Survey templates

Measures how often people use an entertainment chatbot (companionship, roleplay, humor, storytelling), what keeps them coming back, and where the experience falls flat — with an AI follow-up that reconstructs a specific memorable conversation to surface what actually made it feel fun, believable, or disappointing. Built for product and content teams shipping character or entertainment-focused AI experiences.

A short customer feedback survey for people who just chose a paid plan, written to avoid common question mistakes: general questions about the app before specific ones about today's checkout; exact numbers for how often and how long, with a seven-day window instead of "often"; balanced 5-point scales that name what they rate instead of agree/disagree; price and quality asked separately; past behavior (when they last paid for an app like this) instead of "would you pay"; and answer ranges that never overlap. Replace "the app" and "plan" with your own product.

Measures how teams actually use their project management tool day to day — adoption, feature-level satisfaction, and the friction points that drive people back to spreadsheets — with an AI follow-up that reconstructs a specific recent workflow moment instead of general sentiment. Built for product, IT, and operations teams evaluating or renewing tools.

A short customer feedback survey for people who just chose a paid plan, written to avoid common question mistakes: general questions about the app before specific ones about today's checkout; exact numbers for how often and how long, with a seven-day window instead of "often"; balanced 5-point scales that name what they rate instead of agree/disagree; price and quality asked separately; past behavior (when they last paid for an app like this) instead of "would you pay"; and answer ranges that never overlap. Replace "the app" and "plan" with your own product.

Captures how customers rate your drinks, wait time, staff, and atmosphere on their most recent visit, plus their likelihood to return. An AI follow-up digs into the specific moment that shaped their rating so you know exactly what to fix or protect.

A voice-first interview for customers who recently bought your product, capturing why they chose it over alternatives, how the first-use experience went, and whether they'd recommend it. Short closed questions anchor the context; the AI voice interview does the real work, reconstructing the purchase story and probing the reasoning behind their recommendation score. Built for CX and product marketing teams.

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.

Ten statements about confidence around other people from the public-domain International Personality Item Pool (IPIP, ipip.ori.org), the set Open Psychometrics scores as Social confidence. Each is answered from Strongly disagree to Strongly agree (IPIP's own format asks how accurately each statement describes the respondent). Statements 6 to 10 are worded in the opposite direction: score them in reverse (6 minus the answer on a 1 to 5 coding) before combining the ten into one score, and check Cronbach's alpha before relying on it. Each statement is its own single-choice question, so every answer keeps its scale order for analysis.

A nonpartisan pulse-check on what's driving voters ahead of the 2026 midterms: which issues matter most, how affordability is hitting household budgets, what qualities voters want in a candidate, and how likely they are to turn out. An AI follow-up digs into the concrete, personal reason behind each respondent's top issue instead of a generic label.

A general-purpose Likert-scale instrument skeleton for graduate dissertation research, pairing validated-style attitude, norm, and intention batteries with an AI follow-up that probes the reasoning behind attitude-behavior gaps. Built for thesis researchers to adapt with their own constructs; demographics are collected last for clean subgroup analysis.

Captures how shoppers actually behave during Black Friday and Cyber Week: planned versus impulse buys, channel choice (in-store vs. online), deal-research habits, spending mix, and post-purchase regret or returns. Built for retail and e-commerce marketers benchmarking seasonal campaigns, with an AI follow-up that reconstructs the real story behind each shopper's most impulsive or regretted purchase.

Measures whether consumers want brands to weigh in on current events, on which types of issues, in what tone, and what backlash risk they tolerate — for marketing and comms teams weighing when to speak up. The AI follow-up reconstructs a specific recent instance the respondent remembers, uncovering the real reaction behind their ratings.

Measures how recipients actually notice, read, and act on direct mail pieces — postcards, catalogs, letters — versus digital channels, with a best-worst exercise on what drives response and an AI follow-up that reconstructs what happened after a specific piece landed in someone's mailbox. Built for marketing teams and direct mail agencies evaluating campaign performance.

Understand how candidates experience AI-assisted screening, interviews, and assessments. Measure disclosure, explanation, access to human review, fairness, and willingness to continue, then use optional AI follow-ups to explore specific moments and improve the hiring process.

Measures how well employees are reintegrating after parental, medical, caregiving, or bereavement leave — workload ramp pace, manager check-ins, flexibility needs, and wellbeing. Built for HR and people teams running return-to-work programs, with an AI follow-up that surfaces exactly where support broke down instead of just how it was rated.

Measures how well seasonal hires feel trained, scheduled, and supported once they're on the floor — covering training sufficiency, POS confidence, safety prep, and intent to return next season. An AI follow-up digs into the specific gap behind their lowest-rated area so store ops can fix onboarding before the next peak.

