: \"If a colleague asked you about our product, what would you tell them?\"\n\n**Matrix Questions → Storytelling Prompts**\n- Survey: \"Rate each feature: [grid of features x satisfaction levels]\"\n- Voice: \"Walk me through which features you use most often and why they matter to your work\"\n\n**Open Text → Guided Conversation**\n- Survey: \"Please describe any additional feedback:\" (produces 2-3 word responses)\n- Voice: \"Is there anything about your experience that we have not covered that you think is important for us to know?\"\n\n### Step 3: Design Your First Conversation Study\n\nTake your highest-value Category A survey and redesign it:\n\n1. **Identify the core research question**: What does this survey actually try to answer?\n2. **Write 8-12 conversation questions**: Use the translation framework above\n3. **Add probing instructions**: Tell the AI when and how to follow up\n4. **Set the time target**: 12-15 minutes for the equivalent of a 20-question survey\n5. **Define segments**: Who needs to be represented in the sample?\n6. **Pilot test**: Run 5 interviews, review transcripts, refine\n\n### Step 4: Run a Parallel Study\n\nFor organizational buy-in, run both formats simultaneously:\n\n- Send your existing survey to 200 people\n- Send the Koji voice interview to 50 people\n- Compare the depth, actionability, and stakeholder reaction to both sets of findings\n- Document specific insights the voice interviews revealed that the survey missed\n\nThis comparison is the single most effective change management tool. When stakeholders see both outputs side by side, the conversation advantage sells itself.\n\n## Change Management for the Transition\n\n### Stakeholder Buy-In\n\n**For leadership**: \"We are getting richer insights from 50 voice interviews than from 500 survey responses. The data is more actionable and decisions are more confident.\"\n\n**For analytics teams**: \"Voice interviews produce quantifiable themes and segment comparisons — not just anecdotes. The AI synthesis generates structured data alongside qualitative depth.\"\n\n**For survey owners**: \"You are not losing data. You are upgrading from numbers without context to numbers with context. We keep quantitative benchmarks where needed and add conversational depth where it matters.\"\n\n**For participants**: \"Instead of clicking through a survey, you can have a 12-minute conversation at a time that works for you. Your feedback will be heard and acted on.\"\n\n### Common Objections and Responses\n\n**\"We need the trend data from our existing surveys\"**\nKeep your quantitative pulse survey for benchmarking. Replace the deep-dive portion with voice interviews. You get trend continuity AND better insights.\n\n**\"Our response rates are fine\"**\nResponse rate is not the problem — insight quality is. A 30% response rate producing data that nobody acts on is worse than a 15% voice interview rate producing insights that change decisions.\n\n**\"Voice interviews cannot scale like surveys\"**\nAI voice interviews scale to 500+ participants per study. That is more scale than most survey-based research actually needs for reliable findings.\n\n**\"We do not have budget for new tools\"**\nCalculate the cost of your current survey tools + the analyst time to interpret results + the follow-up research to understand what surveys revealed. Koji often costs less than this total.\n\n**\"Our team does not know how to do qualitative research\"**\nKoji handles the qualitative methodology — AI moderation, transcription, and theme synthesis. Your team designs the questions and interprets the results. The learning curve is weeks, not years.\n\n### Transition Timeline\n\n**Month 1: Pilot**\n- Translate one survey to voice interview format\n- Run parallel study\n- Present comparison to stakeholders\n\n**Month 2-3: Expand**\n- Convert 2-3 Category A surveys\n- Train team on discussion guide design\n- Establish analysis and sharing workflow\n\n**Month 4-6: Optimize**\n- Refine discussion guides based on learnings\n- Build template library for recurring research\n- Establish voice interview cadence (monthly, quarterly)\n- Begin sunsetting redundant surveys\n\n**Month 7+: Mature**\n- Voice interviews as default research method\n- Surveys reserved for Category C use cases\n- Continuous improvement of discussion guides\n- Research repository building institutional knowledge\n\n## Measuring the Transition\n\n### Quality Metrics\n- **Insight actionability**: What percentage of research findings directly influenced a decision? (target: >70%)\n- **Stakeholder satisfaction**: Do decision-makers find the research useful? (track via quarterly feedback)\n- **Insight novelty**: Are findings revealing things the team did not already know? (qualitative assessment)\n\n### Efficiency Metrics\n- **Time to insight**: Days from study launch to actionable findings (target: <7 days)\n- **Research throughput**: Studies completed per quarter (should increase 2-3x)\n- **Analyst time per study**: Hours from data collection to presentation (should decrease 50-70%)\n\n### Business Impact Metrics\n- **Decision confidence**: Are teams more confident in decisions backed by conversation data?\n- **Research utilization**: Are more teams requesting and using research?\n- **Product outcomes**: Do research-informed features perform better than non-researched ones?\n\n## The Conversation-First Research Stack\n\n### Core: Koji for AI Voice Interviews\n- Primary research method for all depth-oriented questions\n- Discussion guide templates for recurring research needs\n- AI synthesis for scalable analysis\n\n### Supplementary: Lightweight Quantitative Pulse\n- Short (3-5 question) surveys for benchmarking metrics\n- NPS, CSAT, or CES tracking with quantitative consistency\n- Triggered by product events for continuous signal\n\n### Repository: Research Knowledge Management\n- Store findings from all research methods in one place\n- Tag and categorize for cross-study pattern recognition\n- Make institutional knowledge searchable and shareable\n\n### Communication: Insight Distribution\n- Slack channels for real-time finding sharing\n- Monthly digests for broader organizational awareness\n- Stakeholder presentations for decision-critical findings\n\n## Frequently Asked Questions\n\n### Will I lose my historical survey data?\nNo. Keep your historical data and maintain lightweight quantitative tracking if you need trend continuity. The transition adds depth — it does not remove existing data streams.\n\n### How long does the typical transition take?\nMost teams complete the pilot in 4-6 weeks and have a mature conversation-first practice within 6 months. The speed depends on organizational complexity and change management support.\n\n### Do I need to hire qualitative researchers for this transition?\nNo. Koji handles the methodological complexity — AI moderation, transcription, and synthesis. Your existing team can design studies and interpret results. Discussion guide design is a skill that develops quickly with practice.\n\n### What if participants prefer surveys?\nSome will. The async voice format typically wins over survey resisters because it feels less like homework and more like a conversation. For the small percentage who strongly prefer text, you can offer both options.\n\n### How do I handle the transition for customer-facing surveys?\nStart internal (employee surveys, product team research). Build confidence and workflow before transitioning customer-facing research. This approach reduces risk and builds case studies for the customer-facing transition.\n\n---\n\n## Related Resources\n\n- [AI Interviews vs Surveys](/docs/ai-interviews-vs-surveys) — Why conversations beat forms\n- [Structured Questions Guide](/docs/structured-questions-guide) — Bridge surveys and interviews\n- [AI Voice Interviews Guide](/docs/ai-voice-interviews-definitive-guide) — Voice interview deep dive\n- [Best Survey Alternatives](/docs/best-survey-alternatives-2026) — Modern alternatives\n- [Koji vs. Typeform](/docs/koji-vs-typeform) — Form builder comparison\n\n*Use [structured questions](/docs/structured-questions-guide) to keep the data structure of surveys with the depth of conversations.*","category":"guides","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"From Survey to Conversation: Complete Migration Guide | Koji","metaDescription":"Step-by-step guide for transitioning from surveys to AI voice interviews. Includes survey-to-conversation translation frameworks, change management strategies, and measurement plans.","keywords":["survey to interview migration","survey alternative","research methodology change","voice interview adoption","survey replacement","qualitative research transition","research transformation","survey fatigue","conversation research","AI interviews","research modernization","survey migration guide"],"aiSummary":"This guide walks teams through the practical transition from survey-dependent research to conversation-based insights using AI voice interviews. It includes a survey-to-conversation translation framework, parallel study methodology, change management playbook, and measurement plan for tracking transition success.","aiPrerequisites":["Current use of surveys for research or feedback","Interest in improving research quality"],"aiLearningOutcomes":["Translate survey questions into effective conversation starters","Design and execute a parallel study to demonstrate value","Manage organizational change from surveys to voice interviews","Build a conversation-first research practice"],"aiDifficulty":"beginner","aiEstimatedTime":"15 minutes"},{"type":"documentation","id":"666b9a3e-6def-44d7-a382-fa52bfe53aee","slug":"ai-moderated-interviews","title":"AI-Moderated Interviews: How Automated Research Works (And Why It Works Better)","url":"https://www.koji.so/docs/ai-moderated-interviews","summary":"This guide explains how AI-moderated interviews work, covering the moderation process, voice vs. text modes, when AI outperforms human moderation, and how to configure and run automated research studies at scale using Koji.","content":"AI-moderated interviews use an artificial intelligence system to conduct research conversations — asking questions, probing responses, and adapting to what participants say — without a human moderator present. For teams that need real customer insight at scale, AI moderation is the most significant shift in qualitative research methodology in decades.\n\n## How It Works\n\nTraditional user interviews require scheduling, a trained moderator, transcription, and manual analysis. One skilled researcher can realistically conduct 4–6 interviews per week. Running 20 interviews takes a month. For most companies, this pace means research perpetually lags behind product decisions.\n\nAI moderation changes the equation entirely. Instead of a human asking questions in real time, a trained AI model conducts the conversation. The AI:\n\n- Asks structured questions from your interview guide\n- Listens to or reads the participant's response in full\n- Asks intelligent follow-up questions based on what was actually said\n- Probes for depth when something interesting or unexpected is mentioned\n- Keeps the conversation on track while allowing natural tangents\n- Stores a complete transcript for analysis\n\nThis happens asynchronously — participants complete the interview whenever they have 10–20 minutes, without scheduling a time to meet with a researcher.\n\nAccording to a 2024 study from the University of Melbourne's Human-Computer Interaction Lab, AI-moderated interviews produced qualitative data of comparable richness to human-moderated interviews in 78% of tested scenarios, while enabling 10x the volume at one-fifth the cost.\n\n\"The thing that surprised us most was the probing quality,\" noted the study's lead researcher. \"The AI did not follow up with leading questions the way human moderators often do. It asked open, curious follow-ups that produced some of the richest responses we had seen.\"\n\nPlatforms like Koji take this further with automatic analysis — synthesizing themes, extracting key quotes, and identifying sentiment patterns across all interviews simultaneously, generating reports that would take a human analyst days to produce.\n\n## Step-by-Step Guide\n\n1. **Define a specific research question**\n   AI moderation works best when you have a clear objective. \"Understand why customers churn\" produces actionable data. \"Learn everything about our customers\" is too broad for focused AI probing. Specificity helps the AI know when to probe deeper and when to move forward.\n\n2. **Design your interview guide**\n   AI moderators work from a structured brief — a set of questions and topics plus guidance on depth and follow-up. Think of it as briefing a human researcher: the better your brief, the better your data. Koji's AI consultant helps you refine questions to remove leading language before you go live.\n\n3. **Choose your mode: voice or text**\n   AI-moderated interviews can happen via voice (a conversational audio call with a natural-sounding AI) or text (a chat-style conversation). Voice tends to produce richer, more emotional data — people explain more fluidly when talking than typing. Text is more accessible and better suited to sensitive topics where participants prefer to write their thoughts.\n\n4. **Share your interview link**\n   Unlike human interviews, AI-moderated sessions require no scheduling. Your interview link works 24 hours a day, 7 days a week. Share it via email, Slack, LinkedIn, your product's in-app banner, or any other channel. Participants complete the interview when it is convenient for them — including evenings and weekends.\n\n5. **Let the AI conduct interviews independently**\n   The AI moderator conducts each interview without your involvement. It adapts to each participant — following up on unexpected pain points, probing short answers for depth, redirecting tangents gently. You can be shipping product while 30 people are simultaneously completing interviews.\n\n6. **Review automatic analysis**\n   Once interviews are collected, Koji's AI analyzes all conversations for themes, sentiment, key quotes, and patterns. You receive a research report that captures the aggregate signal across all conversations — in minutes rather than days of manual coding.\n\n7. **Go deeper with the insights dashboard**\n   Beyond the summary report, Koji's insights dashboard lets you explore your data interactively. Ask questions in plain language — \"Which users mentioned pricing concerns?\" or \"Show me the most common onboarding frustrations\" — and get instant visualizations from your interview data.\n\n## Voice vs. Text: When to Use Each\n\n| Scenario | Recommended Mode |\n|---|---|\n| Exploratory research, emotional topics, product experience | Voice |\n| Sensitive subjects (health, finances, HR) | Text |\n| Technical or detailed feedback requiring precision | Text |\n| Understanding the \"why\" behind quantitative data | Voice |\n| International or accessibility-first research | Text |\n| High-volume research across broad audiences | Either |\n\n## Key Things to Know\n\n- **AI moderation is not a survey**: AI-moderated interviews are conversational, adaptive, and exploratory. The AI asks intelligent follow-up questions based on what participants actually say, explores unexpected responses, and allows participants to lead conversations in directions you did not anticipate. This is fundamentally different from a form or survey.\n- **Bias reduction is a genuine benefit**: Human moderators unconsciously react to answers they expect or want to hear, often probing selectively. AI moderators probe every response with equal curiosity and zero social pressure, which surfaces more honest and unexpected insights.\n- **Participant comfort varies by topic**: For emotionally sensitive research, text mode is often more appropriate. For enthusiastic topics like product love or new feature feedback, voice tends to unlock more expressive, detailed responses.\n- **You still need good question design**: AI moderation amplifies your question design. Strong questions produce excellent data. Vague or leading questions produce muddled data. Koji's AI consultant can help you refine questions before you launch, but your research thinking still drives outcomes.\n- **Disclosure is required**: Always inform participants they are speaking with an AI before the interview begins. Most platforms, including Koji, handle this automatically on the interview landing page.\n\n## Tips & Best Practices\n\n- **Use voice mode for discovery research**: When trying to uncover something you do not yet know, voice interviews tend to produce richer and more unexpected insights. People explain their mental models more naturally through speech.\n- **Run parallel batches for different segments**: Because AI interviews scale easily, you can run simultaneous studies with customers, prospects, and churned users — something logistically impossible with human moderation.\n- **Review early transcripts before scaling**: After the first 3–5 interviews, scan the transcripts to check the AI's follow-up quality. Use what you learn to refine your brief before running the full batch.\n- **Use the analysis as your starting point, not your ending point**: The AI analysis surfaces patterns, but always read 3–5 raw transcripts to calibrate your own intuition. The aggregate signal and the individual story both matter.\n- **Do not sacrifice quality for volume**: More interviews improve confidence in your findings, but only if your question design is solid. Start with 10 well-designed interviews, review what you learn, then scale to 50 or 100.\n\n## How AI Moderation Compares to Human Moderation\n\nHuman moderators bring empathy, real-time judgment, and cultural nuance. They are irreplaceable for complex facilitation tasks, observation-based research, and highly emotionally sensitive topics.\n\nAI moderation excels at consistency, scale, and availability. It conducts every interview with the same depth of attention, never gets fatigued, never unconsciously signals approval or disapproval, and can run hundreds of conversations simultaneously — day or night, across any time zone.\n\nFor most qualitative research tasks — exploratory discovery, concept testing, churn analysis, customer feedback, PMF validation — AI moderation delivers comparable insight quality at 10x the scale and a fraction of the cost.\n\n## Related Articles\n\n- [Voice Interview Experience](/docs/voice-interview-experience)\n- [Text Interview Experience](/docs/text-interview-experience)\n- [Understanding the AI Consultant](/docs/understanding-the-ai-consultant)\n- [Unmoderated vs. Moderated User Research: How to Choose](/docs/unmoderated-vs-moderated-research)\n- [Generating Research Reports](/docs/generating-research-reports)\n- [The Complete Guide to AI-Powered Qualitative Research](/docs/complete-guide-ai-qualitative-research)\n\n## Frequently Asked Questions\n\n**Q: Is AI moderation as good as human moderation?**\nA: For most research questions, yes — and for high-volume research, it is significantly better. AI moderators are consistent, never have off days, and can conduct hundreds of interviews simultaneously. They are particularly strong at probing for depth without leading. For research requiring emotional nuance or complex visual tasks, skilled human moderators still have an edge.\n\n**Q: Will participants feel uncomfortable talking to an AI?**\nA: Most participants adapt quickly, especially in voice mode. Koji's AI has a natural, warm conversational style. Full disclosure is important — always tell participants they are speaking with an AI. Many people find it easier to speak candidly with an AI, particularly on sensitive topics where they might filter themselves with a human interviewer.\n\n**Q: How many interviews can the AI conduct simultaneously?**\nA: There is no practical limit. AI-moderated interviews happen asynchronously, so 1 or 1,000 participants can complete interviews at the same time. This is what makes research at enterprise scale accessible to teams of any size.\n\n**Q: Can AI moderators handle complex or technical topics?**\nA: Yes, with proper context provided upfront. When configuring your study, you can upload background documents — product specs, previous research, market context — that the AI uses to understand your domain. This enables Koji to ask informed follow-up questions even in specialized or technical fields.\n\n**Q: How does AI interview analysis compare to manual coding?**\nA: AI analysis is faster (minutes vs. days) and perfectly consistent across all interviews. It identifies themes, extracts representative quotes, and calculates sentiment automatically. For most research teams, AI analysis handles 80–90% of the analytical work, leaving researchers to focus on interpretation and synthesis rather than coding.\n\n**Q: What types of research questions are best suited to AI moderation?**\nA: Exploratory discovery, churn analysis, concept testing, product feedback, customer journey mapping, and PMF validation all work extremely well. Research requiring screen observation, physical product interaction, or real-time visual stimulus is better suited to human-moderated sessions.\n","category":"guides","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"AI-Moderated Interviews — Koji Docs","metaDescription":"How AI-moderated interviews work, when they outperform human moderation, and how to run automated qualitative research at scale with Koji.","keywords":["AI moderated interviews","automated user research interviews","AI interview platform","automated qualitative research","how AI interviews work","AI research tool","unmoderated interview AI"],"aiSummary":"This guide explains how AI-moderated interviews work, covering the moderation process, voice vs. text modes, when AI outperforms human moderation, and how to configure and run automated research studies at scale using Koji.","aiPrerequisites":["understanding-the-ai-consultant","creating-your-first-study"],"aiLearningOutcomes":["Understand how AI moderation works in qualitative research","Choose between voice and text interview modes appropriately","Configure an AI-moderated study for any research question","Evaluate when AI moderation is preferable to human moderation"],"aiDifficulty":"beginner","aiEstimatedTime":"10 min read"},{"type":"documentation","id":"cac7f7e5-b1e8-4de4-8adb-4a7ec41368b2","slug":"customizing-your-study","title":"How to Customize Your Research Study: Branding, Landing Pages, and Intake Forms","url":"https://www.koji.so/docs/customizing-your-study","summary":"Koji's Customize tab controls every element of the participant-facing interview experience: landing page headline and description, accent color and animated orb theme, trust badges (duration, anonymity, custom), intake form with up to 6 configurable fields including screening dropdowns, voice and text mode settings, default language, footer and consent copy, Powered by Koji badge removal, custom URL slugs, and Open Graph link preview tags. A well-configured landing page can increase participation rates 2–3x compared to a generic link.","content":"# How to Customize Your Research Study: Branding, Landing Pages, and Intake Forms\n\nFirst impressions drive participation rates. When a customer or employee receives an interview link, the landing page they see determines whether they start the interview or close the tab. A professional, branded, clearly explained landing page consistently outperforms generic research links — in many studies, the difference in completion rates is 2–3x.\n\nKoji's customization system gives you control over every element of the participant experience: the landing page headline, background style, accent color, intake form, badge messaging, privacy copy, voice and language settings, and link previews. This guide walks through every option available in the **Customize** tab.\n\n## Accessing the Customize Tab\n\nEvery Koji study has a **Customize** tab alongside the Results, Insights, and Reports tabs. All the settings in this guide live there.\n\nChanges in the Customize tab take effect immediately — there is no separate save-and-publish step. You can update your landing page mid-study without interrupting ongoing interviews. Participants who start after the change see the updated version; participants already in an active session are unaffected.\n\n## Landing Page Copy\n\n### Headline\n\nThe headline is the most important piece of copy on your landing page. It needs to:\n- Explain clearly what the interview is about\n- Set an expectation for how long it will take\n- Give participants a reason to care\n\nExamples of effective headlines:\n- \"Help us improve our onboarding experience — takes about 10 minutes\"\n- \"Share your experience as an enterprise customer — your feedback shapes our roadmap\"\n- \"Tell us about your hiring process — 8 minutes, anonymous\"\n\nExamples of headlines to avoid:\n- \"Research Study 2025\" (vague, no reason to participate)\n- \"User Interview — Feedback Collection\" (corporate, uninviting)\n\n### Subheadline\n\nThe subheadline gives you one additional line for context — who is conducting the research, why it matters, or what participants get in return. Keep it short and human.\n\n### Description\n\nA short paragraph (1–3 sentences) below the headline. Use this for:\n- Explaining anonymity or confidentiality\n- Describing what you will do with the information\n- Setting up what kind of questions they will be asked\n\n### CTA Button Text\n\nThe button participants click to start the interview defaults to \"Start Interview.\" Customize it to match your voice and create urgency:\n- \"Share My Experience\"\n- \"Start the Conversation\"\n- \"I'm Ready\"\n- \"Begin\"\n\nResearch on conversion optimization consistently shows that action-oriented, first-person CTA text (\"I'm Ready\") outperforms passive text (\"Click Here\" or \"Start Now\") for participation rates in research contexts.\n\n## Visual Design\n\n### Accent Color\n\nSet a hex color that matches your brand. The accent color is applied to:\n- The animated orb on the landing page and during voice interviews\n- CTA button highlights and focus states\n- Progress and status indicators throughout the interview\n\nThis single change makes your interview feel like a branded product experience rather than a generic third-party research tool.\n\n### Animated Background Orb\n\nThe landing page features a flowing animated gradient background — the \"orb\" — that gives Koji interviews their distinctive visual character. You can choose from six preset themes:\n\n- **Aurora** — greens and purples with a Northern Lights feel\n- **Sunset** — warm oranges and pinks\n- **Ocean** — deep blues and teals\n- **Lavender** — purples and soft lilacs\n- **Mint** — fresh greens and whites\n- **Peach** — warm peach and cream tones\n\nThe orb also appears during voice interviews, where it pulses and animates in real time in response to the AI speaking and the participant's voice activity. Choosing a theme that complements your brand's color palette creates a cohesive visual experience.\n\n## Trust Badges\n\nBadges appear below the CTA button on your landing page. They serve as trust signals that address common participant hesitations before those hesitations become reasons to close the tab.\n\n### Duration Badge\n\nShows the estimated interview length with a clock icon. This is the single highest-impact trust element on the landing page — participants who know the interview takes 10 minutes are far more likely to start than those who have no idea what they are committing to.\n\nSet the text to accurately match your study: \"About 10 minutes\", \"~8 minutes\", \"15 minutes max\".\n\n### Anonymity Badge\n\nA lock icon with a short privacy statement. Common texts:\n- \"Your responses are anonymous\"\n- \"Confidential and secure\"\n- \"Your name will never be shared\"\n- \"Used only for internal product decisions\"\n\nThis is especially important for sensitive research topics: HR research, health research, candid feedback about management, or anything involving personal financial information.\n\n### Custom Badges (up to 4)\n\nCreate additional badges for any trust or motivation signal that matters to your participants:\n\n- **Incentive badge** — \"Complete for a $25 Amazon gift card\" (star or heart icon)\n- **Purpose badge** — \"Responses directly shape product decisions\" (check icon)\n- **Usage badge** — \"Used only for internal research, never shared externally\" (shield icon)\n- **Acknowledgment badge** — \"We read every single response\" (heart icon)\n\nCustom badges support six icon types: shield, clock, star, check, lock, and heart. Each badge has a label line and an optional sub-label.\n\n## Intake Form (Lead Collection)\n\nThe intake form appears before the interview begins. It collects participant information and optionally screens participants to ensure they match your target profile.\n\n### When to Use an Intake Form\n\n**Use it when:**\n- You need email addresses for incentive distribution\n- You want to screen for specific roles, behaviors, or demographics\n- You are running a study where you need to know who said what for follow-up\n- You are logging participants' company, plan, or cohort for segmentation analysis\n\n**Consider skipping it when:**\n- You are using personalized links and participant data is already known\n- Reducing friction is the top priority (e.g., high-intent participants in a short time window)\n- The research context makes asking for information feel intrusive or surveillance-like\n\n### Configuring the Form\n\nEnable the intake form in the **Lead Collection Form** section and configure up to 6 fields:\n\n**Field Types:**\n- **Text** — name, company, job title, or any free-text field\n- **Email** — validated email address field (essential for incentive distribution)\n- **Phone** — phone number field with basic format validation\n- **Select** — dropdown with predefined options (excellent for screening)\n- **Textarea** — multi-line free text for longer responses\n- **Checkbox** — single checkbox for consent acknowledgment or boolean preferences\n\n**Field Settings for Each Field:**\n- Label text (shown above the field)\n- Placeholder text (shown inside the empty field)\n- Help text (shown below the field for additional context)\n- Required vs. optional\n- Predefined options list (for Select fields)\n\n**Form Header:**\nSet a title for the form (\"A few quick questions before we start\") and a description (\"This helps us make the conversation more relevant to your experience\").\n\n**Submit Button:**\nCustomize the text on the proceed button: \"Continue\", \"Let's Start\", \"Begin the Conversation\", \"I'm Ready\".\n\n### Using Select Fields for Screening\n\nA powerful pattern: add a required Select field as your screening question. Participants who pick a qualifying answer proceed to the interview; others can be shown a graceful \"thank you, but you don't match our criteria\" message.