Product & UX research survey templates

Measures perceived website quality across the four standard SUPR-Q factors — usability, trust/credibility, appearance, and loyalty — plus an NPS-style likelihood-to-recommend score. Built for UX and product teams who want a repeatable internal quality read (this template does not include or claim external benchmark norms). The AI follow-up digs into the specific moment behind the respondent's lowest-scoring factor.

Measures how easily shoppers find products, move through checkout, and trust your site with their payment details — with an AI follow-up that reconstructs the exact moment shoppers got stuck or almost abandoned their cart, rather than just a satisfaction score.

Captures a complete brief for an internal or client graphic design request — project type, goal, audience, brand constraints, and priority trade-offs — so designers stop guessing what 'good' looks like. An AI follow-up interview digs into the real goal behind the ask and surfaces conflicting stakeholder expectations before work begins.

Evaluates how people actually use an interior design planning tool or app — from room visualization to furniture shopping — and where they get stuck. Closed questions surface satisfaction and feature priorities; an AI follow-up interview digs into the specific moment the experience broke down, giving product teams a concrete story instead of a vague complaint.

Captures how people actually use your product or service, which features matter most, and where the experience breaks down — with an AI follow-up that digs into the specific moment a respondent got stuck or frustrated. Built for product, UX, and service design teams shaping a roadmap.

Measures how easily shoppers scan, pay, and recover from errors at self-service checkout kiosks, and prioritizes which fixes matter most. An AI follow-up interview reconstructs exactly what happened during a recent frustrating attempt instead of relying on vague complaints. Built for retail UX and store operations teams.

A short on-site intercept that profiles who is visiting your website, why they came, and whether they accomplished their goal — paired with an AI follow-up that reconstructs the actual task a visitor was trying to complete and where it broke down. Built for product and UX teams who need more than traffic analytics to understand visitor intent.

Measures whether visitors can find, understand, and trust the content on your website — covering findability, accuracy, completeness, and currency — for product, content, and UX teams auditing site content. An AI follow-up interview reconstructs what actually happened when a respondent hit a dead end, instead of relying on vague satisfaction ratings.

Measures how easily visitors find music, artists, tickets, or content on your website, where they hit friction, and what improvements matter most. Built for music platforms, label sites, and artist hubs, with an AI follow-up interview that reconstructs exactly where a visit broke down instead of relying on vague satisfaction scores.

Measures how satisfied shoppers are with a retailer's website — from site speed and navigation to checkout and security trust — and where friction costs sales. Built for e-commerce and CX teams benchmarking site performance, with an AI follow-up that digs into the specific moment a shopper's last visit went right or wrong.

Captures what visitors came to your site to do, whether they succeeded, and where they got stuck — with an AI follow-up that reconstructs the actual browsing journey behind any failed or frustrating visit. Built for product, UX, and marketing teams who want more than a bounce-rate number.

Measures how well a makeup product performs in real routines — application, wear time, and repurchase intent — plus what actually drives the buy decision. An AI follow-up interview digs into the specific moment a product over- or under-delivered, surfacing details closed questions miss.

A team health survey for product, design, and engineering members that measures process clarity, cross-functional collaboration, time allocation, and the biggest blockers to shipping — with an AI follow-up that digs into a real recent example behind the top-ranked blocker instead of settling for a vague complaint.

Captures a specific feature request in the requester's own words, gauges how urgent and impactful it would be, and benchmarks it against other candidate ideas using a best-worst trade-off exercise. Built for product teams triaging inbound requests, with an AI follow-up that reconstructs the exact workflow the feature would unblock and what the person does today without it.

Captures how easily visitors find, use, and hear back from your website's contact channels — form, email, chat, or phone — with an AI follow-up that digs into the specific moment things got confusing or slow.

Measures how easily visitors navigate your site, how the visual design lands, and which design elements most need work — with an AI follow-up that reconstructs the exact moment a visitor got confused or stuck instead of just a satisfaction score. Built for product, UX, and marketing teams evaluating a live site or redesign.

A short on-site pop-up survey that captures why a visitor came, whether they accomplished their task, and where they hit friction — with an AI follow-up that reconstructs the exact moment things went wrong instead of relying on a vague satisfaction score. Built for UX and product teams running always-on site feedback.

Measures how easily visitors accomplish what they came to do on your website, how the site rates on speed, navigation, design, and trust, and how likely they are to recommend it — with an AI follow-up that digs into the exact moment things got confusing or broke down. Built for product, UX, and marketing teams running periodic site health checks.

Measures how satisfied users are with your app, where they hit friction, and which improvements matter most — for product and mobile teams tracking release health. An AI follow-up interview digs into the story behind each user's satisfaction score, surfacing the specific moment or feature that shaped it.

Captures how satisfied customers are with a product, which attributes matter most, and where the experience falls short — for teams tracking product quality, value, and repurchase intent. An AI follow-up interview digs into the real story behind each score instead of a generic star rating.

Identifies what actually pushes users toward 5-star versus 1-star app store reviews — crashes, speed, ads, pricing, support — and what specifically would change their rating. Built for product and growth teams; the AI follow-up reconstructs the real incident behind each respondent's rating instead of generic complaints.

