Market research survey templates

Measures how everyday consumers manage money: budgeting habits, financial stress, product usage, spending allocation, and satisfaction with their primary bank or financial provider. An AI follow-up interview digs into the real story behind a recent financial decision or stressful moment instead of settling for a scale score. Built for banks, fintechs, and researchers tracking consumer financial wellbeing.

A pre-conversation intake survey for sales, real estate, and consultative teams to capture a prospective buyer's needs, timeline, budget, and decision-making process before the first meeting. The AI follow-up digs into whichever decision factor matters most to that buyer and surfaces the deal-breakers a form alone would miss.

Measures how personally invested, informed, and careful shoppers are when choosing products in a given category — from perceived risk and personal identity to time spent researching — with an AI follow-up interview that reconstructs the real story behind a recent purchase decision. Built for brand and category teams who need to know how much shoppers actually think before they buy.

Uncovers what actually drives people to buy seminars, workshops, and paid training — skill gaps, career pressure, employer funding, urgency, or discounts — plus pricing perception. Built for seminar and training providers refining offers, messaging, and price points. The AI follow-up interview reconstructs the exact moment a respondent decided to purchase and what nearly stopped them.

Explores how people discover, evaluate, and decide to buy products — where they search, what almost stops them at checkout, and how they weigh price against trust and convenience. An AI follow-up interview reconstructs a respondent's most recent purchase step by step to surface the real friction points behind the ratings.

Measures how often people buy gasoline, what drives their choice of station, and how they perceive fair versus unfair pump prices — for retailers, fuel brands, and pricing teams. An AI follow-up interview digs into the gap between stated price sensitivity and what actually makes drivers switch or stay loyal.

Captures how car audio and stereo system buyers register their new equipment — where they bought it, who installed it, why they registered, and where the process breaks down — with an AI follow-up that digs into the specific friction points behind low ease-of-registration scores. Built for consumer electronics brands and installers refining their warranty and registration funnel.

Measures how satisfied recent car buyers and lessees are with the dealership experience, financing or lease terms, and the vehicle itself — including likelihood to recommend and repurchase. An AI follow-up interview digs into the specific moment or issue that shaped their rating, surfacing details a rating scale alone would miss.

Tests reaction to a new service concept before launch — clarity, uniqueness, price sensitivity, purchase intent, and adoption barriers — with an AI follow-up that digs into why respondents would or wouldn't buy. Built for product and innovation teams validating a concept ahead of investment.

A post-purchase warranty registration survey that captures where and why customers bought a product, how it's performing against expectations, and how likely they are to recommend it — paired with an AI follow-up that digs into the real story behind any gap between expectations and reality.

Traces the full purchase journey — what triggered the search, which channels shoppers trusted, which criteria mattered most, and how they feel about the outcome. Built for product, marketing, and insights teams benchmarking their buying process. The AI follow-up rebuilds the exact moment the decision tipped, surfacing the near-miss competitor and friction points closed questions miss.

Maps the end-to-end path customers take from first considering a purchase to completing it — the triggers, information sources, and trade-offs that shaped the decision. Built for product, marketing, and CX teams optimizing conversion; the AI follow-up interview reconstructs the exact moment of hesitation that almost derailed the purchase.

Builds a rich profile of who your customers are and what they care about—household makeup, media habits, and lifestyle interests—paired with an AI follow-up that digs into the story behind their top interest instead of just a checkbox.

Explores how often people visit the mall, what they buy, how they allocate spend across categories, and what drives their choice of retail destination. Built for mall operators, retail marketers, and tenant-mix planners; the AI follow-up interview reconstructs the real story behind a shopper's most recent purchase decision, including near-misses and price hesitation that closed questions miss.

Groups your customers into meaningful segments by combining behavioral usage patterns, need-based attitudes, purchase-driver trade-offs, and demographics. Built for marketers and researchers building or refreshing a segmentation model. The AI follow-up interview digs into the 'why' behind each respondent's top purchase driver so segments are grounded in real reasoning, not just survey scores.

Captures how recent buyers experienced the search, offer, closing, and move-in process, including satisfaction with their agent or builder and what mattered most in choosing the home. An AI follow-up interview digs into the reasons behind the lowest-rated moment in the journey, surfacing specific friction points a satisfaction score alone would hide.