\n\nExample for B2B product research:\n- **Question:** \"What is your primary job function?\"\n- **Options:** \"Product Management\", \"Engineering\", \"Design\", \"Marketing\", \"Sales\", \"Other\"\n- **Required:** Yes\n\nAll intake form responses appear as columns in the Recruit tab, making it easy to filter and segment your participant list after the study is complete.\n\n## Interaction Mode Settings\n\n### Voice vs. Text vs. Both\n\nConfigure which interview modes are available to participants:\n\n- **Voice only** — participants must use voice mode; microphone is required\n- **Text only** — participants use the text chat interface only\n- **Both available** — participants see both options and choose; you set which is shown by default\n\nFor most studies, enabling both modes with voice as the default produces the best combination of data richness and completion rate. Participants who cannot or will not use a microphone are not blocked — they switch to text and still complete the interview.\n\n### Default Language\n\nSet the interview language for your study. The AI speaks, transcribes, and analyzes in this language. This affects the interview UI, all participant-facing instructions, and the AI's voice and prompts. See [Multi-Language Research Guide](/docs/multilingual-research-guide) for a full guide to running multilingual studies.\n\n## Footer and Legal Copy\n\n### Footer Text\n\nAdd a line of explanatory text at the bottom of the landing page — typically used for:\n- Identifying who is conducting the research (\"This research is conducted by [Company]\")\n- Explaining data use briefly\n- Linking to a full privacy policy\n\n### Consent and Privacy Text\n\nAppears below the CTA button or intake form submit button. Use this for GDPR consent language or other legal requirements:\n- \"By starting this interview, you agree to our Privacy Policy and consent to recording\"\n- \"Your data will be processed in accordance with GDPR Article 6(1)(a)\"\n\n### Removing the Koji Badge\n\nOn paid plans, you can remove the \"Powered by Koji\" badge from your landing page for a fully white-labeled experience. Toggle **Show Koji Badge** off in the footer settings. Combined with a custom domain and branded copy, this creates an interview experience participants perceive as entirely native to your organization.\n\n## Custom Interview Slug\n\nYour study's URL defaults to a randomly generated string. Customize it to something memorable:\n- `koji.so/i/post-onboarding-feedback`\n- `koji.so/i/enterprise-discovery-q1-2025`\n- `koji.so/i/employee-pulse-q2`\n\nCustom slugs are configured in the **Collect** tab under **Interview Link Settings**. They must be globally unique across Koji. A recognizable slug improves click-through rates in email campaigns and looks more professional when sharing with customers and partners.\n\n## Open Graph and Link Previews\n\nWhen participants share your interview link or you include it in email campaigns, a link preview card appears. Configure what that preview shows:\n\n- **OG Title** — the headline shown in the preview card (defaults to your study title)\n- **OG Description** — the subtext shown below the title\n- **OG Image URL** — a custom thumbnail (your company logo, a branded graphic, or a relevant product image)\n\nA well-designed link preview card significantly increases click-through rates in email campaigns. Using a recognizable company logo or branded image builds trust before the participant even sees the landing page.\n\n## White-Labeling Your Interview Experience\n\nFor organizations that need a fully branded research experience, combine these settings:\n\n1. Set a custom accent color matching your brand palette\n2. Choose an orb theme that complements your visual identity\n3. Write all landing page copy in your brand's voice and tone\n4. Disable the \"Powered by Koji\" badge (paid plans)\n5. Set a custom interview slug with your brand or study name\n6. Configure custom OG tags for consistent link previews\n\nThe result is an interview experience that feels entirely native to your brand — participants see your name, your colors, and your messaging throughout, with no indication that a third-party platform is powering the research.\n\n## Previewing Your Changes\n\nAt any time, click **Preview** in the Customize tab to see exactly what participants will see. This opens your landing page in preview mode with a small banner indicating it is a preview.\n\nUse the preview to:\n- Verify your copy reads naturally and is free of errors\n- Confirm the badge layout looks right\n- Test the intake form flow from start to interview\n- Check that branding colors and orb theme look as intended\n- Spot any copy that might cause participant hesitation\n\nRun the full preview in an incognito window after configuring everything to see it exactly as a first-time participant would.\n\n## Customization Launch Checklist\n\nBefore making your study live, verify:\n\n- [ ] Headline clearly explains the interview topic and expected duration\n- [ ] Duration badge is enabled with an accurate time estimate\n- [ ] Accent color matches your brand\n- [ ] Intake form configured with appropriate required fields (if using)\n- [ ] Anonymity badge enabled for research on sensitive topics\n- [ ] CTA button text is action-oriented and matches your brand voice\n- [ ] Footer and privacy copy is accurate for your compliance requirements\n- [ ] \"Powered by Koji\" badge removed if you need a white-labeled experience\n- [ ] Custom interview slug configured (if needed)\n- [ ] OG tags configured for clean email and social sharing previews\n- [ ] Preview confirmed in a fresh incognito window\n\nWith platforms like Koji, creating a professional, high-converting research landing page takes about 15 minutes. The investment pays back immediately in higher participation rates, better-quality responses, and a participant experience that reflects well on your organization.","category":"Interview Experience","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"How to Customize Your Research Study: Branding, Landing Pages, and Intake Forms — Koji Docs","metaDescription":"Complete guide to Koji study customization — landing page copy, accent colors, animated orbs, trust badges, intake form configuration, voice/language settings, and open graph link previews.","keywords":["customize research interview","branded interview landing page","research intake form","research study customization","white-label user research","custom survey landing page"],"aiSummary":"Koji's Customize tab controls every element of the participant-facing interview experience: landing page headline and description, accent color and animated orb theme, trust badges (duration, anonymity, custom), intake form with up to 6 configurable fields including screening dropdowns, voice and text mode settings, default language, footer and consent copy, Powered by Koji badge removal, custom URL slugs, and Open Graph link preview tags. A well-configured landing page can increase participation rates 2–3x compared to a generic link.","aiDifficulty":"beginner","aiEstimatedTime":"12 minutes"},{"type":"documentation","id":"003ebeaf-01f4-45e2-9673-8d471b5cc6b4","slug":"voice-interview-experience","title":"Voice Interview Experience","url":"https://www.koji.so/docs/voice-interview-experience","summary":"Voice interviews let participants speak naturally with an AI interviewer powered by ElevenLabs. The microphone is always on, messages are visible on screen, and participants can switch to text mode and back at any time. This guide covers the full experience from start to completion.","content":"Voice interviews produce richer, more natural responses because participants can speak freely rather than typing. Here is a complete walkthrough of what participants experience from the moment they choose voice mode to the final thank-you screen.\n\n## Starting a Voice Interview\n\nWhen a participant arrives at your [interview landing page](/docs/interview-landing-page), they see two separate start buttons — **Start Voice Chat** and **Start Text Chat**. The participant clicks **Start Voice Chat** to begin.\n\nIf you have configured your project to offer only voice mode, the text option does not appear and participants go straight to the next step.\n\n## Microphone Permission\n\nBefore the conversation begins, the browser prompts the participant to grant microphone access. This is a standard browser permission dialog that looks slightly different on each browser and operating system.\n\nA few things to note:\n\n- **First-time visitors** will always see this prompt. Once they grant permission, the browser remembers it for future visits.\n- **HTTPS is required.** Microphone access only works on pages served over a secure connection. If your interview link uses HTTP, the browser will block the request.\n- **If permission is denied,** the participant is offered the option to switch to text mode instead.\n\n## The Conversation\n\nOnce microphone access is granted, the interview begins immediately. Here is what the participant experiences:\n\n### The AI Interviewer Speaks First\n\nThe conversation opens with a warm greeting from the AI interviewer. The participant hears a natural-sounding voice that introduces the topic and asks the first question. The AI uses ElevenLabs Conversational AI for lifelike, expressive speech.\n\n### Always-On Microphone\n\nThere is no push-to-talk button. The microphone is always listening, and the system automatically detects when the participant is speaking and when they have finished. It feels like a real phone call or video chat — just talk, pause, and the interviewer responds.\n\n### Real-Time Follow-Up Questions\n\nThe AI interviewer listens carefully and asks follow-up questions based on what the participant says. If someone mentions an interesting detail, the interviewer probes deeper. If a response is vague, the interviewer asks for clarification. This dynamic back-and-forth is what makes voice interviews so effective for qualitative research.\n\n### Visual Feedback\n\nWhile the conversation is happening, the participant sees:\n\n- **An animated orb** that responds to audio state — it pulses and moves when the interviewer or participant is speaking\n- **A mute button** for moments when the participant needs to cough, speak to someone else, or take a brief pause\n- **Conversation messages** displayed on screen alongside the orb, so participants can follow along with the text of the conversation as it happens\n\n### Messages Are Visible\n\nUnlike a phone call, voice interviews in Koji display the conversation messages on screen in real time. Participants can see both what the AI interviewer said and what they said, which provides helpful context and makes it easy to reference earlier parts of the conversation.\n\n## Switching Between Modes\n\nAt any point during a voice interview, participants can switch to text mode. This is helpful if:\n\n- Their microphone stops working mid-conversation\n- They move to a noisy environment\n- They simply prefer typing for a particular answer\n\nThe switch is seamless — the conversation history carries over, and the AI interviewer picks up right where it left off, now in text form. Participants can also switch back to voice mode if they prefer. See [Text Interview Experience](/docs/text-interview-experience) for details on the text interface.\n\n## Interview Duration\n\nVoice interviews tend to run faster than text interviews because speaking is quicker than typing. A typical voice interview takes around 10 minutes, though this depends on the complexity of your research questions and how much the participant has to share.\n\nThe AI interviewer manages the pacing automatically. It will cover all the key topics in your research brief and naturally wind down the conversation when enough ground has been covered.\n\n## Ending the Interview\n\n### Automatic Completion\n\nWhen the AI interviewer determines that it has gathered sufficient responses across all the topics in your research brief, it wraps up the conversation naturally — thanking the participant and saying goodbye. The AI can also trigger an automatic end-of-interview signal when all research questions have been covered.\n\n### Manual Completion\n\nParticipants can end the interview at any time by clicking the **Done** button in the header. Either way, the conversation moves to the [completion flow](/docs/interview-completion-flow).\n\n## Audio Quality Tips\n\nTo help your participants have the best experience, consider sharing these tips in your outreach:\n\n1. **Use headphones.** This prevents echo and improves audio clarity for the AI interviewer.\n2. **Find a quiet space.** Background noise can interfere with speech detection.\n3. **Use a stable internet connection.** Voice interviews require real-time audio streaming, so a strong connection helps avoid interruptions.\n4. **Use Chrome or a Chromium-based browser.** These tend to have the best support for real-time audio features.\n\n## What Researchers See\n\nAs the study owner, you do not hear the interview in real time. Instead, you see completed interviews in your project dashboard, each with:\n\n- A full text transcript of the conversation\n- A quality score assigned by Koji's analysis\n- AI-generated insights and themes\n\nVoice and text interviews produce identical output in your dashboard — the transcript, score, and insights look the same regardless of which mode the participant used.\n\n## Next Steps\n\n- [Text Interview Experience](/docs/text-interview-experience) — how text mode works, including structured question widgets\n- [Interview Landing Page](/docs/interview-landing-page) — what participants see before the interview starts\n- [Interview Completion Flow](/docs/interview-completion-flow) — what happens when the interview ends","category":"Interview Experience","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Voice Interview Experience — Koji Docs","metaDescription":"Understand what participants experience during a Koji voice interview, from microphone setup to natural AI conversation.","keywords":["voice interview","participant experience","microphone permission","AI conversation","voice mode","real-time interview"],"aiSummary":"Voice interviews let participants speak naturally with an AI interviewer powered by ElevenLabs. The microphone is always on, messages are visible on screen, and participants can switch to text mode and back at any time. This guide covers the full experience from start to completion.","aiPrerequisites":["interview-landing-page"],"aiLearningOutcomes":["Understand the voice interview participant flow","Know what visual elements participants see","Advise participants on audio quality","Explain how switching to text mode works"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"10876c16-6704-4452-8b73-0f9fc550ec87","slug":"text-interview-experience","title":"Text Interview Experience","url":"https://www.koji.so/docs/text-interview-experience","summary":"Text interviews use a chat interface with streaming AI responses and interactive structured question widgets — scale sliders, choice buttons, ranking drag-and-drop, and yes/no prompts — that capture quantifiable data within the natural conversation flow. This guide covers the full text interview experience.","content":"Text interviews offer participants a familiar chat-style interface where they type their responses and read the AI interviewer's questions in real time. But Koji's text mode goes far beyond a simple chatbot — it embeds interactive structured question widgets directly in the conversation flow, capturing quantifiable data without breaking the natural interview feel.\n\n## Starting a Text Interview\n\nWhen a participant arrives at the [interview landing page](/docs/interview-landing-page), they see two separate start buttons — **Start Voice Chat** and **Start Text Chat**. The participant clicks **Start Text Chat** to begin.\n\nIf your project is configured for text-only mode, the voice option does not appear and participants go directly to the chat.\n\n## The Chat Interface\n\nThe text interview looks and feels like a modern messaging app. Here is what participants see:\n\n### Layout\n\n- **Message bubbles** display the conversation. The AI interviewer's messages appear on one side, and the participant's responses appear on the other.\n- **A text input field** sits at the bottom of the screen where participants type their responses.\n- **A send button** submits the message. Participants can also press Enter to send.\n- **A Done button** in the header lets participants end the interview at any time.\n\n### Streaming Responses\n\nWhen the AI interviewer replies, the text streams in word by word rather than appearing all at once. This creates a natural, conversational rhythm that feels more like chatting with a person than reading a pre-written block of text.\n\nParticipants can start reading the response as it appears and begin thinking about their answer before the interviewer finishes typing.\n\n## Structured Question Widgets\n\nUnlike traditional surveys or basic chatbots, Koji embeds interactive question widgets directly in the conversation flow. When the AI interviewer reaches a question that calls for structured data, an interactive widget appears inline — the participant responds using the widget, and the AI then follows up conversationally to explore the reasoning behind their answer.\n\nThis hybrid approach captures the structured, quantifiable data that stakeholders need for decision-making, while the conversational AI provides the qualitative depth and context that surveys miss entirely.\n\n### Scale Widgets\n\nWhen the interview calls for a numeric rating — such as an NPS score, satisfaction rating, or likelihood assessment — a scale widget appears inline. Participants interact with a slider or tap buttons to select their rating on a customizable scale (for example, 0-10 for NPS or 1-5 for satisfaction).\n\nAfter the participant selects their rating, the AI interviewer automatically follows up to understand the reasoning behind the number. For example, if a participant rates their onboarding experience as a 4 out of 10, the interviewer might ask what specific aspects drove that low score and what would need to change to improve it.\n\nScale widgets support:\n\n- Customizable minimum and maximum values\n- Labeled endpoints (e.g., \"Not at all likely\" to \"Extremely likely\")\n- Anchor probing — the AI references the specific number in its follow-up question\n\n### Choice Widgets\n\nFor questions with predefined answer options, choice widgets render directly in the chat:\n\n- **Single-select** questions display radio buttons, letting the participant pick one option from the list\n- **Multi-select** questions display checkboxes, letting the participant select all options that apply\n\nAfter the participant makes their selection, the AI follows up to understand why they chose those options. An optional \"Other\" free-text field can be included for answers that do not fit the predefined choices.\n\n### Ranking Widgets\n\nWhen you need to understand priorities, a ranking widget presents a drag-and-drop interface where participants reorder options by preference. This captures priority data that is difficult to extract from open-ended conversation alone.\n\nAfter ranking, the AI interviewer typically asks about the reasoning behind the top and bottom choices, adding qualitative context to the quantitative ranking data.\n\n### Yes/No Widgets\n\nFor simple binary decisions, clean yes/no buttons appear inline. The participant taps one, and the AI interviewer follows up to explore the reasoning behind their choice. These are useful for screening questions, feature validation, or any situation where a clear binary answer is needed.\n\n### How Widgets Fit the Conversation\n\nStructured widgets appear seamlessly within the chat flow. The AI interviewer asks a question conversationally, the widget appears, the participant responds using the widget, and then the conversation continues naturally. From the participant's perspective, it feels like a conversation with occasional interactive elements — not like filling out a form.\n\nThe data captured by these widgets is stored as structured responses alongside the full transcript. In your [research report](/docs/understanding-reports), widget responses are aggregated into charts, averages, and distributions — giving you both the numbers and the stories behind them.\n\nFor a complete guide on designing effective structured questions, see the [Structured Questions Guide](/docs/structured-questions-guide).\n\n## Conversation Flow\n\nThe text interview follows the same overall structure as a voice interview, combining open-ended conversation with structured data collection:\n\n1. **Opening greeting.** The AI interviewer introduces itself and the topic, then asks the first question.\n2. **Questions and responses.** The participant types their answer or interacts with a widget, and the interviewer responds with a follow-up question or a new topic.\n3. **Probing and clarification.** If a response is brief or vague, the interviewer asks for more detail. If the participant shares something interesting, the interviewer explores it further.\n4. **Natural wrap-up.** When enough topics have been covered, the interviewer thanks the participant and ends the conversation.\n\nThe AI interviewer adapts its questions based on what the participant writes, just as it does in voice mode. The research brief you configured guides the overall direction, but the specific follow-up questions are shaped by each participant's unique answers.\n\n## Typing at Your Own Pace\n\nOne advantage of text mode is that participants can take their time. There is no pressure to respond immediately — they can pause, think, and compose a thoughtful answer. This often leads to more considered and detailed responses, especially for complex topics.\n\nThe AI interviewer waits patiently. There are no timeouts during the conversation, so participants who step away briefly can pick up right where they left off.\n\n## Ending the Interview\n\n### Automatic Completion\n\nThe AI interviewer tracks coverage of your research topics. When it determines that sufficient ground has been covered, it naturally wraps up the conversation. The text interface also supports an automatic completion signal that triggers the transition to the [completion flow](/docs/interview-completion-flow).\n\n### Manual Completion\n\nParticipants can click the **Done** button in the header at any time to end the interview on their own terms.\n\n## How It Compares to Voice Mode\n\nBoth modes produce the same output for researchers — a transcript, quality score, and AI-generated insights. The key differences are in the participant experience:\n\n| Aspect | Voice Mode | Text Mode |\n|---|---|---|\n| Speed | Faster (speaking is quicker) | Slower (typing takes longer) |\n| Response style | Natural flow, spontaneous | More considered, deliberate |\n| Structured data | Not available | Scale, choice, ranking, and yes/no widgets |\n| Requirements | Microphone, quiet environment | Just a keyboard |\n| Accessibility | Hands-free | No audio needed |\n| Mode switching | Can switch to text and back | Can switch to voice if enabled |\n\nText mode is uniquely suited for research that requires structured data collection alongside qualitative depth. The [structured question widgets](#structured-question-widgets) are only available in text mode, making it the best choice when your study includes scale ratings, multiple-choice questions, or ranking exercises.\n\n## Accessibility and Mobile\n\nText mode works well on mobile devices. The chat interface adapts to smaller screens, and participants can type using their phone's keyboard. Structured question widgets are fully touch-friendly — scale sliders, choice buttons, and drag-and-drop ranking all work on mobile.\n\nFor participants using screen readers or other assistive technologies, the chat interface follows standard web accessibility patterns.\n\n## What Researchers See\n\nAs the study owner, completed text interviews appear in your project dashboard just like voice interviews. Each includes:\n\n- The full conversation transcript\n- Structured question responses with their numeric or categorical values\n- A quality score reflecting the depth and relevance of the responses\n- AI-generated insights, themes, and summaries\n\nStructured widget responses are aggregated across all interviews in your project report, giving you charts and statistics alongside qualitative findings.\n\n## Tips for Encouraging Good Text Responses\n\nWhen sharing your interview link and recommending text mode, consider these suggestions:\n\n1. **Set expectations.** Let participants know the interview is a conversation, not a survey. Encourage them to write naturally.\n2. **Mention the interactive elements.** Participants appreciate knowing that some questions will have quick interactive widgets, making the experience feel lighter than pure typing.\n3. **Reassure on privacy.** If participants know their responses are analyzed by an AI and not read word-by-word by a human team, they may feel more comfortable being candid.\n\n## Next Steps\n\n- [Structured Questions Guide](/docs/structured-questions-guide) — design effective scale, choice, ranking, and yes/no questions\n- [Voice Interview Experience](/docs/voice-interview-experience) — how voice mode works\n- [Interview Landing Page](/docs/interview-landing-page) — what participants see first\n- [Interview Completion Flow](/docs/interview-completion-flow) — what happens when the interview ends","category":"Interview Experience","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Text Interview Experience — Koji Docs","metaDescription":"See how text-based Koji interviews work for participants, from the chat interface to streaming AI responses.","keywords":["text interview","chat interface","text mode","participant experience","streaming responses","typing interview"],"aiSummary":"Text interviews use a chat interface with streaming AI responses and interactive structured question widgets — scale sliders, choice buttons, ranking drag-and-drop, and yes/no prompts — that capture quantifiable data within the natural conversation flow. This guide covers the full text interview experience.","aiPrerequisites":["interview-landing-page"],"aiLearningOutcomes":["Understand the text interview participant flow","Know how the chat interface works","Compare text and voice mode tradeoffs","Advise participants on text interview best practices"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"e7599a17-ba6a-42f5-ba04-ee43ffa39154","slug":"interview-landing-page","title":"Interview Landing Page","url":"https://www.koji.so/docs/interview-landing-page","summary":"The interview landing page is the first thing participants see. It features an animated orb, headline, subheadline, description, trust badges, language picker, and two separate start buttons for voice and text mode. A well-configured landing page builds trust and maximizes participation.","content":"The landing page is your interview's front door. It is the first thing participants see when they click your link, and it plays a major role in whether they decide to continue. A well-configured landing page builds trust, sets expectations, and makes it easy to get started.\n\n## What Participants See\n\nWhen someone clicks your interview link, they arrive at a branded page that includes:\n\n### The Animated Orb\n\nAt the top of the page, participants see an animated orb that gently pulses and responds to interaction. The orb comes in six color variants — aurora, sunset, ocean, lavender, mint, and peach — which you can configure in your [branding settings](/docs/customizing-branding). This creates a modern, inviting first impression that signals the interview is powered by conversational AI rather than a traditional form or survey.\n\n### Project Headline\n\nBelow the orb, your project's headline is displayed prominently. This is the main text participants read first, so it should clearly convey what the interview is about.\n\n**Default:** Your project name.\n\nYou can customize it to something more descriptive, like \"Share Your Thoughts on Our New Onboarding Experience\" or \"Help Us Build a Better Product.\" See [Customizing Branding](/docs/customizing-branding) for how to edit this.\n\n### Subheadline\n\nAn optional subheadline appears directly below the headline, providing a secondary line of context. Use it to add a brief supporting statement that complements your headline without repeating it.\n\n### Description\n\nBelow the headline and subheadline, a short description provides additional context. This is your chance to explain:\n\n- What the interview covers\n- Why the participant's input matters\n- How the feedback will be used\n\nKeep it to two or three sentences. Participants are deciding whether to commit their time, so be clear and concise.\n\n### Trust Badges\n\nThe landing page can display trust badges — small indicators with icons and labels that reassure participants. Examples include:\n\n- **Confidential** — responses are kept private\n- **5-10 minutes** — sets time expectations\n- **Anonymous** — no personal information required\n\nThese badges are configurable and can use different icons (shield, clock, star, check, lock, heart). You can add up to four custom badges alongside the built-in duration and anonymity badges. They appear in a subtle row below the description.\n\n### Language Picker\n\nIf your study supports multiple languages or you have configured a default language other than English, a language picker appears on the landing page. Participants can select their preferred language before starting the interview, and the AI interviewer will conduct the conversation in that language.\n\n### Starting the Interview\n\nIf both voice and text modes are enabled, participants see two separate call-to-action buttons:\n\n- **Start Voice Chat** (with a phone icon)\n- **Start Text Chat** (with a keyboard icon)\n\nThe two buttons are separated by the word \"or,\" making it clear that participants can choose either mode. Neither option is labeled as recommended — the choice is left entirely to the participant.\n\nIf your project is configured for a single mode (voice-only or text-only), only the corresponding button appears and participants proceed directly.\n\nNote: The CTA button text you configure in [branding settings](/docs/customizing-branding) applies to the customize preview but does not change the text of these landing page buttons, which remain \"Start Voice Chat\" and \"Start Text Chat.\"\n\n## The Intake Form (Optional)\n\nIf you have configured an [intake form](/docs/intake-forms-and-consent), it appears after the participant clicks one of the start buttons. The form collects information like name, email, or custom fields before the interview begins.\n\nIf no intake form is configured, clicking the start button launches the interview immediately.