Captures how players actually experience difficulty tuning, progression pacing, monetization fairness, and moment-to-moment session enjoyment. Built for game designers and live-ops teams tracking post-launch sentiment, with an AI follow-up that digs into the specific moment a player felt cheated by difficulty or monetization instead of a generic complaint.

Tests whether prospects can correctly interpret your plan names, pick the tier that actually fits them, and understand what changes when they upgrade. Built for pricing and product teams evaluating a new plan lineup before launch, with an AI follow-up that digs into exactly which name or feature line caused hesitation or a wrong guess.

Structured stakeholder or customer feedback on a design, mockup, or creative asset: first impressions, a click on what draws attention, ratings against the brief, and file upload for marked-up versions. The AI interviewer converts 'I just don't like it' into usable direction.

A self-serve usability session: participants share their screen, complete guided tasks on your website or prototype while thinking aloud, and the AI moderator watches, prompts, and probes — then debriefs them. You get recordings, transcripts, and task-level analysis without scheduling a single session.

The standard 10-item SUS questionnaire, scored 0–100 and benchmarkable against decades of published research — with correct alternating positive/negative wording preserved. An AI debrief interview follows the score, so you learn what to fix, not just where you stand.

Find your biggest opportunities by measuring the same attributes twice — how much each matters, and how well you deliver it today. The gaps become your priority list, and slider matrices keep it fast enough that people actually finish. AI follow-ups dig into the biggest gap each respondent reports.

Where do people click first when given a task on your design? First-click success is one of the strongest predictors of task completion. Upload your screenshot, set the task, and let the AI interviewer ask what they expected to happen — turning a click map into a usability diagnosis.

An open-ended card sorting study for navigation and information architecture: participants group your content into categories that make sense to them, then an AI interviewer probes the logic behind their groupings — the part a drag-and-drop grid alone never captures.

Force real trade-offs with best-worst scaling: respondents pick the most and least valuable feature in rotating sets, then an AI interviewer probes the reasoning behind their top pick. Produces a true preference ranking instead of everything-is-important rating inflation.

Diagnoses why power users complete or abandon purchases, benchmarks competitor performance across key dimensions, and surfaces priority product gaps to guide retention and conversion strategy.

Measures mobile commerce app usability across navigation, task ease, and checkout experience to identify friction points and prioritize UX improvements. Designed for current app users who have shopped within the past 30 days.

Assesses User Acceptance Testing plan quality, feedback loop efficiency, and release confidence from the perspective of UAT participants. Designed for QA, product, and engineering teams seeking to diagnose process gaps and prioritize improvements.

Measures how intuitively users locate key tasks within a website's information architecture. Use this to validate navigation labels, identify findability gaps, and prioritize structural improvements.

Measures plan-change intent, upgrade/downgrade drivers, and migration friction among active SaaS users. Use with account holders and team leads to identify churn risk, prioritize feature investments, and reduce plan-change barriers.

A post-task usability instrument using the validated Single Ease Question (SEQ) to measure task completion, perceived ease, and satisfaction. Ideal for UX researchers conducting moderated or unmoderated usability studies who need benchmarkable ease-of-use data alongside qualitative insight.

Measures how first-time users perceive, interact with, and evaluate pre-filled default settings during product onboarding. Use this to identify friction points and optimize the first-run setup experience.

Evaluates users' search experiences across intent expression, result relevance, and filter/sort usability to identify actionable improvements for product search, UX, and conversions.

Evaluates the discoverability, relevance, clarity, and control of search-filter experiences from the end-user perspective. Ideal for UX researchers and product teams seeking actionable feedback on faceted navigation.

Enables cross-functional stakeholders to evaluate roadmap initiatives on Impact, Confidence, and Ease dimensions, producing quantified ICE priority scores alongside qualitative risk and rationale data.

Measures behavioral patterns, value perceptions, and retention intent among high-frequency e-commerce shoppers to identify churn risk factors and inform loyalty strategy.

Evaluates how users engage with, perceive, and act on product release notes. Use this survey to identify gaps in clarity, content preferences, and delivery channel effectiveness to improve update communications.

A structured stakeholder assessment that measures launch confidence, track-level readiness, risk prioritization, and operational preparedness to support data-driven go/no-go decisions.

Measures user satisfaction with recommendation relevance, perceived control, and content diversity. Designed for product teams optimizing personalization engines and recommendation UX.

A structured post-session debrief for prototype usability tests, measuring task completion, perceived difficulty, friction points, and improvement priorities to inform iterative design decisions.

Measures why active users stay, what value they perceive, and where retention risk exists. Designed for product teams assessing product-market fit, switching costs, and competitive positioning.

Measures product essentiality using the Sean Ellis PMF framework alongside competitive switching risk and perceived differentiation to guide product strategy and roadmap prioritization.

Measures users' comprehension of privacy-preserving analytics techniques, trust in identity protection, and preferences for consent controls and defaults. Designed for UX researchers and product teams evaluating analytics transparency features.

Collects structured feedback from pilot participants on instrument clarity, cognitive burden, ambiguity sources, and session flow to guide pre-launch refinement of research questionnaires.

A recurring pulse survey for collecting structured user feedback on app or website speed, uptime, and error frequency over a 7-day window. Designed for product and engineering teams seeking to prioritize performance improvements and track reliability trends.