{"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":"2026-07-05T12:19:50.399Z"},"content":[{"type":"documentation","id":"4a5f1477-556a-48c7-8221-a6bc93d369f7","slug":"ai-research-for-hardware-iot","title":"AI-Powered User Research for Hardware and IoT Companies","url":"https://www.koji.so/docs/ai-research-for-hardware-iot","summary":"Koji gives hardware and IoT teams continuous AI-moderated customer research across a long, high-risk development cycle: pre-tooling concept and demand testing, beta-unit feedback, out-of-box and setup research, companion-app usability, and post-purchase and returns interviews. Structured questions capture setup difficulty, feature usage, and recommendation scores as chartable data, while AI follow-up probing surfaces why devices are hard to set up or returned — closing the loop within a single sprint.","content":"## The Bottom Line\n\nHardware and IoT companies carry a risk that pure-software teams never face: once you commit to tooling, components, and a manufacturing run, mistakes are measured in months and millions, not a hotfix. Koji gives hardware and connected-device teams a continuous stream of qualitative customer signal — AI voice and text interviews that surface setup friction, unmet needs, and adoption blockers *before* they are locked into physical product. Because the interviews are AI-moderated, you can run them at every stage of a long development cycle without a research team on standby.\n\n## Why Hardware and IoT Research Is Different\n\nSoftware teams can ship, measure, and iterate weekly. Hardware and IoT teams operate under constraints that make research not optional but existential:\n\n### Irreversible commitments\nInjection-mold tooling, PCB layouts, enclosure design, and component sourcing are expensive and slow to change. A misunderstanding about how customers will actually use the device — discovered after tooling — can sink a product line. Research has to happen *early*, when it is still cheap to change your mind.\n\n### The device-plus-app problem\nModern connected products are rarely just hardware. There is a device, firmware, a companion mobile or web app, and often a cloud service. The customer experiences all of it as one product. A beautifully engineered device with a confusing pairing flow feels broken. You need research that spans the full journey — out-of-box, setup, first use, daily use, and troubleshooting — not just the industrial design.\n\n### Setup and onboarding are make-or-break\nFor connected devices, the first fifteen minutes decide everything. Pairing over Bluetooth or Wi-Fi, creating an account, granting permissions, updating firmware — every step is a chance to lose the customer. Setup friction is the single largest driver of early returns and one-star reviews in consumer IoT.\n\n### Returns are your most expensive feedback\nIn hardware, a dissatisfied customer does not just churn — they ship the product back. Return merchandise authorizations (RMAs), restocking, and reverse logistics turn a UX problem into a direct margin problem. Understanding *why* people return devices is among the highest-ROI research a hardware team can do.\n\n## Where Koji Fits in the Hardware Lifecycle\n\nKoji runs continuous AI-moderated interviews at each phase where a hardware team is exposed to risk:\n\n### 1. Pre-tooling concept and demand testing\nBefore you spend on tooling, validate that the concept solves a real problem and that customers understand the value proposition. Koji's AI interviewer walks target buyers through renders, spec sheets, or a value-prop description, probes for objections, and captures willingness-to-pay signals. This is [concept testing](/docs/concept-testing-methodology) run against real prospects in days, not the weeks a traditional panel study takes.\n\n### 2. Prototype and beta-unit feedback\nWhen you ship engineering samples or a beta batch to early users, Koji interviews every participant on a consistent protocol — first impressions, assembly, setup, and daily use — so you get comparable, structured feedback across the whole beta cohort instead of scattered Slack messages. Pair it with a [beta tester interview](/docs/beta-tester-interviews) program to keep the signal flowing through the beta.\n\n### 3. Out-of-box and setup research\nThis is the highest-leverage study for any connected device. Koji triggers an interview right after a customer completes (or abandons) setup and walks them through exactly where the flow lost them: the pairing step, the account gate, the firmware update, the permissions prompt. Because interviews are async and on-demand, you capture the experience while it is still fresh — something impossible to schedule with live moderators across time zones.\n\n### 4. Companion-app usability\nThe app is where most connected-device frustration actually lives. Koji runs [usability-testing](/docs/usability-testing-guide)-style interviews on the companion app — task success, confusion points, and feature discoverability — and links app friction back to overall product satisfaction.\n\n### 5. Post-purchase and returns interviews\nKoji can interview customers after delivery and, critically, interview people who initiated a return. A [post-purchase](/docs/post-purchase-survey-guide) conversation surfaces what the product page over-promised, what setup under-delivered, and which missing feature triggered the RMA — turning your most expensive failures into a prioritized fix list.\n\n## How Structured Questions Power Hardware Research\n\nKoji's [six structured question types](/docs/structured-questions-guide) let you mix open discovery with the quantifiable metrics hardware teams report to leadership:\n\n- **open_ended** — \"Walk me through setting up the device for the first time.