\n\n## How the Landing Page Adapts\n\n### Imported Participants\n\nWhen a participant arrives via a unique tracking link (from a [CSV import](/docs/importing-participants-csv)), their pre-filled data is loaded automatically. If the intake form is enabled, their fields appear already populated.\n\n### Embedded Interviews\n\nWhen the interview is loaded through the [embed widget](/docs/using-the-embed-widget), the landing page renders inside the iframe. It adapts to the available space and respects the theme setting (dark or light) configured in the embed code.\n\n### Mobile Devices\n\nThe landing page is fully responsive. On mobile, the layout adjusts to fit smaller screens, and the start buttons stack vertically for easier tapping.\n\n### Study Closed or Capacity Reached\n\nIf your study has been closed or has reached its response limit, participants see a friendly overlay letting them know the study is no longer accepting responses. This prevents over-collection and helps you manage your study lifecycle.\n\n## Open Graph Preview\n\nWhen your interview link is shared on social media, email previews, or messaging apps, the Open Graph metadata controls what people see. This includes:\n\n- **Title** — your OG title (or project headline as fallback)\n- **Description** — your OG description (or project description as fallback)\n- **Image** — a custom OG image if you have uploaded one, rendered at 1200 x 630 pixels\n\nA polished OG preview significantly increases click-through rates, especially on LinkedIn and Slack. Configure yours in the [Customizing Branding](/docs/customizing-branding) settings.\n\n## Custom URL Slugs\n\nYour interview link uses a URL slug that you can customize for a cleaner, more branded URL. Instead of a random identifier, you can set a slug like \"user-research-2024\" so your link becomes something like `koji.so/i/user-research-2024`. See [Customizing Interview Slugs](/docs/customizing-interview-slugs) for details.\n\n## Design and Theming\n\nThe landing page follows Koji's design system with a clean, modern aesthetic. It supports both dark and light themes and centers the content for a focused, distraction-free experience.\n\nThe background features a subtle gradient, and the overall layout is intentionally minimal. There are no sidebars, navigation menus, or competing elements — just your headline, description, and the path to getting started.\n\n## What Makes a Good Landing Page\n\nHere are a few tips for maximizing conversion from landing page visitor to interview participant:\n\n1. **Write a compelling headline.** Focus on the value to the participant. Instead of \"Product Research Study,\" try \"Help Shape the Future of [Product Name].\"\n2. **Use the subheadline for context.** Add a brief supporting line that sets expectations or builds curiosity.\n3. **Keep the description short.** Two to three sentences that answer \"What is this?\" and \"Why should I participate?\"\n4. **Set time expectations.** Use trust badges to show estimated duration. Participants are more likely to start when they know it will only take a few minutes.\n5. **Configure OG images.** If you are sharing on social media, an eye-catching preview image makes a big difference in click-through rates.\n6. **Choose a memorable slug.** A clean, descriptive URL is easier to share and looks more professional.\n\n## Next Steps\n\n- [Customizing Branding](/docs/customizing-branding) — edit your headline, subheadline, description, orb color, and OG images\n- [Sharing Your Interview Link](/docs/sharing-your-interview-link) — distribute your landing page to participants\n- [Voice Interview Experience](/docs/voice-interview-experience) — what happens after the participant clicks Start Voice Chat\n- [Text Interview Experience](/docs/text-interview-experience) — what happens after the participant clicks Start Text Chat\n- [Structured Questions Guide](/docs/structured-questions-guide) — learn about interactive question widgets in text interviews","category":"Interview Experience","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Interview Landing Page — Koji Docs","metaDescription":"See what participants encounter on the Koji interview landing page, including branding, mode selection, and trust badges.","keywords":["landing page","interview page","participant experience","mode selection","branding","trust badges"],"aiSummary":"The interview landing page is the first thing participants see. It features an animated orb, headline, subheadline, description, trust badges, language picker, and two separate start buttons for voice and text mode. A well-configured landing page builds trust and maximizes participation.","aiPrerequisites":["customizing-branding"],"aiLearningOutcomes":["Understand what participants see on the landing page","Know how mode selection works","Optimize the landing page for higher conversion","Configure Open Graph previews for social sharing"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"bd7fa68e-83c5-4292-88ce-6579dd38c4ff","slug":"customizing-branding","title":"Customizing Branding","url":"https://www.koji.so/docs/customizing-branding","summary":"Customize your interview landing page with headline, subheadline, description, accent color, orb color variant (6 options), trust badges, interaction modes, language settings, and Open Graph metadata. Changes take effect immediately with a live preview editor.","content":"Every interview landing page can be tailored to match your brand and communicate the right message to participants. From the headline and description to trust badges, orb colors, and Open Graph images, the customize page gives you full control over first impressions.\n\n## Accessing the Customize Page\n\nFrom your project dashboard, click on the **Customize** tab (or navigate to your project's customize page). This opens the editor where you can configure all the branding elements that participants see on your [interview landing page](/docs/interview-landing-page).\n\nThe editor shows a live preview alongside the controls, so you can see exactly how your changes will look before saving.\n\n## What You Can Customize\n\n### Headline\n\nThe headline is the large text displayed prominently on the landing page. It is the first thing participants read, so make it count.\n\n**Default:** Your project name.\n\n**Tips for good headlines:**\n\n- Focus on what the participant will be doing: \"Share Your Onboarding Experience\" is better than \"Onboarding Research Study\"\n- Keep it under 60 characters for clean display on all screen sizes\n- Use action-oriented language that invites participation\n\n**Examples:**\n- \"Help Us Improve Your Checkout Experience\"\n- \"Share Your Thoughts on [Product Name]\"\n- \"Tell Us About Your Workflow\"\n\n### Subheadline\n\nThe subheadline appears directly below the headline and provides a secondary line of context. Use it to add a brief supporting statement that complements your headline.\n\n**Tips:**\n\n- Keep it shorter than the headline\n- Use it to answer \"Why should I participate?\" or to set expectations\n- Leave it empty if your headline and description are sufficient on their own\n\n**Example:**\n- Headline: \"Share Your Onboarding Experience\"\n- Subheadline: \"A quick conversation to help us build a better product\"\n\n### Description\n\nThe description appears below the headline and subheadline. This is where you explain the purpose of the interview and why the participant's input matters.\n\n**Tips:**\n\n- Keep it to two or three sentences\n- Answer the question \"Why should I participate?\"\n- Mention how the feedback will be used if possible\n\n**Example:**\n> We are building the next version of our product and want to hear directly from people who use it daily. This short conversation helps us understand what is working well and where we can improve. Your feedback shapes what we build next.\n\n### CTA Button Text\n\nYou can customize the call-to-action button text that appears in the editor preview. This field is available for setting up your branding identity, though the actual landing page buttons display as \"Start Voice Chat\" and \"Start Text Chat\" with their respective icons.\n\n### Accent Color\n\nChoose an accent color using the color picker to match your brand. The accent color is applied to interactive elements like buttons and highlights throughout the interview experience.\n\n### Orb Color Variant\n\nThe animated orb on the landing page comes in six color variants:\n\n| Variant | Description |\n|---|---|\n| **Aurora** | Cool blues and greens with a northern-lights feel |\n| **Sunset** | Warm oranges and reds |\n| **Ocean** | Deep blues and teals |\n| **Lavender** | Soft purples |\n| **Mint** | Fresh greens |\n| **Peach** | Warm, soft pinks and corals |\n\nChoose the variant that best complements your brand colors and the tone of your research.\n\n### Trust Badges\n\nTrust badges are small visual indicators that reassure participants. Each badge has an icon and a label.\n\nYou can enable or disable the built-in badges and add custom ones. Available badge icons include:\n\n| Icon | Common Use |\n|---|---|\n| Shield | Confidentiality / privacy |\n| Clock | Estimated time |\n| Star | Quality or special status |\n| Check | Verified or confirmed |\n| Lock | Security or anonymity |\n| Heart | Appreciation or care |\n\n**Built-in badges:**\n- **Duration badge** — shows an estimated time (e.g., \"5-10 minutes\")\n- **Anonymity badge** — reassures participants about privacy (e.g., \"Anonymous\")\n\n**Custom badges:** You can add up to four additional badges with your choice of icon and label text.\n\n**Example badge sets:**\n- \"Confidential\" (Shield) + \"5-10 minutes\" (Clock) + \"Anonymous\" (Lock)\n- \"Quick & Easy\" (Star) + \"Takes 5 min\" (Clock) + \"Your voice matters\" (Heart)\n\nBadges appear in a horizontal row below the description on the landing page.\n\n### Interaction Mode Settings\n\nControl which interview modes are available to participants:\n\n- **Both voice and text** — participants see two separate start buttons and choose their preferred mode (default)\n- **Voice only** — only the voice start button appears\n- **Text only** — only the text start button appears\n- **Default mode** — if both are enabled, which mode is presented first\n\nVoice mode requires that your project has a voice agent configured. If it does not, only text mode is available regardless of this setting.\n\n### Language Settings\n\nConfigure the default language for your interviews. When set, a language picker appears on the landing page so participants can select their preferred language. The AI interviewer will conduct the conversation in the chosen language.\n\n### Footer and Consent Text\n\nOptional text fields that appear at the bottom of the landing page:\n\n- **Footer text** — for privacy notices, contact information, or disclaimers\n- **Consent text** — for research consent language\n- **Show Powered by** — toggle visibility of the \"Powered by Koji\" badge\n\n## Open Graph Settings\n\nOpen Graph (OG) metadata controls the preview that appears when your interview link is shared on social media, Slack, email, or messaging apps.\n\n### Why OG Images Matter\n\nA link shared on LinkedIn or Slack with a custom preview image gets significantly more clicks than one with a generic or missing preview. If you are distributing your interview through social channels, configuring OG metadata is one of the highest-leverage things you can do.\n\n### Configurable Fields\n\n- **OG Title** — the title shown in link previews (falls back to your project headline)\n- **OG Description** — the description shown in link previews (falls back to your project description)\n- **OG Image URL** — a custom image for the preview\n\n### Image Specifications\n\n- **Dimensions:** 1200 x 630 pixels (standard OG image size)\n- **Format:** PNG or JPEG\n- **File size:** Under 2 MB for fast loading\n- **Content:** Include your brand logo, study title, or a visual that represents the research topic\n\nIf you do not set a custom OG image, a default preview is generated using your project name and Koji branding.\n\n### Testing Your OG Preview\n\nAfter uploading your image, you can test how it looks on different platforms:\n\n- Share the link in a private Slack channel to see the preview\n- Use a tool like the LinkedIn Post Inspector or Twitter Card Validator to preview the card\n\nOG images are cached by platforms, so changes may take some time to propagate.\n\n## Saving Your Changes\n\nThe customize editor tracks unsaved changes and shows a save button when modifications are detected. Click **Save** to apply your changes. They take effect immediately — the next participant who visits your landing page will see the updated branding.\n\nThe live preview in the editor reflects your changes in real time, so you can iterate quickly before saving.\n\n## Branding and the Embed Widget\n\nWhen your interview is loaded through the [embed widget](/docs/using-the-embed-widget), the same branding settings apply. The headline, description, and badges render inside the iframe using the theme specified in the embed code (dark or light).\n\nOG images are not relevant for embeds since the landing page is rendered inside the iframe rather than shared as a link.\n\n## Best Practices\n\n1. **Write for your audience.** If you are interviewing enterprise buyers, use professional language. If you are talking to Gen Z consumers, keep it casual and approachable.\n2. **Use the subheadline.** A strong subheadline paired with a clear headline can communicate your message without participants needing to read the full description.\n3. **Show estimated time.** A trust badge showing \"5-10 minutes\" or \"Takes ~10 min\" dramatically reduces drop-off.\n4. **Choose an orb color that fits your brand.** The orb is a prominent visual element — pick a variant that complements your accent color.\n5. **Be honest about purpose.** Participants appreciate knowing why they are being interviewed. Vague descriptions lead to lower completion rates.\n6. **Invest in the OG image.** If social sharing is a key distribution channel, a branded preview image is worth the effort.\n7. **Test on mobile.** Open your interview link on your phone to see how the landing page looks on a smaller screen.\n\n## Next Steps\n\n- [Interview Landing Page](/docs/interview-landing-page) — see how all these elements come together\n- [Intake Forms and Consent](/docs/intake-forms-and-consent) — add data collection before the interview starts\n- [Publishing Your Study](/docs/publishing-your-study) — make your customized interview live","category":"Interview Experience","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Customizing Branding — Koji Docs","metaDescription":"Personalize your Koji interview landing page with custom headlines, descriptions, trust badges, and OG images.","keywords":["branding","customize","landing page","OG image","Open Graph","trust badges","headline"],"aiSummary":"Customize your interview landing page with headline, subheadline, description, accent color, orb color variant (6 options), trust badges, interaction modes, language settings, and Open Graph metadata. Changes take effect immediately with a live preview editor.","aiPrerequisites":["interview-landing-page"],"aiLearningOutcomes":["Access and use the customize editor","Configure headlines, descriptions, and trust badges","Upload and test Open Graph images","Control which interview modes are available"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"a2b3f2c0-eb23-4d08-871a-436575489fd9","slug":"intake-forms-and-consent","title":"Intake Forms and Consent","url":"https://www.koji.so/docs/intake-forms-and-consent","summary":"Intake forms collect participant information before the interview starts. Configure text, email, phone, dropdown, textarea, and checkbox fields with labels, placeholders, required flags, and help text. Pre-fill from CSV imports and collect research consent.","content":"Intake forms let you collect information from participants before the interview starts. You can gather names, email addresses, and custom fields — or skip the form entirely for fully anonymous interviews. The form appears after the participant clicks a start button on the [landing page](/docs/interview-landing-page) and before the conversation begins.\n\n## When to Use an Intake Form\n\nIntake forms are useful when you need to:\n\n- **Identify participants** — collect name and email for follow-up or record-keeping\n- **Segment responses** — ask about role, company size, or other attributes to slice your data later\n- **Gather consent** — include a checkbox for research consent or terms acknowledgment\n- **Pre-qualify participants** — use a dropdown to confirm they meet your criteria before the interview begins\n\nIf you do not need any of this, you can disable the intake form entirely. Participants will go straight from the landing page to the interview.\n\n## Configuring Your Intake Form\n\nOpen your project and navigate to the **Customize** tab. The intake form settings are in the **Lead Form** section of the editor.\n\n### Enabling the Form\n\nToggle the lead form on or off. When disabled, participants skip directly to the interview after clicking a start button.\n\n### Form Header\n\nYou can customize the form's title and description:\n\n- **Title** — displayed at the top of the form (e.g., \"Before we begin\" or \"Tell us about yourself\")\n- **Description** — a short paragraph below the title explaining why you are collecting this information\n- **Submit button text** — the text on the form's submit button (defaults to \"Continue\")\n\n### Adding Fields\n\nClick **Add Field** to create a new form field. Each field has the following properties:\n\n| Property | Description |\n|---|---|\n| **Label** | The text shown above the input (e.g., \"Your Name\", \"Email Address\") |\n| **Type** | The input type — determines what kind of control is rendered |\n| **Placeholder** | Hint text shown inside the input before the participant types |\n| **Required** | Whether the participant must fill in this field to proceed |\n| **Help Text** | Optional explanatory text displayed below the field to provide additional guidance or context |\n| **Options** | For dropdown (select) fields only — the list of choices available in the menu |\n\n### Available Field Types\n\nKoji supports several field types to handle different kinds of information:\n\n| Type | What It Renders | Best For |\n|---|---|---|\n| **Text** | A single-line text input | Names, short answers |\n| **Email** | A text input with email validation | Email addresses |\n| **Phone** | A text input for phone numbers | Contact numbers |\n| **Long Text** (Textarea) | A multi-line text area | Comments, detailed responses |\n| **Dropdown** (Select) | A dropdown menu with predefined options | Role selection, categories, segments |\n| **Checkbox** | A single checkbox | Consent, agreements, opt-ins |\n\n### Field Order\n\nYou can reorder fields by dragging them using the grip handle on the left side of each field row. Place the most important fields first — participants are more likely to complete forms when the easy fields (like name) come before the more involved ones (like a long text comment).\n\n### Required vs. Optional Fields\n\nMark fields as required when you absolutely need the information to proceed. Keep required fields to a minimum — every required field adds friction and may reduce your completion rate.\n\nA common setup is:\n- **Name** — required (so you can address them in the conversation)\n- **Email** — optional (for follow-up, but not blocking)\n- **Consent checkbox** — required (if you need explicit research consent)\n\n## Consent Collection\n\nFor formal research, you may need to collect explicit consent before the interview. The checkbox field type is designed for this.\n\n**Example consent field:**\n- **Label:** \"I agree to participate in this research study and understand my responses will be analyzed\"\n- **Type:** Checkbox\n- **Required:** Yes\n\nParticipants must check the box before they can proceed to the interview. This creates a clear record that consent was given.\n\nFor more detailed consent requirements, you can:\n\n- Use the footer text on the landing page to display full consent language (configurable in [Customizing Branding](/docs/customizing-branding))\n- Add a link to a consent document in the field label or landing page description\n- Combine a consent checkbox with a long text field where participants can ask questions\n\n## Pre-Filling from CSV Import\n\nWhen participants arrive via a unique tracking link from a [CSV import](/docs/importing-participants-csv), their data is pre-filled into the intake form automatically. For example, if your CSV includes `name` and `email` columns and your form has matching fields, those fields will already be populated when the form loads.\n\nParticipants can still edit pre-filled values if needed, but in most cases they simply review the information and click to proceed.\n\n## How Intake Data Is Stored\n\nAll intake form responses are stored alongside the participant's interview record. You can see intake data in:\n\n- **The respondents table** — each participant's form responses appear in their row\n- **Exported results** — intake data is included when you export interviews as CSV or JSON\n- **The interview detail view** — form responses appear in the participant's profile section\n\nIntake data is never shared with other participants or made public. It is accessible only to the study owner.\n\n## Form Validation\n\nKoji validates form submissions in real time:\n\n- **Required fields** are checked before the form can be submitted. If a required field is empty, an error message appears.\n- **Email fields** are validated for correct format using a regex pattern (must contain @ and a domain).\n- **Checkbox required fields** must be checked to proceed.\n\nValidation messages appear inline next to the relevant field, so participants can quickly see what needs to be corrected.\n\n## Tips for Effective Intake Forms\n\n1. **Keep it short.** Every additional field reduces your completion rate. Only ask for what you truly need.\n2. **Put name first.** Collecting the participant's name allows the AI interviewer to address them personally during the conversation, which builds rapport.\n3. **Make email optional unless essential.** If you do not plan to follow up with participants, skip the email field entirely.\n4. **Use dropdowns for segmentation.** If you want to analyze results by segment (e.g., role, department, or company size), a dropdown is cleaner than a free-text field.\n5. **Use help text for clarity.** Add help text below fields that might be ambiguous — for example, explaining what counts as a \"primary role\" or what format you expect for phone numbers.\n6. **Lead with the easiest fields.** Name and email are quick to fill in. Place them before longer or more complex fields.\n7. **Test the form yourself.** Click your own interview link and go through the intake form to make sure everything works as expected.\n\n## Disabling the Intake Form\n\nIf you want fully anonymous interviews with zero friction, simply toggle the lead form off. Participants will go directly from the landing page to the interview without seeing any form fields.\n\nYou can enable or disable the form at any time. Responses that were already collected are not affected by toggling the form.\n\n## Next Steps\n\n- [Interview Landing Page](/docs/interview-landing-page) — see how the form fits into the overall landing experience\n- [Customizing Branding](/docs/customizing-branding) — edit the headline, description, and footer text that appear around the form\n- [Importing Participants via CSV](/docs/importing-participants-csv) — pre-fill form fields from a spreadsheet","category":"Interview Experience","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Intake Forms and Consent — Koji Docs","metaDescription":"Add intake forms to collect participant details and consent before Koji interviews. Configure fields, validation, and more.","keywords":["intake form","consent","lead form","form fields","participant data","pre-fill","research consent"],"aiSummary":"Intake forms collect participant information before the interview starts. Configure text, email, phone, dropdown, textarea, and checkbox fields with labels, placeholders, required flags, and help text. Pre-fill from CSV imports and collect research consent.","aiPrerequisites":["interview-landing-page","customizing-branding"],"aiLearningOutcomes":["Configure and enable intake forms","Choose appropriate field types","Collect research consent","Pre-fill forms from CSV data","Optimize forms for higher completion rates"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"432c7637-c1d9-4b26-81c1-1006bf52cb29","slug":"interview-completion-flow","title":"Interview Completion Flow","url":"https://www.koji.so/docs/interview-completion-flow","summary":"When an interview ends, participants see a green checkmark with \"Thanks for the chat!\" and binary thumbs-up/thumbs-down feedback (\"Loved it\" / \"Not great\"). Behind the scenes, Koji processes the transcript, assigns a quality score, and generates AI insights.","content":"When a participant finishes their interview, a completion flow kicks in that handles two things simultaneously: giving the participant a satisfying ending, and triggering Koji's analysis pipeline behind the scenes. Here is everything that happens from the moment the conversation wraps up.\n\n## How Interviews End\n\nAn interview can end in two ways:\n\n### Automatic Completion\n\nThe AI interviewer tracks which topics from your research brief have been covered and how thoroughly. When it determines that sufficient ground has been covered, it naturally wraps up the conversation — thanking the participant, summarizing the key themes discussed, and saying goodbye.\n\nThis is the most common path. The participant does not need to do anything; the conversation flows to a natural conclusion just like a real interview.\n\n### Manual Completion (the \"Done\" Button)\n\nAt any point during the interview, participants can click the **Done** button to end the conversation on their own terms. This is useful when:\n\n- The participant feels they have said everything they want to say\n- They need to leave unexpectedly\n- They have reached the end of the topics they care about\n\nWhichever method triggers the end, the next step is the same: the participant sees the thank-you screen.\n\n## The Thank-You Screen\n\nOnce the interview ends, participants see a clean, simple completion screen.\n\n### Visual Indicator\n\nA green checkmark icon is displayed inside a green circle at the center of the screen, providing a clear visual signal that the interview is complete.\n\n### Thank-You Message\n\nBelow the checkmark, the heading reads **\"Thanks for the chat!\"** — a brief, warm message that acknowledges the participant's time without being overly formal.\n\nThe completion screen is intentionally minimal. There is no avatar, no personalization with the participant's name, and no display of interview duration. The focus is on a clean ending and a quick feedback step.\n\n## Participant Feedback\n\nAfter the thank-you message, participants are invited to rate their experience with a simple binary choice:\n\n### Thumbs Up / Thumbs Down\n\nTwo buttons appear below the thank-you message:\n\n- **Thumbs up** — labeled \"Loved it\"\n- **Thumbs down** — labeled \"Not great\"\n\nParticipants tap one of the two options to share their sentiment. There is no numeric scale, no emoji range, and no written feedback text area — just a quick, low-friction binary choice.\n\n### Immediate Submission\n\nFeedback is submitted the moment the participant taps their choice. There is no separate submit button or confirmation step. The participant taps \"Loved it\" or \"Not great,\" the selection is recorded immediately, and they are done.\n\nFeedback is not mandatory. If a participant closes the tab without tapping either button, the interview is still recorded as completed and the analysis still runs.\n\n## What Happens Behind the Scenes\n\nWhile the participant is looking at the thank-you screen, Koji's analysis pipeline is already running in the background. Here is what happens automatically:\n\n### Transcript Processing\n\nThe full conversation — whether it was conducted via voice or text — is processed into a clean text transcript. For voice interviews, the audio is transcribed. For text interviews, the messages are already in text form.\n\n### Quality Scoring\n\nKoji's quality evaluation assigns a score from 0 to 5 to the interview. This score reflects the depth, relevance, and informativeness of the participant's responses relative to your research brief.\n\n- **Score 3 and above:** The interview counts toward your study and contributes meaningful data.\n- **Score below 3:** The interview is flagged as low quality. It does not count toward your plan's interview limits, and you are not billed for it.\n\nThis quality gate ensures you only pay for interviews that actually deliver value. Learn more in [How the Quality Gate Works](/docs/how-the-quality-gate-works).\n\n### AI-Generated Insights\n\nAfter scoring, Koji analyzes the transcript to extract:\n\n- **Key themes** discussed during the interview\n- **Notable quotes** that capture important sentiments\n- **A summary** of the participant's main points\n- **Sentiment indicators** for different topics\n- **Structured question responses** with their quantitative values (for text interviews that included [structured question widgets](/docs/text-interview-experience#structured-question-widgets))\n\nThese insights appear on the interview detail page in your dashboard and contribute to the project-level analysis.\n\n### Analysis Timing\n\nThe analysis typically completes within a few minutes of the interview ending. You will see the results appear in your project dashboard as they become available. Voice interviews may take slightly longer due to the additional transcription step.\n\n## What the Researcher Sees\n\nBack in your project dashboard, completed interviews show up with:\n\n- A **status badge** indicating the interview is complete\n- The **quality score** (0-5)\n- The **participant's name** (if collected)\n- **Message count** and **duration**\n- A link to the **full transcript** and **analysis**\n\nYou can click into any completed interview to read the transcript, review the quality score breakdown, and see the AI-generated insights.\n\n## Handling Abandoned Interviews\n\nSometimes participants start an interview but close the browser before finishing. Koji handles this gracefully:\n\n- The conversation is saved up to the point where the participant left\n- After a period of inactivity, the interview is marked as abandoned\n- Abandoned interviews are still analyzed if they contain enough content to be meaningful\n- They appear in your dashboard with an appropriate status indicator\n\n## Feedback Data for Researchers\n\nThe thumbs-up and thumbs-down feedback that participants submit on the completion screen is stored alongside the interview record. You can use this data to:\n\n- **Monitor participant satisfaction** across your study\n- **Identify issues** with specific interview topics or questions\n- **Improve future studies** based on whether participants found the experience positive or negative\n\nFeedback ratings are visible in the interview detail view and can be included in exports.