Measures how often people use an entertainment chatbot (companionship, roleplay, humor, storytelling), what keeps them coming back, and where the experience falls flat — with an AI follow-up that reconstructs a specific memorable conversation to surface what actually made it feel fun, believable, or disappointing. Built for product and content teams shipping character or entertainment-focused AI experiences.

Evaluates how well an airline or travel site's AI chatbot handles real booking, change, and support tasks — covering task completion, trust, and where users bail out to a human. An AI follow-up interview reconstructs exactly what happened in the respondent's most recent chatbot session, not just how they'd rate it in hindsight.

Measures how well an AI scheduling chatbot helps patients book, reschedule, or get answers about medical appointments, covering task completion, trust, and friction points — with an AI follow-up interview that digs into the specific moment the chatbot helped or failed.

Measures how teams actually use their project management tool day to day — adoption, feature-level satisfaction, and the friction points that drive people back to spreadsheets — with an AI follow-up that reconstructs a specific recent workflow moment instead of general sentiment. Built for product, IT, and operations teams evaluating or renewing tools.

A structured performance check-in for software engineers that pairs self-rated competency scores, behavioral frequency questions, and a priority trade-off exercise with an AI follow-up interview that digs into the developer's biggest blocker and lowest-rated skill area for concrete, specific detail managers can use in 1:1s and growth plans.

Captures how a specific software release landed with users — installation friction, stability, performance, and which features actually helped — for product and engineering teams shipping regular updates. The AI follow-up interview reconstructs exactly what broke or slowed someone down instead of settling for a vague 'it had bugs.'

A general-purpose Likert-scale instrument skeleton for graduate dissertation research, pairing validated-style attitude, norm, and intention batteries with an AI follow-up that probes the reasoning behind attitude-behavior gaps. Built for thesis researchers to adapt with their own constructs; demographics are collected last for clean subgroup analysis.

Captures how much parents budget and spend across back-to-school categories — clothing, supplies, and technology — and how that spending pressures household finances. An AI follow-up digs into the trade-offs behind a parent's stress score: what got delayed, financed, or cut to cover the school list.

Captures how prepared, heard, and informed parents felt during a recent parent-teacher conference, what topics were actually covered, and whether a clear next-steps plan resulted — with an AI follow-up that reconstructs the specific concerns raised and whether they were resolved. Built for schools and PTAs auditing conference quality.

Collects everything organizers need from a confirmed speaker in one pass — bio, session title and abstract, AV/technical requirements, travel logistics, and photo/video consent — for anyone producing a conference, meetup, or panel. An AI follow-up catches AV mismatches before showtime by probing exactly what will happen on stage.

A voice-first interview that captures a grandparent's life story — childhood, moves, work, love, and traditions — for grandchildren to keep. Quick structured questions set the scene, then an AI audio interview draws out one vivid memory in sensory detail and the lesson the grandparent wants passed down, going deeper than a generic oral-history transcript.

A voice-first guide for recording a relative's life story — childhood, family, turning points, and the advice they want to pass on — led by an AI interviewer that asks warm, specific follow-up questions instead of a rigid script. Built for families, genealogists, and community groups preserving memories before they're lost. Respondents can pause or skip anything.

Captures the equipment type, cost, term preference, and business profile a lender needs to pre-qualify an equipment financing request. An AI follow-up digs into what's actually driving the requested timeline — a growth opportunity, a breakdown, or a contract deadline — so underwriters get the real story behind the urgency, not just a checkbox.

Tracks small business sentiment on revenue expectations, hiring plans, cost pressures, tariff-driven pricing decisions, and AI adoption. An AI follow-up digs into the owner's single biggest worry with a concrete recent example instead of a generic checkbox answer — useful for lenders, associations, and market researchers tracking small business health.

Captures who owns cryptocurrency, what they hold, why they got in, how they perceive risk, which platforms they trust, and where they stand on regulation. An AI follow-up digs into the personal reasoning and specific experiences behind each respondent's risk tolerance and buying decisions, giving you the 'why' behind the numbers.

Understand what changed after a team adopted [product].

Understand when customers recommend [product] and what they actually say.

Understand stakeholder goals and constraints for [initiative].

A public health research survey measuring vaping and nicotine product use among adults — what products are used, how often, quit attempts, and how flavor and price shape behavior. An AI follow-up probes the story behind a respondent's most recent quit attempt to surface real barriers and triggers that closed questions miss.

Tracks whether people plan to get vaccinated for flu and COVID-19 this season, what's holding hesitant respondents back, which access barriers get in the way, and which information sources they trust most. An AI follow-up probes the specific concerns behind low intention scores so public health teams can address the actual objection rather than a guess.