\" The AI probes automatically for the exact moment friction appeared.\n- **scale** — capture setup difficulty, likelihood to recommend, or perceived build quality on a consistent 1–10 scale for trend tracking across firmware releases.\n- **single_choice** — \"Where did setup stall?\" (Pairing / Account / Firmware update / Permissions / It worked first try).\n- **multiple_choice** — \"Which features do you use weekly?\" to separate loved features from dead weight before the next hardware revision.\n- **ranking** — have customers rank candidate features for the next model so your BOM investment follows real demand.\n- **yes_no** — \"Did the device pair on the first attempt?\" as a clean, chartable success metric.\n\nEvery response is analyzed automatically and rolled into a real-time report, so a firmware or app fix can be validated against fresh customer data within the same sprint — a cadence hardware teams almost never achieve with traditional research.\n\n## A Practical Program for a Connected-Device Team\n\nA smart-home startup preparing a second-generation device runs a standing Koji program: pre-tooling concept interviews with 80 target buyers to validate the new sensor feature; a beta-unit protocol for 150 early testers focused on setup; an always-on out-of-box interview triggered after first pairing; and a returns interview for every RMA. Within one quarter the team learns that 40% of setup failures trace to a single ambiguous permissions screen in the app — a one-sprint fix that measurably drops early returns. No moderator was scheduled; the AI ran every interview, in the customer's language, on the customer's schedule.\n\n## Koji vs. Traditional Hardware Research\n\nTraditional hardware research leans on expensive in-person usability labs, slow recruited panels, and static surveys that cannot ask a follow-up question. A survey can tell you 30% of customers rate setup \"difficult\" but never *why*. Koji's AI interviewer asks the follow-up automatically, voice or text, no moderator required — combining the depth of a moderated lab session with the scale and speed of a survey. For teams shipping physical product on a clock, that combination is the difference between catching a setup problem in beta and discovering it in your return rate.\n\n## Measuring the ROI of Hardware Research\n\nHardware leaders justify research in the language of margin, not vanity metrics. Koji makes the economics legible. Track **early return rate** before and after a setup fix validated through interviews — a two-point drop on a device with meaningful reverse-logistics cost pays for a research program many times over. Track **setup success rate** (the share of customers who report pairing on the first attempt via a yes/no question) as a leading indicator that moves before returns do. Track **support-ticket deflection** by tying the themes surfaced in interviews to the tickets they prevent. Because Koji analyzes every interview automatically and refreshes a live report, you can attribute a specific firmware or app change to a measurable movement in these numbers within a single release cycle — the kind of closed-loop evidence that turns research from a cost center into a defensible line in the product budget.\n\n## Related Resources\n\n- [Structured Questions Guide](/docs/structured-questions-guide) — mix open discovery with chartable setup and satisfaction metrics\n- [Concept Testing Methodology](/docs/concept-testing-methodology) — validate a device concept before you commit to tooling\n- [Prototype Testing & Concept Validation](/docs/prototype-testing-concept-validation) — get structured feedback on engineering samples\n- [Usability Testing Guide](/docs/usability-testing-guide) — run task-based studies on the companion app\n- [Beta Tester Interviews](/docs/beta-tester-interviews) — keep consistent signal flowing through your beta cohort\n- [AI Research for SaaS](/docs/ai-research-for-saas) — the companion playbook for the software side of your product\n","category":"Use Cases","lastModified":"2026-07-04T03:18:06.810466+00:00","metaTitle":"AI User Research for Hardware & IoT Companies | Koji","metaDescription":"How hardware and IoT teams use Koji AI voice interviews to de-risk long dev cycles — pre-tooling concept tests, unboxing and setup research, companion-app usability, and returns analysis at scale.","keywords":["hardware user research","IoT customer research","connected device research","setup usability testing","unboxing research","companion app usability","hardware concept testing","product returns analysis","beta hardware feedback","IoT product management"],"aiSummary":"Koji gives hardware and IoT teams continuous AI-moderated customer research across a long, high-risk development cycle: pre-tooling concept and demand testing, beta-unit feedback, out-of-box and setup research, companion-app usability, and post-purchase and returns interviews. Structured questions capture setup difficulty, feature usage, and recommendation scores as chartable data, while AI follow-up probing surfaces why devices are hard to set up or returned — closing the loop within a single sprint.","aiPrerequisites":["Experience shipping a physical or connected product","Basic understanding of the hardware development lifecycle"],"aiLearningOutcomes":["Identify the highest-risk moments in a hardware lifecycle where research pays off","Run pre-tooling concept tests before committing to manufacturing","Design out-of-box and setup research that reduces early returns","Use structured questions to track setup and satisfaction metrics across firmware releases","Turn RMA and returns interviews into a prioritized fix list"],"aiDifficulty":"intermediate","aiEstimatedTime":"13 minutes"}],"pagination":{"total":1,"returned":1,"offset":0}}