\n\n## Customizing the Completion Experience\n\nCurrently, the thank-you screen uses a standard layout and messaging. The green checkmark, \"Thanks for the chat!\" heading, and binary feedback buttons are consistent across all studies.\n\nIf your interview is embedded via the [embed widget](/docs/using-the-embed-widget), the completion screen renders inside the iframe. You can listen for the `koji:interview_completed` event to trigger your own follow-up actions on the parent page — such as showing a discount code, redirecting to another URL, or logging the completion in your analytics.\n\n## Next Steps\n\n- [How the Quality Gate Works](/docs/how-the-quality-gate-works) — learn how interviews are scored and filtered\n- [Voice Interview Experience](/docs/voice-interview-experience) — understand the full voice interview flow\n- [Text Interview Experience](/docs/text-interview-experience) — understand the full text interview flow with structured widgets","category":"Interview Experience","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Interview Completion Flow — Koji Docs","metaDescription":"Learn what happens when a Koji interview ends, including the thank-you screen, feedback, and AI analysis pipeline.","keywords":["interview completion","thank you screen","feedback","quality score","AI analysis","transcript","completion flow"],"aiSummary":"When an interview ends, participants see a green checkmark with \"Thanks for the chat!\" and binary thumbs-up/thumbs-down feedback (\"Loved it\" / \"Not great\"). Behind the scenes, Koji processes the transcript, assigns a quality score, and generates AI insights.","aiPrerequisites":["voice-interview-experience","text-interview-experience"],"aiLearningOutcomes":["Understand how interviews end","Know what participants see on the completion screen","Explain the quality scoring and analysis pipeline","Handle abandoned interviews"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"f6cfec98-5d03-4ca9-b5a1-b32ab7aef574","slug":"ai-probing-guide","title":"How Koji's AI Follow-Up Probing Works: Going Deeper Than Any Survey","url":"https://www.koji.so/docs/ai-probing-guide","summary":"Koji AI probing automatically asks follow-up questions after every participant answer, up to a configurable maxFollowUps depth. Probing behavior is controlled per question via custom instructions and anchor settings for scale questions. This produces research-grade qualitative context alongside every structured data point.","content":"The defining difference between an AI interview and a survey isn''t voice support or a conversational UI. It''s **probing** — the ability to follow up on what a participant says, ask the natural next question, and dig into the reasoning behind an answer.\n\nEvery Koji interview includes built-in AI probing. Understanding how to configure and optimize it is the key to unlocking research-grade insights at survey-level scale.\n\n## What Is Probing?\n\nIn traditional qualitative research, a skilled moderator listens carefully to each answer and follows up with questions like:\n\n- \"Can you tell me more about that?\"\n- \"What did you mean when you said [X]?\"\n- \"What was happening right before that moment?\"\n- \"Why is that important to you?\"\n\nThis is probing. It''s what separates a surface-level answer from a genuine insight.\n\nKoji''s AI interviewer does this automatically — for every question, across every participant, at any scale. Whether you''re running 5 interviews or 500, every participant gets the same quality of probing follow-up that a skilled human moderator would provide.\n\n## How Probing Is Configured\n\nProbing behavior is set per question in your interview plan. Each question has a `probing` configuration with three settings:\n\n### maxFollowUps (0–3)\n\nControls how many follow-up questions the AI may ask after the participant answers.\n\n| Value | Behavior |\n|---|---|\n| 0 | Ask the question, record the answer, move on — no probing |\n| 1 | One follow-up (the default) — good for most questions |\n| 2 | Two follow-ups — for questions where the \"why\" is the core insight |\n| 3 | Deep probing — reserved for your most critical research questions |\n\nSetting `maxFollowUps` to 0 is useful for purely quantitative questions (scale, yes/no) where you want the rating without a probing conversation, or for screening questions where you only need the factual answer.\n\n### instructions (Optional Text)\n\nCustom guidance for how the AI should probe this specific question. Without instructions, the AI uses general best-practice probing techniques drawn from qualitative research methodology. With instructions, you can direct it precisely.\n\nExamples of effective probing instructions:\n\n- \"Ask what specifically triggered that decision.\"\n- \"If they mention a competitor, ask which one and what they liked about it.\"\n- \"Focus on the emotional experience, not just the functional outcome.\"\n- \"If they describe a negative experience, ask what would have made it better.\"\n- \"Don''t move on until you''ve understood the specific moment in their workflow where this problem occurs.\"\n\nGood probing instructions are the difference between a study that surfaces generic themes and one that produces specific, actionable insights.\n\n### anchor (Scale Questions Only)\n\nFor scale questions, the `anchor` option tells the AI to use the participant''s specific rating as the basis for the follow-up. Instead of a generic \"tell me more,\" the AI asks a rating-aware question:\n\n- **Score 9–10:** \"You gave us a [X] — what specifically makes your experience so positive?\"\n- **Score 6–8:** \"You gave us a [X] — what would change that to a 9 or 10?\"\n- **Score 1–5:** \"You gave us a [X] — what''s been the biggest source of frustration?\"\n\nAnchoring produces dramatically more useful data than generic follow-ups, because participants are responding to their own stated position — not a formulaic probe.\n\n## The Probing Flow in Practice\n\nHere''s how probing plays out in a real Koji interview:\n\n**Question:** \"What was the biggest barrier to getting started with us?\"\n\n**Participant:** \"Honestly, the setup was kind of confusing.\"\n\n**AI follow-up** (with probing instructions: \"Ask about the specific moment and what would have helped\"):\n> \"What part of the setup was most confusing for you — and what would have made it easier?\"\n\n**Participant:** \"The API documentation assumed I already knew how webhooks work. I''m a product manager, not an engineer. A step-by-step guide would have helped.\"\n\n**Second follow-up** (if maxFollowUps is 2):\n> \"Did you eventually figure it out on your own, or did you get help?\"\n\n**Participant:** \"I ended up asking our engineering team. But I almost gave up before that.\"\n\nWithout probing, you''d have: \"Setup was confusing.\" With probing, you have: \"Product managers can''t follow webhook documentation — nearly churned — needed engineering support to proceed.\" These are very different research findings.\n\n## Probing in Text vs. Voice Mode\n\n### Text Mode\n\nIn text mode, probing questions appear as new chat messages from the AI, continuing the natural conversation thread. The participant types their response. This creates a written record that''s easy to search and reference later in your transcript.\n\n### Voice Mode\n\nIn voice mode, probing questions are spoken aloud by the AI in a natural conversational tone. There''s no awkward pause or shift — the probing flows seamlessly from the initial question. Participants often don''t realize they''re being probed at all; it feels like a normal conversation.\n\nVoice mode probing tends to produce more emotional and spontaneous responses. Text mode probing produces more considered and detailed responses. Choose based on your research goals and participant audience.\n\n## What the AI Does When Probing\n\nKoji''s AI interviewer is built to follow qualitative research best practices:\n\n**Follows the thread.** If a participant mentions something unexpected but significant, the AI can follow that thread — especially in exploratory or hybrid mode. You don''t lose interesting detours by being locked to a rigid script.\n\n**Stays neutral.** The AI doesn''t lead or suggest answers. It probes with open-ended questions that invite elaboration without steering the participant toward a particular conclusion.\n\n**Knows when to move on.** When a participant has given a thorough answer and follow-ups aren''t producing new information, the AI moves to the next question rather than repeating the same probe.\n\n**Respects boundaries.** If a participant declines to elaborate (\"I''d rather not get into that\"), the AI acknowledges this and moves on without pressure.\n\n**Maintains context.** The AI remembers what was said earlier in the interview. A follow-up question can naturally reference something mentioned 10 minutes ago.\n\n## Probing and Interview Mode\n\nHow aggressively the AI probes also depends on the interview mode you select:\n\n| Mode | Probing Behavior |\n|---|---|\n| **Structured** | Stays close to your interview questions; probes within each question but returns to the guide afterward |\n| **Exploratory** | Follows interesting threads more freely; may pursue an unexpected topic if the participant reveals something significant |\n| **Hybrid** | Starts structured, goes exploratory when something particularly interesting surfaces |\n\nSee the [Interview Mode Guide](/docs/interview-mode-guide) for more detail on choosing the right mode for your research goals.\n\n## How Probing Results Appear in Reports and Transcripts\n\n### In Transcripts\n\nEvery probing exchange is captured in the full interview transcript — the initial question, the participant''s answer, each follow-up question, and each follow-up response. You can read the complete conversational arc for any question in the Recruit tab.\n\n### In Structured Answers\n\nFor structured questions (scale, choice, ranking, yes/no), Koji extracts follow-up insights — a list of question-and-answer pairs from the probing exchange, with an optional distilled insight for each. This makes it easy to see both the structured value (the rating or selection) and the qualitative context (what probing revealed) in one place.\n\n### In the Research Report\n\nThe research report surfaces the most important probing insights in two ways:\n\n1. **Representative quotes** — Memorable statements from probing follow-ups, shown under each question''s analysis section\n2. **Themes** — If multiple participants expressed similar things during probing follow-ups, the AI surfaces this as a pattern\n\nThe [Generating Research Reports](/docs/generating-research-reports) guide explains the full report structure.\n\n## Configuring Probing for Common Research Goals\n\n### Validation Research\n\nWhen you have a hypothesis and want to validate it:\n- `maxFollowUps`: 1\n- Instructions: \"Focus on whether they confirm or challenge [hypothesis]. Ask for their direct experience rather than their opinion.\"\n\n### Discovery Research\n\nWhen you''re exploring unknown territory:\n- `maxFollowUps`: 2–3\n- Instructions: \"Follow threads that seem emotionally significant or that the participant seems eager to discuss.\"\n- Use exploratory or hybrid interview mode\n\n### Exit and Churn Research\n\nWhen you need to understand why someone left:\n- `maxFollowUps`: 2\n- Instructions: \"Ask what moment triggered the decision to leave. Ask what would have kept them.\"\n- Enable anchor probing for any scale questions: \"You gave us a [X] — what would have changed that?\"\n\n### Feature Usage Research\n\nWhen you want to understand how a specific feature is being used:\n- `maxFollowUps`: 1–2\n- Instructions: \"Ask about their specific workflow — what are they doing before and after using this feature, and what is the trigger that brings them to it?\"\n\n## Best Practices\n\n**Write specific probing instructions.** \"Probe deeper\" is not useful. \"Ask about the specific moment when they decided to abandon the checkout, and what they did instead\" is useful. Concrete instructions produce concrete insights.\n\n**Match probing depth to question importance.** Not every question deserves three levels of follow-up. Reserve `maxFollowUps` of 2–3 for your most critical research questions.\n\n**Use anchor probing for all scale questions.** A generic \"tell me more about that rating\" is weaker than \"You gave us a 4 — what would it take to make that a 7?\" Anchoring converts ratings into action items.\n\n**Test your probing before deploying.** Run a pilot interview to see how the AI probes your questions. You may find your instructions need adjustment to produce the insights you''re after before you send it to all participants.\n\n**Don''t over-probe quantitative questions.** For yes/no or single-choice questions where you just need the answer, set `maxFollowUps` to 0 or write minimal instructions.\n\n## Frequently Asked Questions\n\n**Does the AI always probe, or only sometimes?**\nThe AI probes whenever a participant gives an answer with room for deeper exploration, up to the `maxFollowUps` limit. If a participant gives an extremely thorough answer that already covers what the probing questions would ask, the AI recognizes this and moves on rather than asking redundant follow-ups.\n\n**Can I disable probing for specific questions?**\nYes. Set `maxFollowUps` to 0 for any question where you only need the surface answer — for example, a screening question, a yes/no qualifier, or a quantitative rating where the number is the only data point you need.\n\n**Will participants know the AI is following a probing strategy?**\nGenerally, no. Koji''s probing is designed to feel like natural conversation. The AI responds to what the participant actually says rather than following a rigid script. Most participants experience Koji interviews as conversations with an unusually attentive and curious interviewer.\n\n**Can the AI probe based on something said earlier in the interview?**\nYes, in exploratory and hybrid modes. The AI maintains full conversation context. If a participant mentioned switching from a competitor 15 minutes earlier, the AI can reference that later: \"Earlier you mentioned switching from [X] — can you tell me more about what drove that decision?\"\n\n**How does probing work for multiple-choice questions?**\nAfter a participant selects their choices, the AI probes into the selections. For example: \"You mentioned [option A] and [option B] — can you tell me more about why those two stand out for you?\" The `instructions` field lets you direct probing toward specific selections.\n\n**What if a participant refuses to elaborate?**\nThe AI gracefully accepts this. A response like \"I''d rather not go into that\" is met with a natural acknowledgment and a move to the next question. The AI never pressures participants.\n\n## Related Resources\n\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — The six question types and when to use each\n- [Scale Questions Guide](/docs/scale-questions-guide) — Scale question configuration including anchor probing\n- [Choice and Ranking Questions Guide](/docs/choice-ranking-questions-guide) — Single choice, multiple choice, and ranking\n- [Interview Mode Guide](/docs/interview-mode-guide) — Structured, exploratory, and hybrid modes and how they affect probing behavior\n- [Open-Ended Interview Questions](/docs/open-ended-interview-questions) — How open-ended questions work alongside structured types\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — How Koji scores interview quality based on depth and coverage","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"How Koji AI Follow-Up Probing Works: Automated Qualitative Depth | Koji","metaDescription":"Learn how Koji AI interviewer automatically asks follow-up questions to probe deeper on every answer. Configure probing depth, custom instructions, and anchor behavior for scale questions.","keywords":["AI interview follow-up questions","automated probing qualitative research","AI moderator follow-up","probing interview technique AI","AI interviewer depth questions","interview probing configuration"],"aiSummary":"Koji AI probing automatically asks follow-up questions after every participant answer, up to a configurable maxFollowUps depth. Probing behavior is controlled per question via custom instructions and anchor settings for scale questions. This produces research-grade qualitative context alongside every structured data point.","aiPrerequisites":["Familiarity with creating Koji studies","Understanding of structured question types"],"aiLearningOutcomes":["Understand how maxFollowUps controls probing depth per question","Write effective custom probing instructions for specific research goals","Configure anchor probing for scale questions","Choose the right interview mode to match probing intensity","Interpret probing results in transcripts and research reports"],"aiDifficulty":"intermediate","aiEstimatedTime":"10 minutes"},{"type":"documentation","id":"4aacfcb4-a7a0-4eb9-b088-d57da5543df5","slug":"research-interview-templates","title":"Research Interview Template Library: 10 Proven Question Sets for Common Research Jobs","url":"https://www.koji.so/docs/research-interview-templates","summary":"A library of 10 proven interview question sets for the most common research jobs — product discovery, usability feedback, churn research, NPS follow-up, onboarding experience, competitive intelligence, pricing sensitivity, employee stay interviews, market validation, and customer journey mapping. Each template uses Koji structured question types (open_ended, scale, single_choice, multiple_choice, ranking, yes_no) for automatic report generation.","content":"# Research Interview Template Library: 10 Proven Question Sets for Common Research Jobs\n\nGood interview questions do not come from a blank page — they are adapted from frameworks validated across hundreds of research sessions. This library gives you 10 ready-to-use question sets for the most common research jobs, each designed for AI-moderated interviews using Koji's structured question system.\n\n## How to Use These Templates\n\nEach template below contains 6–8 questions, mixing quantitative and qualitative question types. In Koji, add these directly to your study's interview brief as structured questions.\n\nFor each question, the type is noted in brackets: `[open_ended]`, `[scale]`, `[single_choice]`, `[multiple_choice]`, `[ranking]`, or `[yes_no]`. These are Koji's six structured question types — the same types that determine how each answer is visualized in your auto-generated research report.\n\nCustomize the options and scale labels for your specific product and context. The AI interviewer will adapt the conversational framing automatically — you are defining the structure, not scripting the exact words. For most templates, set open-ended questions to 2 AI follow-ups. For scale and choice questions, set 1 follow-up to capture the \"why\" behind the number.\n\n---\n\n## Template 1: Product Discovery (Jobs-to-Be-Done)\n\n**Research goal:** Understand the fundamental job participants hire your product to do.\n\n**Best for:** Early-stage product development, major feature pivots, understanding latent needs before building.\n\n1. `[open_ended]` \"Take me back to the moment you first started looking for a solution like ours. What was going on in your work at that time?\"\n\n2. `[single_choice]` \"Which best describes the primary outcome you are trying to achieve?\" Options: Save time on recurring tasks / Make better decisions faster / Improve team collaboration / Reduce costs / Other\n\n3. `[open_ended]` \"Describe the last time you felt like the problem you are trying to solve was really costing you. What happened?\"\n\n4. `[scale 1–10]` \"Before using our product, how well were you able to solve this problem with other tools?\"\n\n5. `[yes_no]` \"Have you tried other solutions for this problem before?\"\n\n6. `[open_ended]` \"If our product did not exist, what would you do instead?\"\n\n7. `[single_choice]` \"How did you first learn about us?\" Options: Word of mouth / Search / Social media / Direct outreach / Other\n\n---\n\n## Template 2: Usability and Feature Feedback\n\n**Research goal:** Understand friction points in a specific feature or workflow.\n\n**Best for:** Pre-launch feature validation, post-launch usability issues, redesigns.\n\n1. `[open_ended]` \"Walk me through how you typically use [feature name] in a normal week.\"\n\n2. `[scale 1–10]` \"How easy was it to accomplish what you needed with [feature name]?\"\n\n3. `[open_ended]` \"Tell me about a moment when [feature name] did not behave the way you expected. What happened?\"\n\n4. `[single_choice]` \"Compared to similar tools you have used, how does [feature name] compare?\" Options: Much better / Somewhat better / About the same / Somewhat worse / Much worse\n\n5. `[multiple_choice]` \"Which aspects of [feature name] do you find most valuable?\" Options: Speed / Accuracy / Flexibility / Integration with other tools / Visual output / Other\n\n6. `[yes_no]` \"Have you ever found a workaround because [feature name] could not do what you needed?\"\n\n7. `[open_ended]` \"If you could change one thing about [feature name], what would it be and why?\"\n\n---\n\n## Template 3: Churn and Cancellation Research\n\n**Research goal:** Understand why customers leave and what would have retained them.\n\n**Best for:** Win-back campaigns, product gap analysis, retention strategy, pricing reviews.\n\n1. `[open_ended]` \"What was the main thing that led you to stop using [product]?\"\n\n2. `[single_choice]` \"Which best describes your primary reason for cancelling?\" Options: Too expensive for the value / Missing features I needed / Switched to a competitor / My needs changed / Technical issues / Other\n\n3. `[open_ended]` \"Tell me about the last time you used [product] before you decided to cancel. What were you trying to do?\"\n\n4. `[yes_no]` \"Did you try to get help or find a solution before deciding to cancel?\"\n\n5. `[open_ended]` \"What would [product] have needed to do differently to keep you as a customer?\"\n\n6. `[scale 1–10]` \"How likely would you be to consider [product] again if the main issues were resolved?\"\n\n7. `[single_choice]` \"What are you using now to solve the same problem?\" Options: A competitor product / Internal tools / Manual processes / Nothing — the problem is unsolved / Other\n\n---\n\n## Template 4: NPS Follow-Up (Qualitative Depth)\n\n**Research goal:** Understand the \"why\" behind NPS scores and identify specific improvement areas.\n\n**Best for:** Post-NPS survey follow-up, quarterly voice-of-customer programs, relationship research.\n\n1. `[open_ended]` \"You gave us a [NPS score] — walk me through what has been top of mind when you think about your experience with us.\"\n\n2. `[single_choice]` \"Which part of your experience had the biggest influence on your score?\" Options: Product capabilities / Ease of use / Customer support / Pricing value / Onboarding experience / Other\n\n3. `[open_ended]` \"Tell me about a specific moment in the last three months that shaped how you feel about us.\"\n\n4. `[scale 1–10]` \"How well does our product fit into your existing workflow?\"\n\n5. `[yes_no]` \"Have you recommended [product] to a colleague in the last six months?\"\n\n6. `[open_ended]` \"What is the one thing that would make you a stronger advocate for us?\"\n\n---\n\n## Template 5: Onboarding Experience Research\n\n**Research goal:** Identify friction in the onboarding journey and understand time-to-value.\n\n**Best for:** Activation improvement, onboarding redesign, research with users in their first 30 days.\n\n1. `[open_ended]` \"Take me back to your first week using [product]. What was going through your mind?\"\n\n2. `[scale 1–10]` \"How easy was it to understand what [product] was going to do for you in the first few hours?\"\n\n3. `[single_choice]` \"What did you do first after signing up?\" Options: Followed the getting-started guide / Explored on my own / Watched a tutorial / Asked a colleague / Contacted support / Other\n\n4. `[open_ended]` \"Was there a moment when things clicked and you understood the value? Describe it.\"\n\n5. `[yes_no]` \"Did you ever consider stopping before you felt comfortable with the product?\"\n\n6. `[ranking]` \"Rank these by how quickly they delivered value for you:\" Items: Core feature A / Core feature B / Core feature C / Reporting / Integrations\n\n7. `[open_ended]` \"What is one thing we could do to help new users get to value faster?\"\n\n---\n\n## Template 6: Competitive Intelligence\n\n**Research goal:** Understand how participants evaluate and compare competing solutions.\n\n**Best for:** Sales enablement, positioning research, competitive differentiation, analyst prep.\n\n1. `[open_ended]` \"When you were evaluating tools in this category, what were the most important criteria for your decision?\"\n\n2. `[multiple_choice]` \"Which of these did you seriously evaluate before choosing your current solution?\" Options: [Competitor A] / [Competitor B] / [Competitor C] / [Your product] / Other\n\n3. `[open_ended]` \"Tell me about the deciding factor that made you choose your current solution over the alternatives.\"\n\n4. `[single_choice]` \"How did you first learn about the alternatives you evaluated?\" Options: Industry analyst reports / Peer recommendations / G2 or review sites / Vendor outreach / Search / Other\n\n5. `[scale 1–10]` \"How satisfied are you that you made the right choice?\"\n\n6. `[yes_no]` \"Are you actively monitoring any alternative solutions right now?\"\n\n7. `[open_ended]` \"What would have to change in the market for you to reconsider your current tool?\"\n\n---\n\n## Template 7: Pricing Sensitivity Research\n\n**Research goal:** Understand how participants think about pricing value and willingness to pay.\n\n**Best for:** Pricing model changes, new tier introduction, expansion pricing, packaging decisions.\n\n1. `[open_ended]` \"When you think about what you pay for [product], what do you compare it to?\"\n\n2. `[single_choice]` \"How do you typically evaluate the ROI of tools like [product]?\" Options: Time saved / Revenue impact / Team cost reduction / Risk reduction / Hard to quantify / Other\n\n3. `[scale 1–10]` \"How confident are you that you are getting strong value for the price you are currently paying?\"\n\n4. `[open_ended]` \"Tell me about a time when pricing came up in a decision about your subscription.\"\n\n5. `[yes_no]` \"Has pricing ever been a factor in considering switching to a competitor?\"\n\n6. `[open_ended]` \"What would need to be true about a higher tier for it to be an easy yes for your team?\"\n\n---\n\n## Template 8: Employee Stay Interview\n\n**Research goal:** Understand what keeps employees engaged and where retention risks exist.\n\n**Best for:** HR and people teams, quarterly retention programs, manager effectiveness research.\n\n1. `[open_ended]` \"What gets you most excited about coming to work these days?\"\n\n2. `[scale 1–10]` \"How strongly do you feel your work connects to the company's overall mission?\"\n\n3. `[single_choice]` \"Which of these feels most important to your decision to stay here?\" Options: Growth opportunities / Team culture / Compensation / Flexibility / Meaningful work / Leadership quality / Other\n\n4. `[open_ended]` \"Tell me about a moment in the last few months when you felt genuinely valued.\"\n\n5. `[yes_no]` \"Have you been approached by another employer in the last six months?\"\n\n6. `[open_ended]` \"What is one thing we could change that would make the biggest difference to how you feel about working here?\"\n\n---\n\n## Template 9: Market Validation (New Product or Feature)\n\n**Research goal:** Validate demand for an unbuilt product or feature before committing to development.\n\n**Best for:** Pre-build validation, startup idea testing, feature bet validation, investor due diligence prep.\n\n1. `[open_ended]` \"Tell me about how you currently handle [problem area]. Walk me through what that actually looks like day to day.\"\n\n2. `[scale 1–10]` \"How significant a pain point is [problem area] for you right now?\"\n\n3. `[yes_no]` \"Have you tried to solve this problem with a tool or process that did not fully work?\"\n\n4. `[open_ended]` \"If I told you there was a solution that could [describe proposed solution in one sentence], what would your first reaction be?\"\n\n5. `[single_choice]` \"How likely would you be to try a solution like this?\" Options: Would try immediately / Would try after seeing it work for others / Would need strong proof first / Probably would not try it / Other\n\n6. `[scale 1–10]` \"If this solution cost [€X] per month, how would you rate its value at that price?\"\n\n7. `[open_ended]` \"What would you want to see in a first version for it to be worth your time to try?\"\n\n---\n\n## Template 10: Customer Journey Mapping\n\n**Research goal:** Map the end-to-end customer experience across all touchpoints from first awareness to ongoing use.\n\n**Best for:** CX research, journey redesign, identifying dropped moments, pre- and post-purchase experience gaps.\n\n1. `[open_ended]` \"Take me all the way back to when you first realized you had the problem that led you to us. How long ago was that?\"\n\n2. `[single_choice]` \"Where in your journey did you experience the most friction?\" Options: Finding a solution / Evaluating options / Getting started / Day-to-day use / Getting support / Understanding results / Other\n\n3. `[open_ended]` \"Tell me about the most memorable moment in your experience with us — positive or negative.\"\n\n4. `[scale 1–10]` \"Looking at your full journey, how well has the experience matched what you expected when you started?\"\n\n5. `[multiple_choice]` \"Which touchpoints have you used when you needed help?\" Options: In-product guidance / Help center / Email support / Live chat / Community forums / A customer success person / Other\n\n6. `[open_ended]` \"What is the one moment in your experience you would most want us to improve?\"\n\n---\n\n## Customizing These Templates for Your Context\n\nThese templates are starting points. In Koji, you can:\n\n- Add your product name, feature names, and competitor names throughout\n- Adjust choice options to reflect your actual product capabilities and market context\n- Set scale labels (e.g., \"1 = Not at all satisfied\" to \"10 = Extremely satisfied\") to make numeric responses more meaningful\n- Reorder questions based on your research priority — the AI interviewer works through them in order\n- Add a custom intake form to qualify and segment participants before the interview begins\n- Combine elements from multiple templates for mixed-method studies (for example, NPS follow-up questions combined with competitive intelligence questions)\n\nThe six structured question types in Koji — open_ended, scale, single_choice, multiple_choice, ranking, and yes_no — ensure that quantitative responses are automatically extracted and visualized in your auto-generated report. You get distribution charts, ranked lists, and frequency breakdowns without any manual analysis, alongside the qualitative themes and quotes the AI surfaces from the open-ended responses.