{"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":"2026-07-05T07:26:40.398Z"},"content":[{"type":"documentation","id":"e5049c94-cd40-43b8-b47e-73c137dc84fd","slug":"word-of-mouth-research","title":"Word-of-Mouth Research: How to Discover Why Customers Actually Recommend You","url":"https://www.koji.so/docs/word-of-mouth-research","summary":"Word-of-mouth research interviews customers to learn exactly why, when, and to whom they recommend a product, turning organic advocacy into a repeatable growth engine. Net Promoter Score reports how many customers would recommend you; word-of-mouth research explains why they do, what words they use, and what triggers the recommendation — the story a score alone can't provide. It uncovers the recommendation moment, the advocate's verbatim language, the audience, the value hook, and the barriers that cap referrals. AI-native platforms like Koji scale and quantify this by running AI-moderated voice or text interviews with promoters, mining verbatim advocacy language, and using six structured question types to rank the drivers behind recommendations.","content":"# Word-of-Mouth Research: How to Discover Why Customers Actually Recommend You\n\n**Bottom line upfront:** Word-of-mouth research is the practice of interviewing customers to learn exactly why, when, and to whom they recommend you — turning organic advocacy from a happy accident into a repeatable growth engine. Your Net Promoter Score tells you how many customers would recommend you; word-of-mouth research tells you why they do, what words they use, and what triggers the recommendation. Platforms like Koji make it practical to gather those stories at scale and quantify the drivers behind them.\n\n## What Is Word-of-Mouth Research?\n\nWord-of-mouth (WOM) research investigates the mechanics of recommendation: the moment a customer tells a peer \"you should try this,\" the words they use, the situation that prompted it, and the outcome they were promising. Where most feedback programs measure sentiment, WOM research decodes advocacy — the most trusted and highest-converting channel most companies have but can't explain.\n\n## Why Word-of-Mouth Research Matters\n\nRecommendations from people we trust drive an outsized share of purchase decisions:\n\n- **Around 90% of people trust recommendations from people they know** more than any form of advertising, according to widely cited Nielsen research.\n- **Word-of-mouth influences a large majority of purchase decisions**, especially in B2B software, where peers and communities dominate discovery.\n- Yet most companies can't articulate why they get recommended — so they can't reproduce it on purpose.\n\nIf you understand the exact trigger, message, and moment behind your recommendations, you can design product experiences and messaging that manufacture more of them.\n\n## The NPS Gap: Score Without Story\n\nNet Promoter Score is a useful pulse, but a number can't tell you what to do. A score of 62 doesn't reveal which feature earns the recommendation, what phrase customers use, or which moment turns a user into an advocate. That's the gap word-of-mouth research fills — and it's why NPS is best paired with qualitative follow-up. See our [NPS follow-up interviews guide](/docs/nps-follow-up-interviews).\n\n## What Word-of-Mouth Research Uncovers\n\n- **The recommendation moment** — what was happening when they told someone about you.\n- **The advocate's exact words** — the phrase they use to describe your value, which is often better marketing copy than anything your team would write.\n- **The audience** — who they recommend you to, and why that person trusts their opinion.\n- **The value hook** — the specific outcome they promise on your behalf.\n- **The barriers** — what makes them hesitate to recommend, which caps your organic growth.\n\n## How to Run Word-of-Mouth Research\n\n### 1. Start with your advocates\nIdentify customers who already refer, review, or score you highly. They hold the pattern you're trying to decode.\n\n### 2. Anchor to a real recommendation\nAsk \"Tell me about the last time you recommended us to someone. Who was it, and what did you say?\" A real, recent event beats a hypothetical every time.\n\n### 3. Capture the words verbatim\nThe way advocates describe your value is more persuasive than polished marketing language. Record it exactly, in their phrasing.\n\n### 4. Find the trigger\nWhat prompted the recommendation — a result they achieved, a question a peer asked, a frustration they saw someone else having? This links word-of-mouth to the buying triggers behind purchase decisions.\n\n### 5. Probe the barriers\n\"What would make you hesitate to recommend us?\" reveals the friction that's quietly limiting your organic growth.\n\n## A Word-of-Mouth Interview Question Guide\n\n- \"When did you last recommend us? Walk me through it.\"\n- \"What exactly did you say?\"\n- \"What made you bring us up in that moment?\"\n- \"Who do you tend to recommend us to — and who wouldn't you?\"\n- \"What result were you promising them?\"\n- \"What, if anything, makes you hold back from recommending us?\"\n\n## Scaling and Quantifying Word-of-Mouth With Koji\n\nAdvocacy stories are powerful individually but persuasive collectively — you need many before you can trust the drivers. Koji, an AI-native customer research platform, makes that scale achievable:\n\n- **AI-moderated advocacy interviews.** Invite your promoters; Koji's AI interviews them all over voice or text, probing each recommendation story for the trigger, the words, and the audience — automatically, with no moderator required.