A periodic self-assessment covering physical health, sleep, stress, activity levels, and emerging symptoms — used by clinics, employers, or wellness programs to spot concerns early. An AI follow-up interview digs into whatever symptom or concern the respondent flagged as most pressing, capturing duration, severity, and impact that a checkbox can't.

Observe whether people can configure and export a report for [audience].

Observe how people upload, verify, and replace a file in [product].

Observe whether readers can locate and recognize useful content on [site].

Captures the origin, destination, cargo specifics, timing, and special handling needs a freight broker or carrier needs to price a shipment accurately. An AI follow-up clarifies any vague or incomplete cargo details — like unclear dimensions or hazmat class — before the quote goes to the routing team.

Measures how well seasonal hires feel trained, scheduled, and supported once they're on the floor — covering training sufficiency, POS confidence, safety prep, and intent to return next season. An AI follow-up digs into the specific gap behind their lowest-rated area so store ops can fix onboarding before the next peak.

Captures how customers rate your drinks, wait time, staff, and atmosphere on their most recent visit, plus their likelihood to return. An AI follow-up digs into the specific moment that shaped their rating so you know exactly what to fix or protect.

Understand the steps and effort involved in returning [item].

Explore what customers experienced between placing [order] and receiving it.

Understand how learners experienced [course] and applied what they learned.

Sean Ellis's product-market fit question, word for word: how would you feel if you could no longer use this product? If 40% or more of your active users answer very disappointed, that is the benchmark Ellis proposed. A screener ends the survey for anyone who has used the product less than twice in the past two weeks, so the share is read on active users; leave out "N/A: I no longer use it" answers when you calculate it. The Ellis question is followed by a values-laddering AI interview that sees each person's answer, then Ellis's follow-ups: who benefits most, the main benefit, the alternative, and what to improve. Keep the question and audience the same each time you run it, so the scores compare.

Test whether a question's wording changes the answers. Respondents are split at random: half are asked about spending on "welfare", half about "assistance to the poor", the General Social Survey pair (Smith 1987; Rasinski 1989). An A/B Condition assigns each person one version, so you can compare the two arms in Results. Swap in your own pair of wordings.

Tracks small business sentiment on revenue expectations, hiring plans, cost pressures, tariff-driven pricing decisions, and AI adoption. An AI follow-up digs into the owner's single biggest worry with a concrete recent example instead of a generic checkbox answer — useful for lenders, associations, and market researchers tracking small business health.

Choose an infographic that explains product benefits without adding confusion. Start with a comparison brief, follow-up questions, and rating scales.

Choose a main image that represents the product clearly and accurately. Start with a comparison brief, follow-up questions, and rating scales.

Choose an invitation that explains the session's value and who it is for. Start with a comparison brief, follow-up questions, and rating scales.

Create a guide layout that helps customers interpret the supplied sizing information. Customize the creative brief and style settings.

Create a comparison layout that helps shoppers understand real product differences. Customize the creative brief and style settings.

Create an explanatory visual direction that makes the supplied information easier to follow. Customize the creative brief and style settings.

Gauges resident awareness, support, and concerns for a local government project — plus which design option they favor and how they want ongoing updates. An AI follow-up probes the reasoning behind each support score, surfacing what would actually shift an opponent or strengthen a supporter's backing.

A resident-facing survey for local government and public safety offices that measures how safe people feel by time of day, whether streetlighting and infrastructure support that feeling, and how often incidents actually get reported. An AI follow-up probes the specific location and moment behind a resident's lowest safety rating, surfacing concrete detail a scale score alone can't capture.

A non-partisan measure of public trust in how elections are run — confidence in vote counting, poll worker conduct, mail voting, and the information sources people rely on. It does not ask about candidates or parties. An AI follow-up digs into the 'why' behind each respondent's confidence rating so administrators can see which specific process step is driving trust or doubt.

Assesses how prepared staff feel to communicate during a crisis — protocol awareness, spokesperson clarity, channel reliability, and drill experience — with an AI follow-up that reconstructs a real or simulated crisis moment to surface exactly where confusion or delay actually occurs.

Captures the origin, destination, cargo specifics, timing, and special handling needs a freight broker or carrier needs to price a shipment accurately. An AI follow-up clarifies any vague or incomplete cargo details — like unclear dimensions or hazmat class — before the quote goes to the routing team.

For employees requesting to onboard a new vendor or supplier. Captures the business need, estimated spend, urgency, risk factors, and alternatives already considered so procurement and operations teams can triage requests quickly. The AI follow-up interview probes why existing approved vendors won't meet the need and surfaces risk details a form alone would miss.