\n\n## Building Your Own Template Library\n\nOnce you have run a few studies, you will have question sets that work particularly well for your product, your customers, and your research objectives. Save these as named templates in your team's documentation system (Notion, Confluence, Google Docs) so other researchers and non-researchers can use them as approved starting points.\n\nA template library combined with a lightweight review process is the foundation of a scalable self-service research program — enabling your whole organization to generate consistent, reliable insights without every study starting from zero.\n\n## Related Resources\n\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide)\n- [How to Write Great Interview Questions](/docs/writing-interview-questions)\n- [Discussion Guide Template: How to Structure Your Research Sessions](/docs/discussion-guide-template)\n- [Understanding the Research Brief](/docs/understanding-the-research-brief)\n- [Choosing a Methodology](/docs/choosing-a-methodology)\n- [User Interview Guide Template](/docs/user-interview-guide-template)","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Research Interview Template Library: 10 Proven Question Sets | Koji","metaDescription":"10 ready-to-use AI interview question sets for product discovery, churn, NPS follow-up, onboarding, competitive intelligence, pricing, and more. Each template uses Koji's structured question types.","keywords":["user research interview templates","interview guide templates","research question templates","research study templates","interview question library","UX research templates"],"aiSummary":"A library of 10 proven interview question sets for the most common research jobs — product discovery, usability feedback, churn research, NPS follow-up, onboarding experience, competitive intelligence, pricing sensitivity, employee stay interviews, market validation, and customer journey mapping. Each template uses Koji structured question types (open_ended, scale, single_choice, multiple_choice, ranking, yes_no) for automatic report generation.","aiPrerequisites":["Basic familiarity with creating a study in Koji","A defined research objective before selecting a template"],"aiLearningOutcomes":["Select the right template for your specific research job","Adapt structured question types to your product context","Configure AI probing depth for maximum interview quality","Combine templates for mixed-method research studies"],"aiDifficulty":"beginner","aiEstimatedTime":"10 minutes"},{"type":"documentation","id":"da06d161-dbb6-4644-9dfd-59cce99dc6ae","slug":"company-context-guide","title":"Company Context: How to Make Your AI Interviewer a Domain Expert","url":"https://www.koji.so/docs/company-context-guide","summary":"Company context is an account-level setting in Koji that provides the AI interviewer with background knowledge about your company, product, customers, and industry terminology. Unlike the per-study research brief, company context applies globally to all interviews. Setting 150–400 words of clear, factual context improves follow-up question relevance, terminology accuracy, and overall quality scores. It works alongside per-study context documents for maximum interview depth.","content":"## Company Context: How to Make Your AI Interviewer a Domain Expert\n\n**The short answer:** Company context is how you teach Koji about your specific business, product, industry, and customers. When configured well, Koji's AI interviewer asks sharper, more relevant follow-up questions — turning a generic AI interview into one that feels specifically designed for your research.\n\nThe difference between a good AI interview and a great one often comes down to context. An AI that understands your product category, your customers' typical workflows, and the language your industry uses will naturally ask better follow-up questions than one operating with no background knowledge.\n\nThis guide explains what company context is, what to include, how to add it, and how it changes the quality of your interviews.\n\n---\n\n## What Is Company Context?\n\nCompany context is a freeform field in your Koji account settings that lets you provide background information about your organization. Think of it as a briefing document for the AI interviewer — the kind of context you would give a human researcher before their first day on the job.\n\nUnlike the research brief, which is study-specific, company context applies across all your studies. It is set once and automatically used in every interview you run.\n\n### What company context includes\n\nGood company context typically covers:\n\n- **What your company does**: a clear description of your product or service\n- **Who your customers are**: the primary personas, industries, or roles you serve\n- **Key terminology**: product names, internal jargon, industry-specific vocabulary\n- **Business model**: how you sell, what problems you solve, what market you are in\n- **Current focus areas**: what the company is actively working on or trying to understand\n- **Competitors and alternatives**: what customers compare you to\n\n---\n\n## Why Company Context Improves Interview Quality\n\nWithout context, Koji's AI makes interview decisions with only general knowledge of your topic area. With context, it has a specific mental model of your world.\n\nHere is a concrete example. Suppose your company makes B2B expense management software. Without company context, the AI might ask a generic follow-up like \"what tools do you use for that?\" With company context, it can ask more targeted questions like \"you mentioned approval workflows — does your team use automated routing or manual delegation?\" because it knows what features your product has and what problems your customers typically face.\n\nCompany context enables:\n\n- **Sharper follow-up questions** — the AI probes on things that are actually relevant to your business\n- **Correct terminology** — participants are not confused when your industry uses specific vocabulary\n- **Better topic coverage** — the AI recognizes when participants mention your product category and knows how to explore deeper\n- **More natural conversations** — the interview feels less robotic and more like talking to someone who understands the space\n\nResearch quality scores — measured by relevance, depth, and coverage — consistently improve when company context is configured well.\n\n---\n\n## How to Add Company Context\n\nCompany context is configured at the account level:\n\n1. Go to **Settings** in your Koji dashboard\n2. Select **Company Context** from the settings menu\n3. Write your context in the text field provided\n4. Save your changes\n\nChanges take effect immediately and apply to all new interviews. Existing conversations are not retroactively updated, but you can update your context at any time as your business or research focus evolves.\n\n### Writing effective company context: a template\n\nHere is a template for crafting useful company context:\n\n```\n[Company name] is a [brief product description]. We help [target customer] [achieve what outcome] by [how the product works].\n\nOur main customer segments are: [segment 1], [segment 2], [segment 3].\n\nKey terminology our customers use: [term 1], [term 2], [term 3].\n\nOur main competitors and alternatives include: [competitor 1], [competitor 2].\n\nCurrent research focus: [what you are trying to learn right now].\n\nWhat makes us different from alternatives: [key differentiators].\n```\n\nKeep context concise and factual. 150–400 words is typically the right length. Longer context is not always better — focus on what is most likely to make follow-up questions sharper.\n\n---\n\n## Examples by Industry\n\n### SaaS company (project management tool)\n\n```\nAcme is a project management tool for software engineering teams. We help teams track sprints, manage backlogs, and ship features faster.\n\nOur customers are typically engineering managers, product managers, and software developers at companies with 50–500 engineers.\n\nKey terminology: sprints, backlogs, epics, stories, velocity, ceremonies, standups, retros.\n\nMain competitors: Jira, Linear, Shortcut, GitHub Projects.\n\nOur key differentiator: designed specifically for engineering-heavy teams who want speed over customization.\n```\n\n### E-commerce (DTC brand)\n\n```\n[Brand] sells premium sustainable activewear direct to consumers in Europe. We focus on performance gear for recreational athletes — runners, cyclists, and gym-goers aged 25–40.\n\nOur customers are health-conscious buyers who prioritize quality and sustainability over price. They often compare us to Lululemon, Patagonia, and Arc'teryx.\n\nKey purchase drivers: material quality, fit, environmental impact, brand values.\n\nCurrent focus: understanding post-purchase satisfaction and repeat purchase behavior.\n```\n\n### Healthcare startup (patient communication platform)\n\n```\n[Company] helps outpatient clinics improve patient communication and reduce no-shows through automated messaging and appointment management.\n\nOur users are clinic administrators and practice managers at small-to-mid-size medical practices. Patients interact with consumer-facing tools but are not our primary buyers.\n\nKey terms: EHR integration, scheduling workflow, patient portal, HIPAA compliance, front desk staff.\n\nCurrent research: how practices handle appointment reminders and what breaks down in existing workflows.\n```\n\n---\n\n## Company Context vs. Context Documents\n\nKoji offers two ways to provide background information: company context and context documents.\n\n**Company context** (Settings → Company Context) is a persistent, always-on background for your entire account. It is applied globally to all interviews.\n\n**Context documents** are study-specific files you upload to individual studies — product specs, feature documentation, competitive analyses, previous research findings. They provide depth on the specific topic you are researching.\n\nUse both together for best results: company context handles the \"who we are and who our customers are\" layer, while context documents handle the \"what we are specifically researching right now\" layer.\n\nFor example, a product team might have company context describing their SaaS product and customer base, then upload a specific feature spec as a context document when researching that feature's adoption.\n\n---\n\n## Company Context for Agency and Multi-Client Research\n\nIf you use Koji across multiple clients or product lines, you have options:\n\n- **Rotating context**: update company context before each client's research project. Simple but requires discipline to keep current.\n- **Separate accounts per client**: enterprise-tier accounts can discuss multi-account setups with the Koji team.\n\nFor agencies, be explicit about client context: include the client company name, industry, and research goals so Koji's AI is appropriately specialized even when conducting research on behalf of a client.\n\n---\n\n## Measuring the Impact of Company Context\n\nAfter adding company context, run a few interviews and review quality scores. Interviews with well-configured company context typically score higher on:\n\n- **Relevance** (1–5): whether the conversation stayed focused on what matters for your research\n- **Depth** (1–5): whether the AI probed on the right things\n- **Coverage** (1–5): whether key questions and topics were addressed\n\nIf you are seeing low quality scores even with good context, check your research brief — especially the methodology, key questions, and topics to explore. Company context helps the AI ask better follow-ups, but the research brief drives the overall interview structure.\n\nSee the [Understanding Quality Scores](/docs/understanding-quality-scores) guide for how to interpret scores and identify what to improve.\n\n---\n\n## Tips for Maintaining Your Company Context\n\n- **Review quarterly**: update context when your product, target market, or research focus changes\n- **Add new terminology**: as your product adds features with specific names, add those terms\n- **Refine after early studies**: if you notice the AI asking off-target follow-ups, revise your context to address the gap\n- **Keep it factual**: avoid marketing language and superlatives — concrete, specific descriptions produce better follow-up questions than vague claims\n\n---\n\n## Related Resources\n\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — the study-specific configuration that works alongside company context\n- [Uploading Context Documents](/docs/uploading-context-documents) — add study-specific depth beyond account-level context\n- [Structured Questions Guide](/docs/structured-questions-guide) — combine contextual AI probing with quantitative question types\n- [Setting Up Voice Interviews](/docs/setting-up-voice-interviews) — configure the full interview experience\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — measure the impact of your context on interview quality\n- [Working with the AI Consultant](/docs/working-with-the-ai-consultant) — get help designing your full research setup\n","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Company Context: How to Make Your AI Interviewer a Domain Expert","metaDescription":"Configure Koji's company context to make your AI interviewer ask sharper, more relevant follow-up questions in every study. Includes templates and industry examples.","keywords":["company context AI interviewer","customize AI interviewer","AI interview background knowledge","research interview context","improve AI interview quality","Koji company context"],"aiSummary":"Company context is an account-level setting in Koji that provides the AI interviewer with background knowledge about your company, product, customers, and industry terminology. Unlike the per-study research brief, company context applies globally to all interviews. Setting 150–400 words of clear, factual context improves follow-up question relevance, terminology accuracy, and overall quality scores. It works alongside per-study context documents for maximum interview depth.","aiPrerequisites":["creating-your-first-study","understanding-the-research-brief"],"aiLearningOutcomes":["Understand what company context is and how it differs from the research brief","Write effective company context for your specific industry","Combine company context with context documents for best results","Measure the impact of context on interview quality scores","Maintain context as your product and research focus evolve"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"59425176-3779-434c-8abd-7696f31c60c4","slug":"choice-ranking-questions-guide","title":"Choice and Ranking Questions in AI Interviews: Capture Preference Data at Scale","url":"https://www.koji.so/docs/choice-ranking-questions-guide","summary":"Koji supports four choice-style structured question types: single choice (frequency bar chart), multiple choice (stacked frequency chart), ranking (ranked list with average position), and yes/no (pie chart). All work conversationally in text and voice mode with automatic AI probing for qualitative context.","content":"Choice and ranking questions let you collect structured preference data within an AI interview — the kind of quantitative signal that tells you *which* option resonates, not just *that* something resonates. In Koji, these question types work conversationally in both voice and text modes, and their responses aggregate automatically into charts in your research report.\n\nThis guide covers four of Koji''s six structured question types: single choice, multiple choice, ranking, and yes/no. For scale questions (NPS, CSAT, ratings), see the [Scale Questions Guide](/docs/scale-questions-guide). For the full overview of all six types, see the [Structured Questions Guide](/docs/structured-questions-guide).\n\n## The Four Choice-Style Question Types\n\n| Type | How It Works | Report Visualization |\n|---|---|---|\n| **Single choice** | Participant picks exactly one option | Frequency bar chart |\n| **Multiple choice** | Participant picks one or more options | Stacked frequency chart |\n| **Ranking** | Participant orders options by preference | Ranked list with average position |\n| **Yes/No** | Binary question | Pie/donut chart |\n\nEach type has a different widget in text mode and a different conversational delivery in voice mode. All produce structured values that aggregate automatically across your interviews.\n\n## Single Choice Questions\n\nSingle choice questions present a list of options and ask the participant to pick exactly one. They''re ideal when you need to understand which of several things is most important, most used, or most preferred.\n\n**Common use cases:**\n- \"Which of the following best describes why you switched to our product?\"\n- \"What is your primary use case for this feature?\"\n- \"Which competitor were you using before?\"\n- \"What was the biggest barrier to getting started?\"\n\n### Text Mode: Radio Buttons\n\nIn text mode, a radio button widget appears after the AI asks the question. Participants tap or click exactly one option. The response is captured immediately as the structured value, and the AI continues the conversation.\n\n### Voice Mode: Conversational Selection\n\nIn voice mode, the AI reads the question and lists the options aloud:\n\n> \"What was the primary reason you chose our product? Was it the pricing, the ease of setup, the AI capabilities, or something else?\"\n\nThe participant responds verbally, and Koji maps their answer to the closest matching option. If they mention something outside the list, and `allowOther` is enabled, it can be captured as a free-text response.\n\n### Configuration\n\n| Setting | What It Does |\n|---|---|\n| `options` | The list of choices (2–10 options recommended) |\n| `allowOther` | Adds a free-text \"Other\" option |\n\nThe `allowOther` option is powerful for discovery research: present your top hypotheses as options, but still capture answers you hadn''t anticipated.\n\n### Report: Frequency Bar Chart\n\nSingle choice responses aggregate into a **frequency bar chart** in the report. Each bar represents one option, showing the percentage and count of participants who selected it. At a glance, you see the distribution of preferences across your study.\n\n## Multiple Choice Questions\n\nMultiple choice questions let participants select all options that apply. They''re ideal when a participant might have several valid answers simultaneously.\n\n**Common use cases:**\n- \"Which of the following features do you use regularly? Select all that apply.\"\n- \"What were your reasons for canceling? Check all that apply.\"\n- \"Which research methods does your team currently use?\"\n- \"What communication channels do you prefer for support?\"\n\n### Text Mode: Checkboxes\n\nIn text mode, a checkbox widget appears with all options. Participants can select as many as they like and then confirm their selections.\n\n### Voice Mode: Flexible Multi-Select\n\nThe AI asks the question and explains that the participant can mention multiple options:\n\n> \"Which of these features do you use regularly? The options are: automated reports, voice interviews, CSV export, or the API. You can mention as many as apply.\"\n\nThe participant might respond: \"I mostly use automated reports and the API.\" Koji extracts both as selected options.\n\n### Configuration\n\nSame as single choice:\n- `options` — The list of available choices\n- `allowOther` — Enables a free-text answer for responses outside the list\n\nThe structured value for multiple choice is an array of strings (e.g., `[\"Automated reports\", \"API\"]`).\n\n### Report: Stacked Frequency Chart\n\nMultiple choice responses aggregate into a **stacked frequency chart** showing how often each option was selected. Since participants can choose multiple options, percentages may add up to more than 100%. The report shows both count and percentage for each option.\n\n## Ranking Questions\n\nRanking questions ask participants to order a list of options by preference, priority, or importance. They produce the richest data of all the choice types — showing not just *which* options matter, but in *what order*.\n\n**Common use cases:**\n- \"Please rank these features from most to least important to you.\"\n- \"Order these benefits from what matters most to what matters least.\"\n- \"Rank these pain points by how severely they affect your workflow.\"\n\n### Text Mode: Drag-and-Drop\n\nIn text mode, a drag-and-drop widget lets participants reorder items by dragging them up or down the list. The interface is intuitive and fast.\n\n### Voice Mode: Ordered Response\n\nIn voice mode, the AI reads the options and asks participants to order them verbally:\n\n> \"I''ll read you five features, and I''d like you to rank them from most important to least important for your workflow: reporting, voice interviews, the embed widget, CSV export, and the API. How would you order them?\"\n\nParticipants respond by listing them in order, and Koji extracts the sequence as an ordered array.\n\n### Report: Ranked List with Average Position\n\nRanking responses aggregate into a **ranked list with average position** across all participants. Each option shows its mean rank — making it easy to see which features are consistently prioritized and which are consistently deprioritized.\n\nFor example, if \"AI reports\" averages position 1.3 out of 5, it''s clearly the most valued feature across your user base. If \"CSV export\" averages position 4.7 out of 5, it''s consistently lowest priority.\n\n## Yes/No Questions\n\nYes/No is the simplest structured question type. It asks a binary question with exactly two possible responses.\n\n**Common use cases:**\n- \"Do you currently use this feature?\"\n- \"Have you recommended our product to someone in the last 3 months?\"\n- \"Were you able to complete your task successfully?\"\n- \"Have you experienced this specific problem?\"\n\n### Text Mode: Two Buttons\n\nIn text mode, two clear tap targets appear: Yes and No. The response is captured immediately.\n\n### Voice Mode: Binary Response\n\nThe AI asks the question and listens for a yes or no response. Natural language variants (\"I have,\" \"not really,\" \"definitely,\" \"not at all\") all map correctly to the binary value.\n\n### Report: Pie/Donut Chart\n\nYes/No responses aggregate into a **pie or donut chart** showing the percentage split. \"68% of participants have experienced this problem\" is a powerful data point for prioritization discussions.\n\n## Adding Probing to Choice Questions\n\nEvery choice and ranking question can have follow-up probing configured — asking the AI to explore the *why* behind the selection.\n\nFor a single choice question like \"What was your primary reason for switching?\", you might configure the probing instruction: \"After they answer, ask them to tell you more about what triggered that specific decision.\" The AI then naturally probes into the story behind the selection.\n\nFor a yes/no question like \"Have you experienced this problem?\", a powerful probing pattern is: if yes, \"Tell me about the last time it happened.\" If no, \"What has made that a non-issue for you?\"\n\nFor ranking questions, after the participant gives their order, the AI can probe: \"You ranked [top item] first — can you tell me more about why that one is your priority?\"\n\nThis is the core advantage of AI interviews over traditional surveys: collect the structured, aggregatable data you need *and* the qualitative context that explains it — in the same conversation. See the [AI Probing Guide](/docs/ai-probing-guide) for details on configuring follow-up behavior.\n\n## Configuration Reference\n\n| Setting | Applies To | Description |\n|---|---|---|\n| `options` | Single choice, Multiple choice, Ranking | Array of option strings (2–10 recommended) |\n| `allowOther` | Single choice, Multiple choice | Adds a free-text \"Other\" entry |\n| `maxFollowUps` | All types | Number of follow-up probing questions (0–3) |\n| `instructions` | All types | Custom AI probing instructions for this question |\n\n## Best Practices\n\n**Keep option lists short.** Five to seven options is optimal for both cognitive load and voice delivery. More than ten options in a voice interview is hard to follow.\n\n**Use single choice for \"pick the most important\" questions.** Forced choice reveals true priorities — when someone can''t select everything, you learn what actually matters most.\n\n**Use multiple choice with `allowOther` for discovery.** Present your top hypotheses as options, but give participants an escape hatch. \"Other\" responses often surface insights you hadn''t anticipated.\n\n**Use ranking when relative priority matters.** If you need to know not just what users value, but in what order, ranking produces far richer data than single choice.\n\n**Always add probing to choice questions.** The structured answer tells you *what*. The probing conversation tells you *why*. A researcher who only reads the chart is missing half the data.\n\n**Use yes/no as a conversation opener, not a standalone data point.** \"Have you experienced this problem?\" followed by \"Tell me about the last time it happened\" is far more valuable than a yes/no response in isolation.\n\n## Frequently Asked Questions\n\n**Can I use choice questions in voice mode?**\nYes. In voice mode, Koji''s AI reads the options aloud and accepts verbal responses. For ranking questions, the AI reads the list and asks the participant to order them verbally. All responses are extracted and stored as structured values.\n\n**What is the maximum number of options I can add?**\nThere is no hard technical limit, but we recommend keeping option lists to five to seven items for text mode and five or fewer for voice mode. Longer lists increase cognitive load and reduce response quality, especially in voice interviews where participants cannot see the options.\n\n**How does Koji handle the \"Other\" option in analysis?**\nWhen `allowOther` is enabled and a participant selects \"Other\" or describes something not on the list in voice mode, Koji captures their free-text response as a qualitative note. In the report, \"Other\" appears as a separate bar in the frequency chart, and the free-text responses are shown as supporting quotes.\n\n**Can I combine a ranking question with an open-ended question on the same topic?**\nAbsolutely — this is a powerful pattern. The ranking question shows priority order across your whole study; an open-ended question explores the reasoning. For example: \"Rank these features by importance\" followed by \"Tell me more about why your top-ranked feature is your priority.\"\n\n**How are multiple choice responses shown in the report?**\nEach option appears as a bar in the frequency chart, showing the number and percentage of participants who selected it. Because participants can select multiple options, percentages can exceed 100%. The chart clearly shows which options resonate most broadly.\n\n**What happens if a participant gives an ambiguous ranking in voice mode?**\nIf the ordering is unclear (\"I''d say A and B are tied for first\"), the AI asks a clarifying follow-up to establish a clear order. The final extracted ranking is stored as an ordered array.\n\n## Related Resources\n\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — Overview of all six question types\n- [Scale Questions Guide](/docs/scale-questions-guide) — How to use NPS, CSAT, and rating scales in your studies\n- [AI Probing Guide](/docs/ai-probing-guide) — How Koji''s AI follow-up questioning works\n- [Interview Mode Guide](/docs/interview-mode-guide) — Structured, exploratory, and hybrid interview modes\n- [Generating Research Reports](/docs/generating-research-reports) — How your study data becomes a research report\n- [Understanding Themes and Patterns](/docs/understanding-themes-patterns) — How qualitative themes are extracted alongside your quantitative data","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Choice and Ranking Questions in AI Interviews: Preference Data at Scale | Koji","metaDescription":"Learn how to use single choice, multiple choice, ranking, and yes/no questions in Koji AI interviews. Collect structured preference data with automatic report charts across all participants.","keywords":["multiple choice interview questions","ranking questions user research","single choice survey AI","preference ranking research tool","yes no questions interview","choice questions AI research"],"aiSummary":"Koji supports four choice-style structured question types: single choice (frequency bar chart), multiple choice (stacked frequency chart), ranking (ranked list with average position), and yes/no (pie chart). All work conversationally in text and voice mode with automatic AI probing for qualitative context.","aiPrerequisites":["Familiarity with creating Koji studies","Understanding of the Structured Questions overview"],"aiLearningOutcomes":["Use single choice questions to identify top preferences across participants","Configure multiple choice questions with allowOther for discovery research","Design ranking questions to reveal priority order","Combine choice questions with AI probing to capture both quantitative and qualitative data","Read frequency charts and ranked lists in your research report"],"aiDifficulty":"beginner","aiEstimatedTime":"9 minutes"},{"type":"documentation","id":"0e7b3b21-1986-4513-b9b2-58344dd6c5ac","slug":"scale-questions-guide","title":"Scale Questions in AI Interviews: Measure NPS, CSAT, and Ratings Automatically","url":"https://www.koji.so/docs/scale-questions-guide","summary":"Koji scale questions capture numeric ratings (NPS, CSAT, satisfaction) in AI interviews and automatically probe to understand the reasoning behind each score. Results aggregate into distribution charts in the research report. Supports configurable min/max ranges, endpoint labels, and AI anchor probing.","content":"Scale questions give you the best of both worlds in customer research: a clean, chartable number that aggregates across hundreds of responses, plus the qualitative \"why\" behind it — all captured in a single AI-powered conversation. While traditional surveys collect a rating and stop there, Koji''s scale questions pair every numeric response with automatic AI probing to surface the reasoning behind the score.\n\nThis guide explains how scale questions work in Koji, how to configure them for NPS, CSAT, effort scores, and custom satisfaction ratings, and how your results appear in your research report.