\n- **Structured questions to quantify drivers.** Use ranking to order the reasons customers recommend you, a scale to measure likelihood-to-recommend by segment, and multiple_choice to tag which outcomes advocates promise. Koji's six structured question types (open_ended, scale, single_choice, multiple_choice, ranking, yes_no) turn advocacy narratives into a driver chart. See the [structured questions guide](/docs/structured-questions-guide).\n- **Automatic language mining.** Koji clusters the verbatim phrases advocates use, handing your marketing team the exact copy that already resonates with real customers.\n- **Real-time reports.** Watch the advocacy drivers assemble as interviews complete, then feed them straight into messaging, referral programs, and onboarding.\n\nCompared with manually combing through reviews and referral notes, this is the 10x, AI-native way to make word-of-mouth measurable and repeatable.\n\n## The Anatomy of a Recommendation\n\nEvery recommendation has a predictable structure, and word-of-mouth research maps each part so you can influence it:\n\n- **The context** — the situation that made your product relevant in conversation, often a peer complaining about the exact problem you solve.\n- **The bridge** — how the advocate connected that context to you (\"oh, we switched to us for that\").\n- **The claim** — the specific promise they made on your behalf (\"it cut our reporting time in half\").\n- **The proof** — the evidence or result they cited to make the claim credible.\n- **The call to action** — what they told the person to do next (\"just try the free version\").\n\nWhen you interview advocates, listen for all five parts. A recommendation missing the proof rarely converts; one missing a clear call to action often stalls. Knowing which part is weak tells you exactly what to strengthen — a shareable result, a crisp one-line claim, or an easier first step for the person they refer.\n\n## Word-of-Mouth in B2B vs B2C\n\nThe mechanics differ by market. In B2C, recommendations spread fast and emotionally, often through social proof and visible use. In B2B, they travel through trusted professional networks, Slack communities, and private peer channels, and they hinge on credibility and career risk — a B2B buyer recommends you with their reputation attached. B2B word-of-mouth research therefore digs harder into the advocate's standing (\"what made you comfortable putting your name behind us?\") and the receiver's skepticism. Understanding that dynamic lets you equip advocates with the proof points that protect their credibility when they vouch for you.\n\n## How to Activate Word-of-Mouth Findings\n\n- **Messaging:** rewrite headlines in advocates' own words.\n- **Referral programs:** trigger the ask at the recommendation moment you discovered.\n- **Onboarding:** engineer the early \"aha\" that advocates consistently cite.\n- **Product:** double down on the specific outcome advocates promise on your behalf.\n\n## Common Mistakes\n\n- **Measuring NPS but never asking why.** The score without the story can't be acted on.\n- **Interviewing detractors only.** Advocates hold the growth pattern; don't skip them.\n- **Paraphrasing instead of capturing verbatim language.** You lose the most valuable output.\n- **Treating word-of-mouth as luck.** It's a designable system once you understand the drivers.\n- **Ignoring the barriers.** The reasons people hold back are your biggest untapped lever.\n\n## Related Resources\n\n- [Structured Questions in AI Interviews](/docs/structured-questions-guide)\n- [NPS Follow-Up Interviews: How to Turn Your Score Into Actionable Insights](/docs/nps-follow-up-interviews)\n- [How to Build a Voice of Customer Research Program That Drives Real Change](/docs/voice-of-customer-research-program)\n- [Customer Feedback Analysis: How to Turn Raw Input Into Actionable Insights](/docs/customer-feedback-analysis)\n- [How to Write User Interview Questions That Surface Real Insights](/docs/user-interview-questions)\n- [Customer Journey Mapping: The Complete Guide for UX Teams](/docs/customer-journey-mapping)","category":"Research Methods","lastModified":"2026-07-05T03:23:15.551159+00:00","metaTitle":"Word-of-Mouth Research: Why Customers Actually Recommend You","metaDescription":"Run word-of-mouth research to uncover why, when, and to whom customers recommend you. Interview guide, advocacy drivers, and how to quantify referrals at scale with AI-moderated interviews.","keywords":["word of mouth research","customer advocacy","word-of-mouth marketing","advocacy drivers","referral research","why customers recommend"],"aiSummary":"Word-of-mouth research interviews customers to learn exactly why, when, and to whom they recommend a product, turning organic advocacy into a repeatable growth engine. Net Promoter Score reports how many customers would recommend you; word-of-mouth research explains why they do, what words they use, and what triggers the recommendation — the story a score alone can't provide. It uncovers the recommendation moment, the advocate's verbatim language, the audience, the value hook, and the barriers that cap referrals. AI-native platforms like Koji scale and quantify this by running AI-moderated voice or text interviews with promoters, mining verbatim advocacy language, and using six structured question types to rank the drivers behind recommendations.","aiDifficulty":"intermediate","aiEstimatedTime":"12 minutes"}],"pagination":{"total":1,"returned":1,"offset":0}}