Captures the full vehicle-purchase experience — what drove the decision, how the dealership or online process performed, and how likely buyers are to return or recommend — for auto manufacturers and dealer groups. An AI follow-up interview reconstructs the actual deciding moment, including near-misses and hesitations that closed-ended questions miss.

Measures unaided and aided brand awareness, familiarity, and consideration within a product category — including where awareness comes from. An AI follow-up interview digs into why certain brands come to mind first and what's blocking awareness of brands that don't, going beyond simple recall percentages.

Tests how a new product or service concept lands with your target audience — appeal, differentiation, believability, and purchase intent — plus a best-worst trade-off on concept features and a price sensitivity check. An AI follow-up interview digs into the reasoning behind purchase intent scores so you know what to fix before you build it.

Estimates the total addressable spend, current usage, and switching potential for a product or service category — combining budget allocation, price-sensitivity, and prioritization questions with an AI follow-up that reconstructs the real trigger events and decision-makers behind category spend, not just stated intent.

Tests reaction to a new product or service concept — appeal, differentiation, purchase intent, and an optimal price range — for teams deciding whether to build or launch. An AI follow-up interview digs into what would make respondents actually buy or walk away.

For consumer packaged goods and product teams running in-home or trial-use tests. Captures actual usage behavior, attribute-level ratings, purchase intent, and what matters most to improve — with an AI follow-up interview that reconstructs a specific moment of use to explain the ratings behind it.

Maps how many paid subscriptions people juggle, which categories they'd protect versus cancel first, and what actually triggers a cancellation. Built for subscription businesses and researchers tracking churn risk, with an AI follow-up that reconstructs a real recent cancel-or-keep decision instead of a hypothetical one.

Measures where budget-pressured shoppers trade down to store brands and where they still splurge on premium products, using a best-worst trade-off exercise and a budget-allocation task to map real priorities. An AI follow-up interview reconstructs the last splurge purchase to uncover the emotional vs. practical logic behind when 'worth it' actually wins. Built for CPG, retail, and pricing teams.

Measures how much shoppers trust functional food and beverage claims around fiber, protein, and gut health/probiotics, what evidence makes a claim believable, and where skepticism creeps in. An AI follow-up interview digs into a specific claim the respondent doubted or trusted and reconstructs what actually changed their mind. Built for brand, insights, and regulatory teams evaluating label and marketing language.

Compares how shoppers use a retailer's own AI assistant versus general tools like ChatGPT or Gemini for product research and purchase decisions. Built for retail and ecommerce teams deciding where to invest in AI shopping tools. The AI follow-up interview reconstructs the specific moment a shopper picked one over the other and what would change their mind.

Explores how Gen Z respondents use AI chatbots and assistants alongside search engines, social feeds, and influencers to find and vet products — including trust levels, time allocation, and what actually drives a purchase. An AI follow-up interview reconstructs a real recent AI-assisted shopping moment instead of relying on self-reported habits.

Measures how comfortable people are letting an AI agent renew, cancel, reorder, or renegotiate their subscriptions on their own — and what safeguards would make that trust possible. Built for product teams evaluating agentic commerce features, with an AI follow-up that digs into why trust differs by task type and what a bad experience would look like.

Measures how shoppers react when a familiar food product is reformulated with added protein or fiber claims — covering purchase intent, trust in the new claims, taste expectations, and price sensitivity. Built for brand and innovation teams testing packaging or recipe changes, with an AI follow-up that digs into the real tension between health claims and taste concerns behind the numbers.

Tracks how GLP-1 medication-driven weight change is reshaping clothing size, shopping frequency, spend allocation, and what people do with ill-fitting garments. Built for apparel and resale researchers, with an AI follow-up interview that digs into the emotional and financial decisions behind buying new versus altering or holding onto old clothes.

Measures how GLP-1 medications (like Ozempic, Wegovy, or Zepbound) are reshaping household grocery and snack shopping — spending shifts, category cuts, and snack decision factors. Built for CPG and retail researchers, with an AI follow-up interview that reconstructs a real recent shopping trip instead of relying on general impressions.

Captures how listeners actually consume your podcast — ideal episode length, topic preferences, ad tolerance, and what would push them to subscribe or share it — with an AI follow-up that digs into the real reason behind their loyalty score instead of a generic satisfaction number.