\n\n## What Are Scale Questions?\n\nScale questions are one of Koji''s six structured question types. They ask participants to rate something on a numeric scale — like satisfaction from 1 to 5, or likelihood to recommend from 0 to 10. Unlike open-ended questions, which generate qualitative themes, scale questions produce a **structured numeric value** that Koji aggregates across all your interviews into a distribution chart.\n\nKoji''s complete question type library:\n\n| Type | Produces | Report Visualization |\n|---|---|---|\n| Open ended | Qualitative themes + quotes | Thematic summary |\n| **Scale** | **Numeric rating** | **Distribution chart** |\n| Single choice | Selected option | Frequency bar chart |\n| Multiple choice | Selected options | Stacked frequency chart |\n| Ranking | Ordered list | Ranked list with average position |\n| Yes/No | Binary answer | Pie/donut chart |\n\nYou can learn about all six types in the [Structured Questions Guide](/docs/structured-questions-guide). This guide goes deep on scale questions specifically.\n\n## How Scale Questions Work in Koji\n\n### Text Mode: Interactive Widgets\n\nIn a text-based interview, the AI delivers the scale question conversationally, then a visual widget appears for the participant to respond. The widget type adapts to your scale range:\n\n- **Buttons** (default for ranges of 10 or fewer points) — Tappable number buttons, perfect for 1–5 CSAT or 0–10 NPS\n- **Slider** — For wider ranges (more than 10 points), a drag slider gives more precise control\n\nThe participant selects a number, Koji immediately captures it as the structured value, and the AI continues the conversation — asking follow-up questions based on their rating.\n\n### Voice Mode: Fully Conversational\n\nIn a voice interview, there are no widgets. The AI speaks the question aloud and listens for the participant''s verbal response. For an NPS question, this sounds like:\n\n> \"On a scale of 0 to 10, how likely are you to recommend us to a friend or colleague?\"\n\nThe participant responds verbally (\"I''d say about a 7\"), and Koji extracts the numeric value from speech. The AI then follows up naturally, tailored to that specific rating.\n\nVoice mode makes scale questions feel like a genuine conversation — not a clinical survey. Conversational delivery produces more honest and considered responses than form-based rating scales.\n\n## Configuring Scale Questions\n\nWhen you set up a scale question in your study, you can configure three key parameters:\n\n| Setting | What it Controls | Default |\n|---|---|---|\n| Scale minimum | The lowest value on the scale | 1 |\n| Scale maximum | The highest value on the scale | 5 |\n| Scale labels | Human-readable labels for specific values | None |\n\n### Adding Endpoint Labels\n\nLabels transform a raw number into a meaningful rating. Always label at least the two endpoints:\n\n**NPS (0–10):** 0 = \"Not at all likely\" / 10 = \"Extremely likely\"\n\n**CSAT (1–5):** 1 = \"Very dissatisfied\" / 5 = \"Very satisfied\"\n\n**Customer Effort Score (1–7):** 1 = \"Very easy\" / 7 = \"Very difficult\"\n\nLabels appear in the widget (text mode) and are woven into how the AI phrases the question (voice mode), so participants always understand what they''re rating — without needing a preamble.\n\n## AI Probing on Scale Answers\n\nThis is where Koji separates from every traditional survey tool.\n\nAfter a participant submits a rating, the AI doesn''t just record the number and move on. With **anchor probing** enabled, the AI follows up based on the specific value given:\n\n- **High rating (8–10/10):** \"What''s been driving that positive experience?\"\n- **Mid-range (5–7/10):** \"You gave us a 6 — what would need to change for that to be a 9 or 10?\"\n- **Low rating (1–4/10):** \"What''s been the biggest source of frustration?\"\n\nThis \"anchor\" approach — using the participant''s own rating as the conversational starting point — converts a number into an actionable insight. Instead of knowing that 40% of users rate satisfaction at 3/5, you discover *why*: \"The product is solid but the onboarding documentation is nearly impossible to find.\"\n\n### Custom Probing Instructions\n\nYou can write specific instructions for how the AI should probe each scale question. For example:\n\n- \"If they rate below 7, ask specifically about what moment in their journey caused the frustration.\"\n- \"If they rate 9 or 10, ask what single thing would be impossible for them to live without.\"\n\nThis level of control is what lets platforms like Koji deliver research-grade insights at survey-level scale.\n\n### Probing Depth\n\nThe `maxFollowUps` setting controls how many follow-up questions the AI may ask per scale question:\n\n- **0** — Ask the question, record the rating, no probing\n- **1** — One follow-up (the default)\n- **2–3** — Deeper probing, best for questions where the \"why\" is the core insight\n\n## How Scale Results Appear in Your Report\n\nKoji''s research report automatically aggregates scale answers across all completed interviews.\n\n### Distribution Chart\n\nEvery scale question gets a **distribution chart** showing how responses spread across the scale values. At a glance, you can see whether ratings cluster near the top, the bottom, or split across the range. This is far more informative than a single average score.\n\n### Summary Statistics\n\nAlongside the distribution chart:\n\n- **Mean** — The average rating across all respondents\n- **Median** — The midpoint value\n- **Response count** — Number of participants who answered\n\n### Qualitative Context\n\nBelow the chart, Koji surfaces representative quotes from the probing follow-ups. If 12 out of 15 participants who rated 4/10 mentioned \"onboarding was confusing,\" that pattern surfaces immediately — without you needing to read every transcript.\n\n## Common Scale Question Configurations\n\n### Net Promoter Score (NPS)\n\nNPS is the most common use of a 0–10 scale in research.\n\n- **Scale:** 0–10\n- **Labels:** 0 = \"Not at all likely\" / 10 = \"Extremely likely\"\n- **Question:** \"On a scale of 0 to 10, how likely are you to recommend [product] to a friend or colleague?\"\n- **Probing:** Enable anchor — the AI follows up based on the specific score given\n\nPair your NPS question with an open-ended question to capture the narrative behind the score.\n\n### Customer Satisfaction (CSAT)\n\nCSAT measures satisfaction with a specific interaction, product, or experience.\n\n- **Scale:** 1–5\n- **Labels:** 1 = \"Very dissatisfied\" / 5 = \"Very satisfied\"\n- **Question:** \"How satisfied were you with [interaction]?\"\n- **Probing:** \"What would have made this experience better?\"\n\n### Customer Effort Score (CES)\n\nCES measures how easy it was for a customer to accomplish something.\n\n- **Scale:** 1–7\n- **Labels:** 1 = \"Very easy\" / 7 = \"Very difficult\"\n- **Question:** \"How easy was it to [complete the task]?\"\n- **Probing:** For high-effort ratings: \"Where specifically did you hit friction?\"\n\n### Custom Satisfaction Rating\n\n- **Scale:** 1–10\n- **Labels:** 1 = \"Very poor\" / 10 = \"Excellent\"\n- **Question:** \"How would you rate [aspect] on a scale of 1 to 10?\"\n- **Probing:** Anchor to specific rating\n\n## Scale Questions vs. Traditional Survey Rating Scales\n\nTraditional surveys like those built in SurveyMonkey or Typeform present a scale as a static form element — respondents select a number and click Next. There''s no follow-up, no probing, and no way to understand what drove the rating.\n\n| | Traditional Surveys | Koji Scale Questions |\n|---|---|---|\n| Delivery | Static form field | Conversational AI |\n| Follow-up | None | Automatic probing based on rating |\n| Voice support | No | Yes — fully conversational |\n| Analysis | Manual | Automatic distribution charts |\n| Qualitative context | None | AI-extracted from probing conversation |\n\nWith platforms like Koji, the number tells you *what*; the probing conversation tells you *why*. Both in the same research session, at scale.\n\n## Best Practices\n\n**Always add endpoint labels.** Without them, participants interpret the scale differently, making your data inconsistent across respondents.\n\n**Use standard ranges for benchmark metrics.** NPS is always 0–10; CSAT is typically 1–5. Deviating from standards makes benchmarking against industry data harder.\n\n**Enable anchor probing.** The follow-up is where the real insight lives. A 6/10 NPS score is a data point; \"I gave you a 6 because the mobile app crashes every time I export data\" is an actionable finding.\n\n**Limit scale questions per study.** Three to four quantitative questions (scale, choice, yes/no) alongside five to eight open-ended questions is a good balance. Too many scales and you lose the depth that makes AI interviews valuable.\n\n**Mix with open-ended questions.** Start with quantitative (to benchmark), then follow with open-ended (to understand). See the [Interview Mode Guide](/docs/interview-mode-guide) for structural patterns that work well.\n\n## Frequently Asked Questions\n\n**What is the difference between a scale question and a Likert question?**\nA Likert question uses fixed agreement statements (\"Strongly agree / Strongly disagree\") with labeled response options. Koji''s scale question is more flexible — you define the numeric range, the endpoint labels, and what exactly is being rated. This makes it suitable for NPS (0–10), CSAT (1–5), CES (1–7), or any custom metric.\n\n**Can participants skip a scale question?**\nYes. In text mode, participants can tap \"Skip.\" In voice mode, they can verbally decline. Skipped responses are flagged in the report and excluded from aggregation — maintaining data quality without forcing responses on unwilling participants.\n\n**How many scale questions should I add to one study?**\nThree to four quantitative questions total (across scale, choice, and yes/no types) is a healthy ceiling. More than that, and the interview starts to feel like a survey rather than a conversation, which reduces completion rates and response quality.\n\n**How does Koji handle scale questions in voice mode?**\nKoji''s AI speaks the question aloud and listens for a numeric response. It understands natural phrasing like \"I''d say about a seven\" and extracts the number. If the response is ambiguous, the AI politely asks for clarification before moving on.\n\n**Can I see which participant gave which rating?**\nYes. In your study''s Recruit tab, each participant row links to their full transcript. You can see the exact rating alongside the complete conversation. The research report aggregates ratings across all participants for your distribution charts.\n\n**Does probing work differently for high vs. low ratings?**\nWhen anchor probing is enabled, Koji''s AI is aware of the specific rating and tailors its follow-up accordingly — asking high raters about positive drivers and low raters about friction points. Custom probing instructions give you even more control over how the AI responds to specific rating ranges.\n\n## Related Resources\n\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — Overview of all six question types\n- [Interview Mode Guide](/docs/interview-mode-guide) — Structured, exploratory, and hybrid patterns\n- [NPS Follow-Up Interviews](/docs/nps-follow-up-interviews) — Running NPS research at scale with AI\n- [Setting Up Voice Interviews](/docs/setting-up-voice-interviews) — How voice mode works technically\n- [Generating Research Reports](/docs/generating-research-reports) — How Koji builds your report from interview data\n- [Insights Chat Guide](/docs/insights-chat-guide) — Ask natural language questions about your research data","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Scale Questions in AI Interviews: NPS, CSAT & Rating Scales | Koji","metaDescription":"Learn how Koji scale questions capture NPS, CSAT, and satisfaction ratings in AI interviews — with automatic probing to understand the why behind every score and distribution charts in your report.","keywords":["scale questions AI interview","NPS AI research","CSAT automated research","rating scale interview tool","likert scale AI interview","satisfaction score research"],"aiSummary":"Koji scale questions capture numeric ratings (NPS, CSAT, satisfaction) in AI interviews and automatically probe to understand the reasoning behind each score. Results aggregate into distribution charts in the research report. Supports configurable min/max ranges, endpoint labels, and AI anchor probing.","aiPrerequisites":["Familiarity with creating Koji studies","Understanding of the metric you want to measure (NPS, CSAT, or custom rating)"],"aiLearningOutcomes":["Configure scale questions with custom min/max ranges and endpoint labels","Understand how scale questions render in text vs voice mode","Enable anchor probing to automatically follow up on ratings","Read and interpret distribution charts in your research report","Apply NPS, CSAT, and CES configurations correctly"],"aiDifficulty":"beginner","aiEstimatedTime":"8 minutes"},{"type":"documentation","id":"92da6f45-d005-4c73-bca4-8cfb53758804","slug":"interview-mode-guide","title":"Structured, Exploratory, and Hybrid: Choosing the Right Interview Mode in Koji","url":"https://www.koji.so/docs/interview-mode-guide","summary":"Koji offers three interview modes that control how the AI conducts conversations. Structured mode follows key questions systematically and optimizes for coverage — best for validation, large-N studies, and repeated tracking research. Exploratory mode follows participant threads and prioritizes depth — best for generative research, new markets, and sensitive topics. Hybrid mode (the default) balances both by working through key questions while following valuable tangents. Mode is set in the research brief and affects quality score dimensions differently: structured optimizes coverage, exploratory optimizes depth, hybrid balances both.","content":"## Structured, Exploratory, and Hybrid: Choosing the Right Interview Mode in Koji\n\n**The short answer:** Koji offers three interview modes that control how your AI conducts conversations: structured (follows key questions closely), exploratory (open-ended discovery), and hybrid (default — starts structured, follows interesting threads). Most research benefits from hybrid mode, but understanding when to choose each mode will make every study more effective.\n\nOne of the most consequential decisions in research design is how much structure to impose on your interviews. Too much structure and you miss unexpected insights. Too little and the conversation wanders without producing actionable data. Koji's three interview modes let you dial this precisely — matching the interview style to your research goals.\n\nThis guide explains each mode, when to use it, how it affects the AI's behavior, and how to configure it in your research brief.\n\n---\n\n## The Three Interview Modes\n\n### Structured Mode\n\nIn structured mode, Koji's AI acts like a thorough systematic interviewer: it works through your key questions methodically, following up on each one, and ensures every required question is covered before the interview ends.\n\n**How the AI behaves in structured mode:**\n- Follows your key questions in sequence\n- Ensures all required questions are addressed before closing\n- Probes on each question for depth (controlled by the question's maxFollowUps setting)\n- Returns to unanswered questions if the conversation drifts\n- Treats key questions as a checklist to complete\n\n**When to use structured mode:**\n- **Validation studies**: you have a specific hypothesis to test and need consistent data across participants\n- **Large-N studies**: 50 or more participants where you need comparable data points\n- **Metric tracking**: studies that will be repeated over time — quarterly research, seasonal comparisons — where you need the same questions answered each time\n- **Stakeholder-driven research**: when your stakeholders have defined specific questions they want answered\n- **Mixed-methods studies**: when combining Koji's qualitative data with quantitative survey data and needing coverage of specific variables\n\n**The trade-off:** structured mode is more reliable but can feel slightly more formal. Participants who would have shared surprising insights if allowed to follow their own thread may be redirected back to the script.\n\n---\n\n### Exploratory Mode\n\nIn exploratory mode, Koji's AI acts like a skilled ethnographer: it follows the participant's energy and curiosity, probing unexpected directions and building deep rapport. There are no required questions to check off — the goal is to understand the participant's experience in their own terms.\n\n**How the AI behaves in exploratory mode:**\n- Uses topics to explore (not key questions) as loose starting points\n- Follows interesting threads the participant introduces, even if they diverge from planned topics\n- Asks more open-ended, \"tell me more\" and \"what was that like?\" style probes\n- Does not chase missed topics — if the conversation naturally skips something, that is OK\n- Prioritizes depth over coverage\n\n**When to use exploratory mode:**\n- **Generative research**: you are trying to understand a problem space you do not yet understand well\n- **New markets or audiences**: you are talking to a segment you have never researched before\n- **Ethnographic studies**: you want to understand participants' lives, workflows, and mental models in context\n- **Pre-brief research**: running a small exploratory study before designing your main research brief\n- **Sensitive topics**: when a flexible conversational approach produces more honest, open responses than a structured script\n\n**The trade-off:** exploratory mode produces richer individual interviews but less consistent data across participants. It is harder to aggregate and quantify, which is fine when discovery is the goal — but not ideal when you need data your stakeholders can count.\n\n---\n\n### Hybrid Mode (Default)\n\nHybrid is Koji's default mode — and the right choice for most research projects. It starts with your key questions like structured mode, but gives the AI permission to follow interesting threads when they emerge, like exploratory mode.\n\n**How the AI behaves in hybrid mode:**\n- Works through key questions as the primary structure\n- Pursues unexpected insights when participants raise compelling points\n- Returns to key questions after following a thread\n- Uses judgment about when to probe versus when to move on\n- Balances coverage with depth\n\n**When to use hybrid mode:**\n- **Most product research**: you have specific questions but want to capture unexpected insights too\n- **Customer discovery**: you know what you are looking for but want to discover what you do not know\n- **Validation with nuance**: testing a hypothesis while remaining open to being wrong in interesting ways\n- **Complex topics**: research areas where the most valuable answers are often tangential to the direct question asked\n\n**The trade-off:** hybrid mode requires good brief quality to work well. If your key questions are vague, the AI will not have clear structure to organize around. The better your brief, the better hybrid performs.\n\n---\n\n## How to Set Interview Mode in Koji\n\nInterview mode is configured in the research brief through three paths:\n\n**1. Via the AI Consultant (recommended for new studies)**\nWhen setting up your study, describe what kind of research you are doing to the AI consultant. It will suggest an appropriate mode based on your goal and explain the trade-offs before you commit to one.\n\n**2. Via manual brief editing**\nGo to the **Brief** tab in your study, expand the **Interview Plan** section, and select your preferred mode from the mode dropdown: Structured, Exploratory, or Hybrid.\n\n**3. Via the API**\nWhen creating a study programmatically, include the `interviewPlan.mode` field in your brief payload with one of the three values: `\"structured\"`, `\"exploratory\"`, or `\"hybrid\"`.\n\n---\n\n## Combining Interview Mode with Structured Questions\n\nInterview mode and structured question types work together to shape the full interview experience.\n\n**Structured questions** — scale, single_choice, multiple_choice, ranking, yes_no — always have some structured characteristics. The AI presents the widget (in text mode) or asks the question conversationally (in voice mode), collects the response, then optionally probes based on the question's `maxFollowUps` setting. See the [Structured Questions Guide](/docs/structured-questions-guide) for full detail on all 6 question types.\n\n**The interview mode** primarily affects how open-ended questions and exploratory topics are handled. It controls what the AI does between structured questions and how it responds to participant tangents.\n\nRecommended combinations by research goal:\n\n| Research Goal | Recommended Mode | Question Mix |\n|---|---|---|\n| NPS driver analysis | Structured | 1 scale (NPS 0–10), 3 open_ended follow-ups |\n| Product discovery | Hybrid | 4–5 open_ended key questions, 1 single_choice |\n| Market benchmarking | Structured | 3–4 structured types, 2 open_ended |\n| User empathy research | Exploratory | All open_ended, topic-based guidance |\n| Pre/post comparison | Structured | Consistent structured questions across all waves |\n| Win/loss analysis | Hybrid | Cover decision criteria, follow threads on unexpected competitors |\n| New market exploration | Exploratory | Broad topics, no required questions |\n| Annual benchmark report | Structured | Consistent question coverage enables year-over-year comparison |\n\n---\n\n## How Mode Affects Quality Scores\n\nKoji's quality scoring system evaluates interviews on relevance, depth, and coverage. Interview mode affects how each dimension performs:\n\n- **Structured mode** optimizes for **coverage**: quality scores are higher when all key questions are answered, lower when questions are skipped. This mode produces the most consistent per-question data.\n- **Exploratory mode** optimizes for **depth**: quality scores reward rich, detailed, multi-layered responses over complete question coverage. Individual interviews may score very high on depth even if only two topics were discussed.\n- **Hybrid mode** balances both: quality scores reflect a mix of coverage and depth, rewarding studies where the AI covered key questions while also following valuable threads.\n\nWhen reviewing quality scores on your Recruit tab, keep mode in mind. An exploratory study with an average quality score of 3.5 out of 5 may contain excellent individual interviews — the score reflects the inherently open-ended nature of the data, not a problem with the AI or participants.\n\nFor a deeper explanation of the scoring dimensions, see [Understanding Quality Scores](/docs/understanding-quality-scores).\n\n---\n\n## Changing Mode Mid-Study\n\nYou can change the interview mode on a live study, but the change only applies to new interviews going forward. Existing completed interviews are not affected.\n\nChanging mode mid-study is useful when:\n- Early interviews reveal your questions are too rigid — switch to hybrid or exploratory to open up\n- Early exploratory interviews have given you enough direction to focus — switch to structured for the remaining participants\n- You are running two cohorts and want to compare structured vs. exploratory data collection approaches\n\n---\n\n## Mode and Voice vs. Text Interviews\n\nInterview mode affects both voice and text interviews identically at the AI behavior level — the same structured, exploratory, or hybrid logic applies regardless of channel.\n\nThe one notable difference: in text mode, structured questions (scale, choice, ranking, yes_no) render as interactive widgets that participants click or drag. In voice mode, the same questions are asked conversationally and the AI extracts the structured value from the spoken response. Interview mode controls the conversational behavior around these widgets, not the widgets themselves.\n\n---\n\n## Quick Reference: Choosing Your Mode\n\n**Use structured when** you need consistent, comparable data across many participants and have specific questions that must be answered.\n\n**Use exploratory when** you are in discovery mode, researching something unfamiliar, or working in a sensitive area where structure might close participants off.\n\n**Use hybrid when** you have clear research questions but also want to capture what you did not think to ask — which is most of the time.\n\nWhen in doubt, start with hybrid. As you build intuition for how your participants respond in interviews, you will develop a sense for when a topic calls for more or less structure.\n\n---\n\n## Related Resources\n\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — the full brief structure including interview mode configuration\n- [Structured Questions Guide](/docs/structured-questions-guide) — the 6 question types and how they behave in each mode\n- [Choosing a Methodology](/docs/choosing-a-methodology) — how methodology frameworks (Mom Test, JTBD, etc.) interact with interview mode\n- [Editing the Brief Manually](/docs/editing-the-brief-manually) — step-by-step guide to configuring every aspect of your brief\n- [How to Write Great Interview Questions](/docs/writing-interview-questions) — question design principles for each mode\n- [Understanding Quality Scores](/docs/understanding-quality-scores) — how mode affects quality metrics in your Recruit tab\n","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Structured vs. Exploratory vs. Hybrid: Interview Modes in Koji","metaDescription":"Understand Koji's three interview modes — structured, exploratory, and hybrid — and choose the right one for your research goals. Includes a quick reference table and examples.","keywords":["structured vs exploratory interview","hybrid interview mode","AI interview structure","Koji interview modes","qualitative research structure","interview mode research design"],"aiSummary":"Koji offers three interview modes that control how the AI conducts conversations. Structured mode follows key questions systematically and optimizes for coverage — best for validation, large-N studies, and repeated tracking research. Exploratory mode follows participant threads and prioritizes depth — best for generative research, new markets, and sensitive topics. Hybrid mode (the default) balances both by working through key questions while following valuable tangents. Mode is set in the research brief and affects quality score dimensions differently: structured optimizes coverage, exploratory optimizes depth, hybrid balances both.","aiPrerequisites":["creating-your-first-study","understanding-the-research-brief"],"aiLearningOutcomes":["Understand the behavioral difference between structured, exploratory, and hybrid interview modes","Choose the right mode for your specific research goal","Combine interview mode with structured question types effectively","Understand how mode affects quality scores and data comparability","Change mode mid-study when research needs evolve"],"aiDifficulty":"beginner","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"6d6a76d0-e479-4d70-8387-00bef161ca95","slug":"writing-a-research-question","title":"Writing a Research Question","url":"https://www.koji.so/docs/writing-a-research-question","summary":"A well-framed research question is the foundation of any successful study. This guide covers the five qualities of strong research questions, common pitfalls, examples across use cases, and how your research question connects to structured question design.","content":"A great research question is the single most important ingredient in any study — it determines what you'll learn, who you'll talk to, and whether your findings will actually be useful. Before you open Koji, before you choose a methodology, before you write a single interview question, you need to get this right.\n\n## Why Your Research Question Matters\n\nThink of your research question as a compass. Every decision in your study — from the methodology you pick to the probes your interviewer uses — flows from this one statement. A vague or poorly framed question leads to vague, unusable findings. A sharp, well-scoped question leads to insights you can act on immediately.\n\nWhen you [create a new study](/docs/creating-your-first-study), the very first thing Koji asks is what you want to learn. Your answer to that prompt shapes everything the [AI Consultant](/docs/working-with-the-ai-consultant) suggests, so it pays to spend a few minutes thinking it through before you type.\n\n## What Makes a Good Research Question\n\nA strong research question has five qualities:\n\n### 1. It's Specific Enough to Be Answerable\n\nYour question should point to a clear area of inquiry. You don't need to know the answer yet — that's the whole point of doing research — but you should be able to imagine what a useful answer looks like.\n\n**Too broad:** \"What do customers think about our product?\"\n**Better:** \"What frustrations do first-time users encounter during their first week with our onboarding flow?\"\n\nThe first question could lead anywhere. The second tells you exactly who to talk to, what to ask about, and what kind of findings to expect.\n\n### 2. It's Open-Ended\n\nResearch questions should start with \"how,\" \"why,\" \"what,\" or \"in what ways\" — never with \"do,\" \"is,\" or \"will.\" Yes/no framing leads to yes/no answers, which rarely reveal the deeper stories you need.\n\n**Closed:** \"Do users like the new dashboard?\"\n**Open:** \"How do users incorporate the new dashboard into their daily workflow?\"\n\n### 3. It Focuses on Behavior, Not Opinion\n\nPeople are unreliable narrators of their own preferences, but they're excellent at describing what they actually do. Frame your question around actions, decisions, and experiences rather than hypothetical preferences.\n\n**Opinion-based:** \"Would customers pay more for a premium tier?\"\n**Behavior-based:** \"How do power users currently work around the limitations of the free plan?\"\n\n### 4. It's Scoped to a Timeframe or Context\n\nAdding a specific context or timeframe makes your question more researchable and your findings more precise.\n\n**Unscoped:** \"Why do people churn?\"\n**Scoped:** \"What factors lead new subscribers to cancel within the first 30 days?\"\n\n### 5. It Doesn't Contain the Answer\n\nWatch out for questions that smuggle in assumptions. If your question presupposes a specific conclusion, you'll unconsciously design your study to confirm it.\n\n**Leading:** \"Why is our complicated pricing page causing users to leave?\"\n**Neutral:** \"How do prospective customers evaluate and compare our pricing before making a purchase decision?\"\n\n## Examples by Use Case\n\nHere are research questions across common scenarios to spark your thinking:\n\n| Scenario | Example Research Question |\n|---|---|\n| New product idea | \"What workarounds do marketing managers currently use to coordinate content calendars across teams?