Find the price range your market will accept and the price point it finds fair. Uses the Price Sensitivity question: four Van Westendorp price questions on one card, charted in the report as the acceptable range and optimal price point, with a short AI follow-up on the reasons behind the prices. Before you launch, add a short description of your product (what it is, what it does, and who it is for) to the intro message.

Builds a demand curve by asking purchase likelihood at 4-5 ascending price points for the same product or service. Ideal for pricing teams who already run Van Westendorp but need a sequential-elasticity read. The AI follow-up probes the exact price where intent drops, surfacing the value story behind the number instead of just the drop-off itself.

Measures how a recent menu price increase is shaping visit frequency, item choices, and perceived value at your restaurant, plus how guests feel about service charges, kitchen fees, and other surcharges. Built for restaurant operators and multi-unit brands; the AI follow-up uncovers the real story behind a guest's value rating or changed visit habits, not just an averaged score.

Captures whether spa clients truly understand and freely consent to treatment risks, health disclosures, and photo or data use before their appointment — built for spa managers and compliance teams. The AI follow-up interview explores any hesitation or confusion behind risk-factor answers instead of settling for a checked box.

A pre-interview consent form that confirms participants understand what they're agreeing to — recording formats, how their words may be used, and their right to withdraw — plus an AI follow-up that catches confusion or hesitation before the real interview starts. Built for researchers running qualitative interviews, usability sessions, or customer discovery calls.

Measures how employees actually handle passwords, multi-factor authentication, updates, and suspicious messages day to day — not just what they know in theory. Built for IT and security teams auditing awareness programs, with an AI follow-up that reconstructs a real near-miss or incident instead of a hypothetical one.

A pre-proposal intake for consultants and agencies that captures a prospect's goals, current situation, budget range, timeline, and decision process before the first call. An AI follow-up digs past the polished pitch to find out what's actually broken, what's already been tried, and what a real win would look like — so you can scope and price with far less guesswork.

Captures how B2B buyers expect their procurement budget to move next year — direction, category-level growth or cuts, vendor consolidation plans, and AI spend intent — plus an AI follow-up that surfaces the real business reasons behind the number, not just the number itself. Built for sales, RevOps, and market research teams sizing pipeline and messaging for the year ahead.

Qualifies prospective gaming console buyers by purchase timeline, budget sensitivity, and feature priorities, then captures contact details for sales follow-up. The AI follow-up interview digs into what's actually holding back a purchase decision, giving sales reps a real objection to address instead of a generic 'still deciding.'

Measures public familiarity with kangaroos, perceptions of their characteristics, attitudes toward kangaroo conservation, and behavioral intentions. Suitable for wildlife researchers, conservation organizations, or educational program evaluators.

Measures leisure travelers' trip frequency, destination decision drivers, transportation preferences, satisfaction, and likelihood to recommend — providing actionable segmentation and experience insights for travel brands, tourism boards, or academic research.

A structured instrument for collecting student feedback on course satisfaction, instructional effectiveness, and learning outcomes. Designed for end-of-term deployment to support data-driven course improvement.

Assesses how prepared staff feel to communicate during a crisis — protocol awareness, spokesperson clarity, channel reliability, and drill experience — with an AI follow-up that reconstructs a real or simulated crisis moment to surface exactly where confusion or delay actually occurs.

Assesses whether a new geography or segment is ready for your offering — measuring category awareness, what people currently use instead, how locked in they are to it, and what they'd expect to pay. An AI follow-up digs into the real switching barrier behind any hesitation, surfacing contract, trust, or workflow obstacles a closed-ended scale would miss.

Captures why employees are requesting changes to their work schedule — shifted hours, compressed weeks, remote days, or reduced hours — and how urgent and job-critical the need is. Built for HR and People teams shaping flexible-work policy, with an AI follow-up that digs into the real constraint behind the request and what a workable compromise would look like.

Measures how employees actually get to work — mode, duration, out-of-pocket cost, and parking — and how hybrid schedules and transit benefits shape those choices. An AI follow-up digs past the surface rating to uncover the specific cost or reliability tipping points that would change commute behavior, giving facilities and HR teams evidence for benefit and parking decisions.

Measures how aware, concerned, and behaviorally engaged people are with climate change — from perceived risk and trust in institutions to everyday actions — with an AI follow-up that digs into the gap between stated concern and real-world behavior. Built for sustainability teams, communicators, and researchers tracking public climate sentiment.

Measures how consumers weigh responsible water use against price, quality, and other product attributes. Designed for consumer panels to quantify sustainability-driven tradeoffs and identify the attitudinal and contextual drivers behind willingness to pay for water stewardship.