Measures whether a celebrity or influencer endorsement actually transfers credibility to a brand — covering perceived authenticity, trustworthiness, and purchase influence — with an AI follow-up that unpacks why the pairing does or doesn't feel believable to this specific respondent. Built for brand, marketing, and partnerships teams testing a specific spokesperson or creator campaign.

Benchmarks how customers and prospects rate you against specific named competitors on the criteria that actually drive their choice — combining a max-diff prioritization, a head-to-head perception battery, and an AI follow-up that digs into the exact moment a competitor won or nearly won the deal.

Tests how a set of logo or visual identity options perform on recognition, brand fit, emotional tone, and head-to-head preference. Built for brand and marketing teams comparing logo candidates or evaluating a redesign, with an AI follow-up that digs into the specific visual cues driving the winning choice instead of just recording a rank.

Captures what shoppers came to buy, how they navigated the store, what they did when an item was missing, and how checkout felt — with an AI follow-up that digs into the real reasoning behind substitution choices and navigation friction. Built for retail insights and category management teams.

Reconstructs the full journey behind a considered purchase — how customers first discovered the product, which sources shaped their decision, what nearly stopped them, and how confident they feel now. An AI follow-up rebuilds the actual sequence of events around the moment of hesitation, not just the tidy summary customers give afterward.

Measures which features customers want bundled together, how much value each feature category adds, and what they'd pay if features were sold à la carte — built for product and pricing teams designing tiered packaging. An AI follow-up unpacks the reasoning behind a respondent's top-ranked bundle instead of stopping at the ranking itself.

Measures how deal-driven your customers really are: what triggers a discount-motivated purchase, whether frequent promotions erode brand perception, and how willing shoppers are to pay full price. An AI follow-up reconstructs the last purchase decision they made under a discount to separate genuine price sensitivity from habit.

Measures how customers react to an upcoming or recent price increase — their acceptance thresholds, likelihood to churn, need to re-justify the value, and preferences for how the change is communicated. An AI follow-up digs into the specific reasoning behind each customer's churn risk score, surfacing what would actually change their mind versus what they say in the closed-ended scales.

Measures how prepared people feel for retirement — perceived savings adequacy, understanding of plan mechanics, and where they turn (or don't turn) for advice. Built for financial services, benefits, and wealth-management teams. An AI follow-up probes the reasoning behind the respondent's confidence score, surfacing the specific knowledge gaps and unmet advice needs that generic satisfaction scores miss.

A general-purpose consumer study covering category usage, brand awareness, buying criteria, and willingness to pay — the workhorse market research questionnaire, upgraded with unaided-first question order, a screener, and AI probing of the purchase story behind the checkboxes.

Map your brand's personality on classic semantic differential scales — traditional vs. innovative, corporate vs. human, premium vs. affordable — and compare against a competitor. The AI interviewer chases the associations behind the ratings, so you learn where the perception comes from.

Paste your landing page copy, ad, or email and see exactly which phrases pull people in and which lose them: respondents highlight the words that resonate and the words that create doubt, then an AI interviewer probes why. Sharper than a star rating on the whole block.

A voice AI debrief for closed deals — won or lost. Buyers tell an AI interviewer things they'd never tell the salesperson: the real decision criteria, who else was in the running, where the pitch missed, and what nearly changed the outcome. Structured questions make results comparable across deals.

A voice AI discovery interview that follows Mom Test rules automatically: past behavior, not hypotheticals; specifics, not compliments. It asks about the last time the problem happened, what it cost, and what they've already tried — the questions founders forget to ask when a polite prospect starts flattering their idea.

A choice-based conjoint that shows respondents realistic product profiles built from attributes and levels — plan tier, price, support, contract terms — and measures which combinations win. AI follow-ups capture the reasoning behind choices that a choice matrix alone can't explain.

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.

Prioritize waitlist signups by qualifying use case fit, urgency, decision authority, and advocacy potential. Designed for pre-launch SaaS and tool-based products targeting founders and early-stage teams.

Measures how clearly, credibly, and compellingly a value proposition communicates to target audiences, identifying perceived benefits, barriers to action, and messaging improvements to optimize conversion.