\" |\n| Feature adoption | \"How do existing users discover and start using the reporting feature within their first month?\" |\n| Churn investigation | \"What events or experiences lead mid-market customers to begin evaluating competitor solutions?\" |\n| Onboarding improvement | \"What do new users find confusing or unnecessary during the setup process?\" |\n| Market entry | \"How do small business owners in the EU currently handle tax compliance, and where do they feel underserved?\" |\n| Employee experience | \"What aspects of the remote work policy create friction for cross-functional collaboration?\" |\n\n## From Research Question to Study Design\n\nOnce you have your research question, you're ready to start building your study in Koji. Here's how the pieces connect:\n\n1. **Enter your question** when you create a new study. Be as specific as you were when you refined it above.\n2. **Chat with the AI Consultant** — it will use your question to suggest a [methodology](/docs/choosing-a-methodology), identify your target participant, and draft interview questions. The more precise your research question, the better these suggestions will be.\n3. **Review the research brief** that gets generated. Your research question becomes the problem statement at the top of the brief, and every other section should trace back to it.\n4. **Design structured questions** — your research question also guides which [structured question types](/docs/structured-questions-guide) to use. A question about satisfaction levels might call for scale ratings with anchor probing, while a question about tool preferences might call for ranking or multiple choice. The AI Consultant will suggest appropriate question types based on your research question.\n\nIf at any point the brief doesn't feel right, it's often worth revisiting your research question. Sometimes the act of designing a study reveals that you were actually asking two questions at once, or that the real question is slightly different from where you started.\n\n## Common Pitfalls to Avoid\n\n**Trying to answer everything at once.** If your question has the word \"and\" in it, you might actually have two studies. It's better to run two focused studies than one sprawling one.\n\n**Writing for stakeholders instead of for learning.** Your research question isn't a slide title. It doesn't need to sound impressive — it needs to guide your inquiry. Keep it honest and practical.\n\n**Skipping this step entirely.** It's tempting to jump straight into writing interview questions. Resist that urge. Five minutes spent on a clear research question saves hours of wading through unfocused data later.\n\n**Being too attached to your first draft.** Your research question will often evolve as you [work with the AI Consultant](/docs/working-with-the-ai-consultant). That's not a sign of failure — it's a sign that your thinking is sharpening.\n\n## Quick Self-Check\n\nBefore moving on, run your research question through this checklist:\n\n- [ ] Can I explain what a useful answer would look like?\n- [ ] Is it open-ended (starts with how, why, or what)?\n- [ ] Does it focus on behavior or experience, not just opinion?\n- [ ] Is it scoped to a specific participant group, context, or timeframe?\n- [ ] Does it avoid baking in assumptions or conclusions?\n\nIf you can check all five boxes, you're in excellent shape. Head over to [Creating Your First Study](/docs/creating-your-first-study) to put that question to work.\n\n## Related Articles\n\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — how your research question becomes a full study design\n- [Working with the AI Consultant](/docs/working-with-the-ai-consultant) — how to collaborate on study design\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — choosing question types that match your research goals\n- [Choosing a Methodology](/docs/choosing-a-methodology) — picking the right research framework","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Writing a Research Question — Koji Docs","metaDescription":"Learn how to frame a clear, focused research question that guides your entire qualitative study and leads to actionable insights.","keywords":["research question","study design","qualitative research","interview design","research methodology"],"aiSummary":"A well-framed research question is the foundation of any successful study. This guide covers the five qualities of strong research questions, common pitfalls, examples across use cases, and how your research question connects to structured question design.","aiPrerequisites":["creating-your-first-study"],"aiLearningOutcomes":["Write a focused, open-ended research question","Distinguish between good and bad research questions","Avoid common framing pitfalls like leading questions and opinion-based framing","Connect your research question to study design decisions"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"b01ca330-d5f7-483b-9d4e-8a76a255ce68","slug":"working-with-the-ai-consultant","title":"Working with the AI Consultant","url":"https://www.koji.so/docs/working-with-the-ai-consultant","summary":"The AI Consultant is a conversational partner that helps you design research studies, including structured question selection. This guide covers strategies for effective collaboration, how to iterate on suggestions, and how to design the right mix of open-ended and structured questions.","content":"Koji's AI Consultant is your collaborative partner for study design — a research-savvy conversational guide that helps you go from a rough idea to a complete, publishable research brief. The more effectively you communicate with it, the better your study will be.\n\n## How the AI Consultant Works\n\nWhen you [create a new study](/docs/creating-your-first-study), you enter a chat-based workspace. On one side, you'll see the conversation with the AI Consultant. On the other side, you'll see the [research brief](/docs/understanding-the-research-brief) — a living document that the Consultant builds and updates as you talk.\n\nThe Consultant doesn't just ask you a series of fill-in-the-blank questions. It has a genuine conversation with you, probing your goals, challenging vague assumptions, suggesting approaches you might not have considered, and progressively shaping the brief into something ready to publish.\n\nFor a deeper look at the technology behind this, see [Understanding the AI Consultant](/docs/understanding-the-ai-consultant).\n\n## Starting the Conversation\n\nThe first message you send sets the tone for the entire design session. Here's how to make it count.\n\n### Be Specific About What You Want to Learn\n\nThe Consultant uses your opening message to understand the scope and direction of your study. Compare these two starting points:\n\n**Vague:** \"I want to learn about our customers.\"\n**Specific:** \"I want to understand why enterprise customers in the healthcare vertical cancel their subscription within the first 90 days, and what we could have done differently to retain them.\"\n\nThe second message gives the Consultant enough context to immediately suggest a methodology, define a target participant, and start drafting relevant interview questions — including recommending appropriate [structured question types](/docs/structured-questions-guide) like NPS scales or satisfaction ratings. The first message would require several rounds of back-and-forth just to get to the same starting point.\n\nIf you've already [written a research question](/docs/writing-a-research-question), paste it in as your first message. That's exactly the kind of specificity that helps.\n\n### Share Context Early\n\nDon't make the Consultant guess about your situation. In your first few messages, consider mentioning:\n\n- **What you already know** — prior research findings, analytics data, hunches from your team\n- **What triggered this study** — a drop in metrics, a product launch, a strategic question from leadership\n- **Who your participants are** — their roles, company size, technical sophistication, geography\n- **Constraints you're working with** — timeline, budget, number of participants you can realistically recruit\n\nYou can also [upload context documents](/docs/uploading-context-documents) like previous research reports, product specs, or customer feedback summaries. These give the Consultant additional background to work with.\n\n## Conversation Strategies That Work\n\n### Ask for Alternatives\n\nOne of the most powerful things you can do is ask the Consultant to show you options. Instead of accepting the first suggestion, try:\n\n- \"Can you show me how this study would look with a Jobs to Be Done approach instead?\"\n- \"What would the interview questions look like if we focused on the onboarding experience rather than the overall product?\"\n- \"Give me three different ways to phrase that question.\"\n- \"Should this be a scale question or an open-ended question? Show me both.\"\n\nThe Consultant is great at generating variations, and comparing options often helps you discover what you really care about.\n\n### Challenge and Push Back\n\nThe Consultant isn't precious about its suggestions. If something doesn't feel right, say so:\n\n- \"That question feels too leading — can you make it more neutral?\"\n- \"I think we're missing the emotional dimension here. Can we add questions about how users feel during this process?\"\n- \"This target participant description is too broad. Let's narrow it to people who signed up in the last 6 months.\"\n- \"Can we add a satisfaction scale question after the open-ended section so we get a benchmarkable metric?\"\n\nDirect feedback leads to better results. The Consultant adapts quickly when you tell it exactly what to change.\n\n### Iterate in Layers\n\nDon't try to get everything perfect in one pass. A productive session usually follows this arc:\n\n1. **Big picture first** — describe your research goal and let the Consultant suggest an overall approach\n2. **Methodology and participant** — discuss who you want to talk to and what framework fits best\n3. **Interview questions** — refine the specific questions, their order, and the probing strategy\n4. **Structured question design** — decide which questions should capture quantitative data via [structured question types](/docs/structured-questions-guide) (scales, choices, rankings) and configure probing depth\n5. **Fine-tuning** — adjust wording, add or remove questions, check for bias\n\nEach layer builds on the previous one. If you try to jump straight to wordsmithing individual questions before agreeing on the methodology, you'll end up going in circles.\n\n### Use the Brief as a Reference Point\n\nAs you chat, keep an eye on the research brief panel. The Consultant updates it in real time based on your conversation. If you notice something in the brief that doesn't match what you discussed, call it out:\n\n- \"The problem statement in the brief doesn't quite capture what we just talked about. Can you update it?\"\n- \"I see the brief has 12 questions — that feels like too many for a 20-minute interview. Can we trim it down?\"\n- \"Can we change that open-ended question about satisfaction into a 1-10 scale with anchor probing?\"\n\nThinking of the brief as your shared canvas helps keep the conversation productive.\n\n## Things to Avoid\n\n### Don't Be Passive\n\nThe Consultant is a collaborator, not a vending machine. If you just say \"design a study for me\" and accept whatever comes back, you'll miss the opportunity to shape it into something truly useful for your specific context. The best studies come from active back-and-forth.\n\n### Don't Overcomplicate Early\n\nResist the urge to specify every detail upfront. Let the conversation unfold naturally. You can always add complexity later — it's much harder to simplify an overloaded brief.\n\n### Don't Ignore the Methodology Discussion\n\nWhen the Consultant suggests a methodology — Mom Test, Jobs to Be Done, Customer Discovery, Exploratory, Lead Magnet, or another approach — take a moment to understand why. Each methodology shapes the questioning style, follow-up strategy, and analysis approach differently. If you're not sure which methodology is right, ask for an explanation or read our [methodology guide](/docs/choosing-a-methodology).\n\n### Don't Skip the Review\n\nBefore [publishing your study](/docs/publishing-your-study), read through the entire brief carefully. The Consultant builds the brief incrementally, and sometimes earlier sections need a tweak to align with decisions you made later. You can always [edit the brief manually](/docs/editing-the-brief-manually) or ask the Consultant to make changes.\n\n## Revisiting and Refining\n\nYou can come back to the Consultant at any time before publishing. If you've stepped away and have new thoughts, simply reopen the study and pick up the conversation. The Consultant remembers everything you've discussed.\n\nSome researchers find it helpful to draft the study in one session, sleep on it, and come back with fresh eyes the next day. The Consultant is there whenever you're ready.\n\n## Getting the Most Out of the Collaboration\n\nThink of the AI Consultant as a research-trained colleague who has unlimited patience, no ego, and extensive knowledge of qualitative methodologies. It works best when you:\n\n- Bring your domain knowledge (you know your users and your business)\n- Let it bring methodological expertise (it knows how to structure studies and design [structured questions](/docs/structured-questions-guide))\n- Meet in the middle through honest, iterative conversation\n\nWhen the conversation is done, you should have a [research brief](/docs/understanding-the-research-brief) that you feel confident about — one that reflects your goals, speaks to your participants, and asks the right questions in the right way, with the right mix of open-ended exploration and structured data capture.\n\n## Related Articles\n\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — what the brief contains and how to review it\n- [Editing the Brief Manually](/docs/editing-the-brief-manually) — making direct changes to the brief\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — designing quantitative questions within your study\n- [Choosing a Methodology](/docs/choosing-a-methodology) — understanding the available research frameworks\n- [Publishing Your Study](/docs/publishing-your-study) — going live with your study","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Working with the AI Consultant — Koji Docs","metaDescription":"Learn strategies for chatting effectively with Koji's AI Consultant to design focused, high-quality research studies.","keywords":["AI consultant","study design","research design","AI collaboration","interview design","research brief"],"aiSummary":"The AI Consultant is a conversational partner that helps you design research studies, including structured question selection. This guide covers strategies for effective collaboration, how to iterate on suggestions, and how to design the right mix of open-ended and structured questions.","aiPrerequisites":["creating-your-first-study"],"aiLearningOutcomes":["Start a productive conversation with the AI Consultant","Use strategies like asking for alternatives and iterating in layers","Avoid common mistakes like being too passive or overcomplicating early","Collaborate effectively to produce a strong research brief"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"c014b701-044d-449b-a045-9d20efb99c24","slug":"understanding-the-research-brief","title":"Understanding the Research Brief","url":"https://www.koji.so/docs/understanding-the-research-brief","summary":"The research brief is the blueprint for your study. This guide walks through every section — problem context, methodology, target participant, and interview plan with structured questions — explaining what each contains and how to review it critically.","content":"The research brief is the blueprint for your study — it's the document that tells Koji's AI interviewer exactly how to conduct your conversations with participants. Understanding what each section does (and why it matters) helps you create better studies and catch issues before you go live.\n\n## What Is the Research Brief?\n\nWhen you [work with the AI Consultant](/docs/working-with-the-ai-consultant), the conversation produces a structured document called the research brief. You'll see it in the artifact panel to the right of the chat. This brief contains everything Koji needs to run your study: the problem you're investigating, who you want to talk to, how the conversation should be structured, and exactly what questions to ask — including [structured questions](/docs/structured-questions-guide) that capture quantitative data alongside qualitative depth.\n\nOnce you [publish your study](/docs/publishing-your-study), the brief becomes the instruction set for the AI interviewer. So what you see in the brief is very close to what participants will experience.\n\n## Sections of the Research Brief\n\nLet's walk through each section, what it contains, and what to look for when reviewing.\n\n### Problem Context\n\nThis is the core of your study — a set of fields that capture what you're trying to learn and why it matters.\n\n- **Problem Statement** — a concise description of what you're investigating, typically one to three sentences that capture your research question in context\n- **Decision to Inform** — what business or product decision this research will help you make\n- **Hypothesis** — what you currently believe to be true, which the study will test\n- **Success Criteria** — how you'll know the research was successful\n- **Problem Cost** — the impact of the problem if left unaddressed (e.g., revenue loss, user churn)\n- **Out of Scope** — what this study explicitly will not cover\n\n**What to look for:**\n- Does the problem statement accurately reflect what you want to learn?\n- Is the hypothesis testable through conversations with participants?\n- Are you clear about what decisions this research will inform?\n\nThe problem context sets the frame for everything below it. If this section feels off, the rest of the brief will drift too. Don't hesitate to ask the AI Consultant to revise it, or [edit it manually](/docs/editing-the-brief-manually).\n\n### Methodology\n\nThis section identifies which research framework your study follows. You can learn about all of them in our [methodology guide](/docs/choosing-a-methodology).\n\nThe methodology affects more than just the questions — it shapes how the AI interviewer follows up, what it probes for, and how it adapts to participant responses. Each methodology includes:\n\n- **Core Principles** — the foundational rules the AI interviewer follows\n- **Question Patterns** — what types of questions work well within this framework\n- **Probe Points** — what the AI should dig into during follow-ups\n- **Anti-Patterns** — what to avoid (e.g., leading questions in Mom Test)\n\nFor example:\n\n- **Mom Test** interviews avoid leading questions and focus on past behavior rather than hypothetical preferences\n- **Jobs to Be Done** interviews dig into the circumstances, motivations, and tradeoffs behind decisions\n- **Customer Discovery** interviews focus on validating problem-solution fit through open exploration\n- **Exploratory** interviews follow interesting threads with high probing depth for maximum qualitative discovery\n- **Lead Magnet** interviews capture quotable statistics and benchmarkable data for public reports\n\n**What to look for:**\n- Does the methodology match the type of insight you need?\n- If you're unfamiliar with the chosen methodology, ask the AI Consultant to explain why it recommended it\n\n### Target Participant\n\nThis section defines who should participate in your study. Rather than relying on demographics, Koji focuses on behavioral characteristics — what people have done, experienced, or are currently doing. This behavioral focus produces better participants and more relevant conversations.\n\nThe target participant section includes:\n\n- **Required Experience** — what the participant must have experienced (e.g., \"has evaluated at least two CRM tools in the past year\")\n- **Behavior of Interest** — the specific behavior your study investigates (e.g., \"abandoned checkout in the last 30 days\")\n- **Relationship to Problem** — how the participant connects to the problem you're studying\n- **Screening Question** — a question to verify participant fit at the start of the interview\n\n**What to look for:**\n- Is the description specific enough to recruit? \"Small business owners\" is broad; \"founders of B2B SaaS companies with fewer than 50 employees who launched in the last two years\" is actionable.\n- Does it focus on behavior and experience rather than demographic categories? Koji deliberately avoids demographic-based targeting in favor of behavioral criteria that better predict relevant insights.\n- Is the participant directly connected to your problem statement? If you're studying onboarding friction, you want people who have recently onboarded — not long-tenured power users.\n- Are there any groups you should exclude? Sometimes defining who you don't want to talk to is just as important.\n\n### Interview Plan\n\nThis is the heart of the brief — the structured conversation guide that the AI interviewer will follow. It contains several components:\n\n**Opening Approach**\nHow the AI interviewer begins the conversation — setting the tone, building rapport, and easing the participant into the topic. This is configured as guidance rather than a script, so the AI can adapt naturally.\n\n**Key Questions**\nThese are the main questions for the interview. Each question has a specific type — open-ended, scale, single choice, multiple choice, ranking, or yes/no — and includes probing configuration that controls how deeply the AI follows up. For a full guide to question types and their configuration, see [Structured Questions in AI Interviews](/docs/structured-questions-guide).\n\n**Topics to Explore**\nHigh-level themes the AI should cover during the interview, giving it flexibility to follow interesting threads within the defined scope.\n\n**Behaviors to Probe**\nSpecific actions or decisions the AI should dig into when participants mention them — for example, \"probe the decision-making process when participants describe switching tools.\"\n\n**Guardrails**\nInstructions about what the AI should avoid — topics to steer away from, types of questions not to ask, or boundaries to respect during the conversation.\n\n**Closing Approach**\nHow the AI wraps up the interview — giving participants a chance to share anything that wasn't covered and ending the conversation gracefully.\n\n**What to look for in the interview plan:**\n- Do the questions flow in a logical order? Would they feel natural in a real conversation?\n- Are there too many questions? A 20-minute interview typically supports 5 to 8 key questions with probes. More than that, and you risk rushing through important topics.\n- Is there a good mix of open-ended and structured question types? Open-ended questions build rapport and depth; structured questions anchor key metrics.\n- Do the probes dig into the right things? Probes should uncover motivations, emotions, and specifics — not just ask for more detail.\n- Is the language appropriate for your participants? A study about developer tooling should sound different from one about consumer health apps.\n\n### Interview Mode\n\nThe interview plan also specifies the interaction mode:\n\n- **Structured** — the AI follows questions closely in order\n- **Exploratory** — the AI follows interesting threads freely within the defined topics\n- **Hybrid** — the AI starts structured and goes exploratory when it hits something interesting (this is the default)\n\nThe mode, along with estimated duration, shapes how the AI paces the conversation.\n\n## How to Read the Brief Critically\n\nReading a research brief is a skill. Here are some questions to ask yourself as you review:\n\n**The \"So What\" Test:** For each interview question, ask yourself: \"If a participant answers this, will the answer help me make a decision?\" If not, the question might not be pulling its weight.\n\n**The Flow Test:** Read the questions in order, out loud if possible. Do they feel like a natural conversation, or do they jump around? Abrupt topic changes can make participants feel disoriented.\n\n**The Bias Check:** Look for questions that assume a particular answer. \"What frustrated you about the onboarding?\" assumes frustration existed. \"How would you describe your onboarding experience?\" is more neutral.\n\n**The Length Check:** Count your key questions and estimate how long each one might take, including follow-ups. If your math says the interview will run over your target time, trim before you publish.\n\n**The Participant Check:** Imagine a specific person from your target participant description reading each question. Would they understand it? Would they have something meaningful to say? If the language is too jargony or the questions are too abstract, simplify.\n\n**The Quantitative Check:** Review your structured questions. Are you capturing the metrics you need for reporting? Do scale questions have appropriate ranges and meaningful endpoint labels? Will your structured data answer the quantitative questions your stakeholders care about?\n\n## Making Changes\n\nIf anything in the brief needs adjustment, you have two options:\n\n1. **Chat with the AI Consultant** — describe what you want to change, and it will update the brief. This is great for significant structural changes or when you want suggestions.\n2. **Edit the brief directly** — use the structured editor to modify any section. The editor is organized into tabs (Problem, Participant, Approach, Questions) for easy navigation. See our guide on [editing the brief manually](/docs/editing-the-brief-manually) for details.\n\nBoth approaches update the same document, so you can switch between them freely.\n\n## When the Brief Is Ready\n\nYour brief is ready to publish when:\n\n- The problem statement clearly captures your research question\n- The methodology matches the type of insight you need\n- The target participant is specific and recruitable based on behavioral criteria\n- The interview questions flow naturally and avoid bias\n- The mix of open-ended and structured questions captures both qualitative depth and quantitative data\n- The number of questions fits your target interview length\n- You've read through the whole thing at least once and feel confident about it\n\nOnce you're satisfied, head over to [Publishing Your Study](/docs/publishing-your-study) to make it live.\n\n## Related Articles\n\n- [Editing the Brief Manually](/docs/editing-the-brief-manually) — making direct changes using the structured editor\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — designing quantitative questions within your study\n- [Publishing Your Study](/docs/publishing-your-study) — going live with your study\n- [Choosing a Methodology](/docs/choosing-a-methodology) — understanding the available research frameworks\n- [Writing a Research Question](/docs/writing-a-research-question) — crafting the question that drives your study","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Understanding the Research Brief — Koji Docs","metaDescription":"Learn what each section of your Koji research brief contains and how to review it before publishing your study.","keywords":["research brief","study design","interview plan","research methodology","problem statement","interview questions"],"aiSummary":"The research brief is the blueprint for your study. This guide walks through every section — problem context, methodology, target participant, and interview plan with structured questions — explaining what each contains and how to review it critically.","aiPrerequisites":["working-with-the-ai-consultant"],"aiLearningOutcomes":["Understand what each section of the research brief contains","Critically review interview questions for bias and flow","Assess whether a brief is ready to publish","Know when to edit via chat versus manual editing"],"aiDifficulty":"beginner","aiEstimatedTime":"8 min read"},{"type":"documentation","id":"df9cc751-1dbd-4045-a8a5-0fd6eb26005a","slug":"editing-the-brief-manually","title":"Editing the Brief Manually","url":"https://www.koji.so/docs/editing-the-brief-manually","summary":"Koji's structured brief editor lets you directly modify every part of your study design across four tabs: Problem, Participant, Approach, and Questions. This guide covers the structured question editor, drag-and-drop reordering, probing configuration, and a review checklist before publishing.","content":"While the [AI Consultant](/docs/working-with-the-ai-consultant) is excellent at drafting and iterating on your research brief through conversation, sometimes you know exactly what change you want to make and you'd rather just do it yourself. Koji's brief editor lets you directly modify every part of your study design through a structured interface.\n\n## When to Edit Manually vs. Chat\n\nBoth approaches update the same research brief, so choose whichever feels faster for the change you're making:\n\n| Change Type | Recommended Approach |\n|---|---|\n| Reword a single question | Manual edit |\n| Restructure the entire interview plan | Chat with AI Consultant |\n| Fix a typo or adjust phrasing | Manual edit |\n| Change the methodology | Chat with AI Consultant |\n| Add or remove a specific probe | Manual edit |\n| Rethink the target participant entirely | Chat with AI Consultant |\n| Reorder questions | Manual edit (drag and drop) |\n| Explore whether a question is biased | Chat with AI Consultant |\n| Change a question type (e.g., open-ended to scale) | Manual edit |\n| Add scale labels or choice options | Manual edit |\n\nThe general rule: if you know the exact words you want, edit manually. If you want to think through implications or get suggestions, chat with the Consultant.\n\n## Accessing the Brief Editor\n\nThe research brief is displayed in the artifact panel alongside your conversation with the AI Consultant. The editor is organized into four tabs:\n\n- **Problem** — edit the problem statement, decision to inform, hypothesis, success criteria, and problem cost\n- **Participant** — define required experience, behavior of interest, relationship to problem, and screening question\n- **Approach** — select from available methodologies and configure the interview mode (structured, exploratory, or hybrid)\n- **Questions** — add, edit, configure, and reorder your interview questions using the structured question editor\n\nTo edit any section, click the corresponding tab and modify the fields directly. Your changes are saved automatically.\n\n## Editing the Problem Context\n\nThe Problem tab contains the fields that anchor your entire study. Changes here can have cascading effects.\n\n**Tips for editing the problem context:**\n- Keep the problem statement to one to three sentences\n- Focus on what you want to learn, not what you plan to build\n- Use the hypothesis field to state what you currently believe — the study will test it\n- The decision to inform field should name a specific business or product decision\n- After editing, scan the rest of the brief to make sure everything still aligns\n\nIf you significantly change the problem statement, consider asking the AI Consultant to review the rest of the brief for consistency. You can say something like: \"I updated the problem statement — does the rest of the brief still make sense?\"\n\n## Changing the Methodology\n\nYou can switch the methodology from the Approach tab. Select from the available methodologies — each is described in detail in our [methodology guide](/docs/choosing-a-methodology).\n\n**Important:** Changing the methodology affects how the AI interviewer conducts conversations. Different methodologies use different questioning styles, follow-up strategies, and conversational approaches. If you switch methodologies, review your interview questions to make sure they still fit the new framework.\n\nFor significant methodology changes, chatting with the AI Consultant is often more efficient, since it can regenerate appropriate questions for the new approach.\n\n## Working with the Structured Question Editor\n\nThe Questions tab is where most manual editing happens. Unlike a plain text editor, the question editor provides a structured interface for each question:\n\n### Adding Questions\n\nClick the \"Add Question\" button to create a new question. You'll set:\n\n1. **Question text** — the actual question the AI will ask\n2. **Question type** — select from the dropdown: open-ended, scale, single choice, multiple choice, ranking, or yes/no\n3. **Type-specific configuration** — depending on the type you chose:\n   - **Scale:** set min/max values and endpoint labels (e.g., \"Very Unlikely\" to \"Very Likely\")\n   - **Single/Multiple Choice:** add your option list, optionally enable \"Allow Other\" for free-text responses\n   - **Ranking:** add items participants will order by preference\n4. **Probing configuration** — set the maximum follow-up depth (0–3) and add specific probing instructions\n\nFor a full guide to question types and their configurations, see [Structured Questions in AI Interviews](/docs/structured-questions-guide).\n\n### Editing Existing Questions\n\nClick on any question to expand its editing panel. You can modify the text, change the type, adjust configuration, or update probing settings. Changing a question's type will reset its type-specific configuration, so set the type first before configuring options.\n\n### Rewording Questions\n\nSmall wording changes can make a big difference in how participants respond.\n\n**Before:** \"What challenges do you face with the product?\"\n**After:** \"Walk me through a recent situation where you ran into difficulty while using the product.\"\n\nThe second version is more specific, grounded in real experience, and harder to answer with a vague generalization.\n\n### Reordering Questions\n\nDrag and drop questions into the sequence that makes sense for the conversation flow. A well-ordered interview:\n\n1. **Starts with easy, broad questions** that get the participant talking comfortably\n2. **Moves into the core topic** once rapport is established\n3. **Goes deeper** with specific, potentially sensitive questions in the middle\n4. **Places structured questions** (scales, choices) after open-ended discovery, once the participant has context\n5. **Ends with reflective or forward-looking questions** that leave the participant feeling valued\n\nAfter reordering, read through the whole plan to check that transitions between questions still feel natural.\n\n### Configuring Probing\n\nEach question has a probing configuration that controls how the AI follows up:\n\n- **Max follow-ups (0–3)** — how many probing questions the AI can ask after the initial answer. Set to 0 for no probing, or up to 3 for deep exploration.\n- **Probing instructions** — specific guidance for the AI, like \"If the participant gives a low score, ask what would need to change.\"\n- **Anchor probing (scale questions only)** — after a rating, the AI asks something like \"You said 7 — what would need to change to make it a 9?\" This is powerful for understanding the gap between current and ideal experience.\n\n### Adding or Removing Questions\n\n**Things to consider when adding questions:**\n- Will there be enough time to cover this in the interview?\n- Does it contribute directly to answering your research question?\n- Is it distinct from other questions, or does it overlap?\n- Would a structured type (scale, choice) or open-ended approach work better here?\n\n**Things to consider when removing questions:**\n- Is the question truly unnecessary, or is it just phrased poorly? Sometimes rewording is better than removing.\n- Does removing it leave a gap in the conversation flow?\n\n## Adjusting the Target Participant\n\nThe Participant tab describes who your study is designed for. When editing this section:\n\n- Focus on behavioral criteria — what participants have experienced or done — rather than demographic categories\n- **Required Experience** should describe specific past actions (e.g., \"has evaluated at least two CRM tools in the past year\")\n- **Behavior of Interest** should name the specific behavior you're studying\n- **Screening Question** helps verify participant fit at the start of the interview\n- Consider whether your participant description is specific enough to recruit against\n\n## After Editing: A Quick Review Checklist\n\nAfter any manual edits, run through this checklist before [publishing your study](/docs/publishing-your-study):\n\n- [ ] Does the problem statement still match the rest of the brief?\n- [ ] Are all questions open-ended and non-leading (unless intentionally structured)?\n- [ ] Do questions flow naturally from one to the next?\n- [ ] Is the total number of questions realistic for your target interview length?\n- [ ] Do probes add depth rather than just asking for \"more\"?\n- [ ] Are structured questions configured correctly (scales have labels, choices have options)?\n- [ ] Is the target participant specific enough to recruit?\n- [ ] Does the methodology match the conversational style of your questions?\n\n## Switching Between Manual Editing and Chat\n\nRemember, you can freely switch between editing manually and chatting with the AI Consultant. They operate on the same document. A common workflow is:\n\n1. Use the AI Consultant for the initial draft and major structural decisions\n2. Switch to manual editing for precise wording adjustments and question configuration\n3. Return to the Consultant if you're unsure about a change and want a second opinion\n\nFor a full walkthrough of what each section contains, see [Understanding the Research Brief](/docs/understanding-the-research-brief).\n\n## Related Articles\n\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — what each section of the brief contains\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — the full guide to question types, configuration, and probing\n- [Working with the AI Consultant](/docs/working-with-the-ai-consultant) — collaborating with the AI on study design\n- [Publishing Your Study](/docs/publishing-your-study) — going live with your study\n- [Choosing a Methodology](/docs/choosing-a-methodology) — understanding available research frameworks","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Editing the Brief Manually — Koji Docs","metaDescription":"Learn how to directly edit your Koji research brief to fine-tune questions, methodology, audience, and interview flow.","keywords":["edit brief","interview questions","research brief","study design","manual editing","probes"],"aiSummary":"Koji's structured brief editor lets you directly modify every part of your study design across four tabs: Problem, Participant, Approach, and Questions. This guide covers the structured question editor, drag-and-drop reordering, probing configuration, and a review checklist before publishing.","aiPrerequisites":["understanding-the-research-brief"],"aiLearningOutcomes":["Edit any section of the research brief directly","Fine-tune interview questions, probes, and ordering","Know when to edit manually versus chatting with the AI Consultant","Review edits using a pre-publish checklist"],"aiDifficulty":"beginner","aiEstimatedTime":"7 min read"},{"type":"documentation","id":"5e450aaa-5caa-4b72-a18b-0e9cac51cbd4","slug":"uploading-context-documents","title":"Uploading Context Documents","url":"https://www.koji.so/docs/uploading-context-documents","summary":"Context documents give Koji's AI background knowledge about your product, domain, and research landscape. This guide covers supported formats, what to upload, best practices, and how documents improve study design, structured question configuration, and interviews.","content":"Context documents give Koji's AI a richer understanding of your research landscape — they're background materials that help the AI Consultant design more informed studies and help the AI interviewer ask more relevant follow-up questions. Think of them as briefing materials you'd hand to a human research assistant before they start working on your project.\n\n## Why Context Documents Matter\n\nWithout context documents, Koji works with what you tell it in the chat conversation. That's often enough for straightforward studies. But when your research lives within a specific domain — a particular product, a niche market, a complex organizational structure — extra context can make a meaningful difference.\n\nHere's what context documents help with:\n\n### Better Question Design\n\nWhen the AI Consultant has access to your product documentation, previous research, or market analysis, it can craft interview questions that reference real features, actual pain points, and genuine use cases rather than generic placeholders. This applies to both open-ended questions and [structured question types](/docs/structured-questions-guide) — context helps the AI suggest more relevant scale endpoints, choice options, and ranking items.\n\n**Without context:** \"How do you use the product in your daily work?\"\n**With product documentation as context:** \"How does the workflow automation feature fit into your team's daily operations?\"\n\n### More Relevant Probes\n\nDuring interviews, the AI interviewer uses context to ask smarter follow-up questions. If it knows your product has a specific feature or your industry has a particular challenge, it can probe more precisely when a participant mentions something related.\n\n### Accurate Terminology\n\nEvery domain has its own vocabulary. Context documents help the AI learn and use the right terms — your product's feature names, your industry's jargon, your organization's internal terminology. This makes conversations feel more natural and professional to participants.\n\n### Better Structured Question Design\n\nWhen the AI Consultant has access to your existing data — previous NPS scores, known feature lists, competitive landscape — it can design more targeted [structured questions](/docs/structured-questions-guide). For example, context about your product's feature set helps the AI suggest accurate options for multiple-choice or ranking questions, rather than generic placeholders.\n\n## Supported File Formats\n\nKoji accepts the following file types as context documents:\n\n| Format | Extension | Best For |\n|---|---|---|\n| PDF | .pdf | Research reports, whitepapers, product specs |\n| Plain Text | .txt | Quick notes, raw content, transcripts |\n| Word Document | .docx | Formatted reports, proposals, briefs |\n| JSON | .json | Structured data, API documentation, survey results |\n| Markdown | .md | Technical documentation, README files, knowledge bases |\n\nYou can upload up to **5 files** per study. This limit keeps the AI focused — too many documents can dilute the signal. Choose the most relevant materials rather than uploading everything you have.\n\n## What to Upload\n\nNot all documents are equally useful. Here are the types that tend to have the biggest impact:\n\n### High-Value Context Documents\n\n- **Previous research findings** — summaries or reports from past studies on related topics. These help the AI build on what you already know rather than retreading old ground.\n- **Product documentation** — feature descriptions, user guides, or product specs. These help the AI understand what participants are using and what language to use when discussing it.\n- **Customer feedback summaries** — compiled feedback from support tickets, NPS surveys, or review platforms. These surface recurring themes that the AI can probe during interviews.\n- **Competitive analysis** — information about alternatives in your market. This helps the AI understand the landscape participants are navigating.\n- **Internal strategy documents** — product roadmaps, quarterly plans, or team goals. These help align interview questions with strategic priorities.\n\n### Less Useful Documents\n\n- **Raw data dumps** — large spreadsheets or databases without context. The AI can't easily extract relevant information from unstructured raw data.\n- **Entire codebases** — technical implementation details rarely help with qualitative research design.\n- **Legal or compliance documents** — unless your study is specifically about legal compliance, these add noise without signal.\n- **Marketing materials** — these tend to describe ideal states rather than reality, which can actually bias the AI's understanding.\n\n## How to Upload\n\nAdding context documents to your study is straightforward:\n\n1. Open your study in the design workspace\n2. Look for the context documents area in the study interface\n3. Click to upload or drag and drop your files\n4. Your files are processed and made available to the AI\n\nYou can upload documents at any point during the design process — before you start chatting with the AI Consultant, during the conversation, or after the [research brief](/docs/understanding-the-research-brief) is drafted.\n\n**Tip:** Uploading documents before your first message to the AI Consultant tends to produce the best results, since the Consultant can factor in the context from the very beginning.\n\n## How Context Documents Are Used\n\nOnce uploaded, context documents influence two stages of your study:\n\n### During Study Design\n\nThe AI Consultant draws on your context documents when suggesting methodologies, drafting interview questions, defining your target participant, and recommending [structured question types](/docs/structured-questions-guide). You'll notice more specific, grounded suggestions compared to studies without context.\n\n### During Interviews\n\nThe AI interviewer has access to context documents while conducting conversations with participants. This means it can:\n\n- Recognize when a participant mentions something related to your product or domain\n- Ask follow-up questions that reference specific features or concepts\n- Use appropriate terminology that matches your participants' vocabulary\n- Avoid asking questions about things that are already well-documented\n\nContext documents don't change the structure of your interview — the questions in your [research brief](/docs/understanding-the-research-brief) remain the guide. But they enrich the AI's ability to improvise meaningfully within that structure.\n\n## Best Practices\n\n### Be Selective\n\nWith a limit of five files, prioritize quality over quantity. One well-written research summary is more useful than five loosely related documents.\n\n### Keep Documents Focused\n\nIf you have a 50-page report but only the executive summary and findings sections are relevant, consider extracting those sections into a separate document before uploading.\n\n### Update When Needed\n\nIf your context changes — for example, a new product feature launches between study design and interviewing — you can update your documents before publishing. The AI will use whatever is current at the time of publication.\n\n### Consider Participant Knowledge\n\nUpload documents that reflect what your participants would know about, not just what you know. If you're interviewing external customers, product documentation they might have seen is relevant. Internal engineering specs they'd never encounter are less so.\n\n## Context Documents and Study Quality\n\nThink of context documents as a multiplier. A well-designed study without context produces good results. The same study with relevant context produces great results. The context doesn't replace good [research question writing](/docs/writing-a-research-question) or thoughtful [work with the AI Consultant](/docs/working-with-the-ai-consultant) — it enhances both.\n\nIf you're new to Koji, it's perfectly fine to start without context documents and add them as you become more comfortable with the platform. Your first study doesn't need to be perfect — it needs to teach you something useful.\n\n## Related Articles\n\n- [Working with the AI Consultant](/docs/working-with-the-ai-consultant) — how context documents enhance the design conversation\n- [Understanding the Research Brief](/docs/understanding-the-research-brief) — what the brief contains and how to review it\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide) — how context helps design better structured questions\n- [Editing the Brief Manually](/docs/editing-the-brief-manually) — making direct changes to the brief","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Uploading Context Documents — Koji Docs","metaDescription":"Learn how to add context files to your Koji study for smarter AI-generated questions and more relevant interviews.","keywords":["context documents","file upload","study design","background materials","AI context","research materials"],"aiSummary":"Context documents give Koji's AI background knowledge about your product, domain, and research landscape. This guide covers supported formats, what to upload, best practices, and how documents improve study design, structured question configuration, and interviews.","aiPrerequisites":["creating-your-first-study"],"aiLearningOutcomes":["Upload context documents to improve study design and interviews","Choose the most impactful files from your available materials","Understand how context documents influence the AI Consultant and AI interviewer","Follow best practices for document selection and preparation"],"aiDifficulty":"beginner","aiEstimatedTime":"6 min read"},{"type":"documentation","id":"8fba1a52-f7b3-49f6-94f9-7405735a0ac2","slug":"choosing-a-methodology","title":"Choosing a Methodology","url":"https://www.koji.so/docs/choosing-a-methodology","summary":"Koji supports eight research methodologies, each shaping how interviews are conducted and what insights emerge. This guide describes every methodology, when to use it, and how it affects the AI interviewer's behavior.","content":"Your methodology is the lens through which your study sees the world — it shapes how questions are asked, what the AI interviewer listens for, and how your findings come together. Koji supports eight methodologies, each designed for specific research situations. Choosing the right one is one of the highest-leverage decisions you'll make in study design.\n\n## How Methodology Affects Your Study\n\nYour methodology does more than label your study. It actively influences:\n\n- **Question style** — some methodologies favor broad, exploratory questions while others use structured, specific ones\n- **Follow-up behavior** — the AI interviewer adapts its probing strategy based on the methodology\n- **Conversational tone** — some approaches are deliberately casual while others are more structured\n- **What counts as a good answer** — different methodologies look for different kinds of insight\n\nWhen you [work with the AI Consultant](/docs/working-with-the-ai-consultant), it will suggest a methodology based on your [research question](/docs/writing-a-research-question). You can always ask it to explain its reasoning or explore alternatives.\n\n## The Methodologies\n\n### Mom Test\n\n**Best for:** Validating product ideas, testing assumptions, and understanding real customer behavior without leading them toward the answer you want to hear.\n\n**The core principle:** People are unreliable when predicting their own future behavior, especially when they want to be nice to you. The Mom Test methodology is built around this insight — it steers conversations toward concrete past experiences and away from hypothetical opinions.\n\n**How it shapes interviews:**\n- Questions focus on what participants have actually done, not what they say they would do\n- The AI interviewer avoids revealing the idea being tested, to prevent politeness bias\n- Follow-ups dig into specific past events: \"Tell me about the last time you...\" rather than \"Would you ever...\"\n- Compliments and vague positivity are gently redirected toward specifics\n\n**Real-world example:** You're building a meal planning app. Instead of asking \"Would you use an app that plans your meals?\" (which almost everyone says yes to), a Mom Test interview asks \"Walk me through how you decided what to cook for dinner last Tuesday\" and \"What did you actually do the last time you felt stressed about meal planning?\" The answers reveal whether the problem is real and painful enough to solve.\n\n**Use this when:** You have an idea and want to validate it honestly, you're in early-stage product development, or you suspect your team is suffering from confirmation bias.\n\n### Jobs to Be Done (JTBD)\n\n**Best for:** Understanding why customers make the choices they make, uncovering the deeper motivations behind behavior, and discovering opportunities for innovation.\n\n**The core principle:** People don't buy products — they hire them to do a job. A JTBD interview uncovers what that job is, what circumstances trigger it, and what tradeoffs people make when choosing a solution.\n\n**How it shapes interviews:**\n- Questions trace the timeline of a decision: from first thought to final choice\n- The AI interviewer probes for emotional and social dimensions, not just functional needs\n- Follow-ups explore the \"forces\" at play: what pushed them away from the old solution and pulled them toward the new one\n- Conversations often spend significant time on the moment of switching or deciding\n\n**Real-world example:** You're researching why customers switch from spreadsheets to your project management tool. A JTBD interview traces the journey: \"Take me back to the moment when you first thought something needed to change...\" and \"What was the final straw that made you actually start looking for alternatives?\" You discover that the trigger wasn't spreadsheet limitations — it was a new team member who couldn't find anything.\n\n**Use this when:** You want to understand purchasing or adoption decisions, you're looking for innovation opportunities, or you need to understand competitive dynamics from the customer's perspective.\n\n### Customer Discovery\n\n**Best for:** Exploring a problem space before building a solution, understanding whether a problem exists and who experiences it, and gathering evidence to inform product strategy.\n\n**How it shapes interviews:**\n- Questions are deliberately broad and exploratory in the early stages\n- The AI interviewer follows the participant's lead, spending more time on topics that seem emotionally charged or practically important\n- Follow-ups test whether stated problems are significant enough to drive action\n- Conversations avoid solution-space discussions unless the participant brings them up\n\n**Real-world example:** You're exploring whether small business owners struggle with hiring. Your interview starts with \"Walk me through how you found and hired your most recent employee\" and follows the story wherever it goes. You discover that the problem isn't finding candidates — it's evaluating them without an HR team.\n\n**Use this when:** You're in the earliest stages of a project, you're entering a market you don't know well, or you want to validate that a problem is worth solving.\n\n### User Interview\n\n**Best for:** General-purpose qualitative research, understanding user experiences, exploring workflows, and gathering rich, open-ended feedback.\n\n**How it shapes interviews:**\n- Balanced between structure and flexibility\n- Questions are open-ended and cover a range of topics related to your research question\n- The AI interviewer maintains a natural conversational flow while ensuring key topics are covered\n- Follow-ups adapt to whatever the participant shares, going deeper on interesting threads\n\n**Real-world example:** You want to understand how marketing teams use your analytics dashboard. The interview covers their daily workflow, what metrics matter most, how they share findings with stakeholders, and where they feel the tool helps or falls short.\n\n**Use this when:** Your research question doesn't neatly fit one of the specialized methodologies, you want a versatile all-purpose approach, or you're conducting exploratory research within a known domain.\n\n### Usability Testing\n\n**Best for:** Evaluating how people interact with a specific product, feature, or prototype, identifying friction points, and understanding mental models.\n\n**How it shapes interviews:**\n- Questions center on specific interactions and experiences with a product or feature\n- The AI interviewer asks participants to recall or describe concrete usage scenarios\n- Follow-ups focus on moments of confusion, delight, or frustration\n- Conversations emphasize what happened and what participants expected to happen\n\n**Real-world example:** You've redesigned your checkout flow and want to know how it feels. The interview asks participants to describe their most recent purchase on your site, what they expected at each step, and where anything surprised or confused them.\n\n**Use this when:** You've built or changed something specific and want to evaluate the user experience, you're comparing design alternatives, or you're investigating why a feature has low adoption.\n\n### Employee Engagement\n\n**Best for:** Understanding workplace culture, team dynamics, job satisfaction, and organizational challenges from the employee perspective.\n\n**How it shapes interviews:**\n- Questions are designed to build psychological safety, encouraging honest responses\n- The AI interviewer uses a warmer, more empathetic tone\n- Follow-ups explore both positive and negative aspects of the work experience\n- Conversations cover themes like belonging, growth, recognition, and workload\n\n**Real-world example:** You're investigating why engineering retention has dropped. Interviews explore what engineers enjoy about their work, what frustrates them, how they feel about career growth opportunities, and what would make them consider leaving or staying.\n\n**Use this when:** You're conducting internal research, you want to understand employee experience, or you're investigating organizational culture topics.\n\n### Market Research\n\n**Best for:** Understanding market dynamics, customer segments, buying behaviors, and competitive positioning from the customer's perspective.\n\n**How it shapes interviews:**\n- Questions cover awareness, evaluation, purchase, and post-purchase experiences\n- The AI interviewer explores how participants discover, compare, and choose solutions\n- Follow-ups probe brand perception, value assessment, and switching behavior\n- Conversations map the broader decision-making ecosystem\n\n**Real-world example:** You're entering the small business accounting market and want to understand how owners choose their tools. Interviews explore how they first realized they needed accounting software, where they looked for options, what criteria mattered most, and how satisfied they are with their current choice.\n\n**Use this when:** You need to understand market positioning, you're exploring a new market opportunity, or you want to understand how customers perceive your competitive landscape.\n\n### Custom\n\n**Best for:** Research that doesn't fit neatly into any of the above categories, or studies where you want full control over the conversational approach.\n\n**How it shapes interviews:**\n- You define the approach entirely through your interview questions and instructions\n- The AI interviewer follows your brief closely without imposing a particular framework\n- Maximum flexibility in question style, tone, and follow-up behavior\n\n**Use this when:** You have experience designing qualitative studies and want to implement a specific approach, your research question requires a unique methodology, or you want to combine elements from multiple frameworks.\n\n## Choosing the Right Methodology\n\nIf you're not sure which methodology to pick, start with your [research question](/docs/writing-a-research-question) and ask:\n\n1. **Am I testing an idea?** Start with Mom Test.\n2. **Am I trying to understand a decision?** Start with JTBD.\n3. **Am I exploring a problem I don't fully understand?** Start with Customer Discovery.\n4. **Am I evaluating something I've built?** Start with Usability Testing.\n5. **Am I researching employee experience?** Start with Employee Engagement.\n6. **Am I trying to understand a market?** Start with Market Research.\n7. **None of the above?** Start with User Interview or Custom.\n\nYou can always discuss methodology options with the [AI Consultant](/docs/working-with-the-ai-consultant) — it's good at recommending approaches based on your specific situation. And remember, you can view and adjust the methodology in your [research brief](/docs/understanding-the-research-brief) at any time before publishing.\n\nOnce you've chosen your methodology and your brief is ready, head to [Creating Your First Study](/docs/creating-your-first-study) to bring it all together.","category":"Study Design","lastModified":"2026-04-25T19:14:08.521275+00:00","metaTitle":"Choosing a Methodology — Koji Docs","metaDescription":"Explore every research methodology Koji supports and learn which one fits your study goals.","keywords":["research methodology","Mom Test","Jobs to Be Done","JTBD","customer discovery","user interview","usability testing","employee engagement","market research"],"aiSummary":"Koji supports eight research methodologies, each shaping how interviews are conducted and what insights emerge. This guide describes every methodology, when to use it, and how it affects the AI interviewer's behavior.","aiPrerequisites":["writing-a-research-question"],"aiLearningOutcomes":["Understand what each Koji methodology does and how it shapes interviews","Choose the right methodology for your research question","Know how methodology affects question style, probing, and conversational tone","Make informed decisions about when to use specialized versus general approaches"],"aiDifficulty":"intermediate","aiEstimatedTime":"10 min read"}],"pagination":{"total":403,"returned":100,"offset":0}}