Market research survey template — research-grade methodology, no PhD required

Who this template is for

There's a category gap between a customer satisfaction survey and a market research study, and the difference is methodology. A satisfaction survey collects feedback from people who already use your product; a market research study generates statistically usable signal about a population that may or may not include your customers. The difference shows up in the design — research surveys need screening criteria (only the target segment qualifies), attention checks (so you can identify and discard low-effort respondents), randomized question order (to control for primacy and recency effects on aggregate results), specific question types matched to research goals (Likert for attitudes, ranking for preference, MaxDiff for relative importance, Van Westendorp for pricing, conjoint adjacent for tradeoffs), and a panel-sourcing pattern that matches your goal (own-customer panel for validation, third-party panel for population-representative samples). This template doesn't make you a PhD researcher, but it gives you the structure that prevents the most common mistakes — accepting unqualified respondents, building bias into the question order, missing attention failures, mishandling incentive payments, and producing a sample your team can't defend in front of a skeptical stakeholder.

From fielding to analysis-ready dataset

Define your sample first (target segment + size + demographic quotas). Decide your panel source — your own customer list (free, fast, biased toward existing customers), a third-party panel (Toluna, Pollfish, dscout, UserTesting, Prolific, Respondent, MindMiners or Provokers for Brazilian panels, OpinionBox, IRIS for Iberian panels), or a hybrid (your own list for primary, panel top-up to fill quotas you can't fill from your list). Run the screening questions first — disqualify respondents who don't match the target segment as early as possible to save panel cost. Randomize question order within blocks where appropriate (especially for any question where item order would create primacy or recency bias). Insert attention checks ("to confirm you're reading, please select 'agree'" or trap items like "how many times in the last 6 months have you used a product that doesn't exist") at 1-2 points in a longer survey. On submission, route the data into your analysis tool — SPSS, R, Stata, Python pandas, or for less technical teams Google Sheets / Airtable with summary tabs. For paid panels, the incentive handoff is automated (the panel platform pays the respondent; you pay the panel platform per complete). For own-customer panels, integrate the form with a gift-card delivery API (Tremendous, Giftbit, or a points platform if you operate one).

What's included

Each field and each structural pattern is here because research methodology depends on it. Skip what you don't need for your specific study, but resist removing screening criteria, attention checks, and randomization — those are the difference between data you can act on and noise you can't.

Built for the teams that run market research without a research department

• Product market-fit validation (founders and startups)

  The 'Sean Ellis test' question ("how would you feel if you could no longer use this product?" with a Very Disappointed threshold of 40%+) is the most-cited PMF measurement, but a research-grade PMF survey is more than that one question. Include screening on customer status (active in last 30/60/90 days), randomized open-text questions on what value the product delivers, value-prop importance ranking (MaxDiff or simple ranking), competitive comparison (which products would they switch to if yours disappeared), and Van Westendorp pricing questions for monetization studies. For early-stage founders, this is often the highest-ROI research you can run — a 30-minute survey to 100-300 customers can reset your roadmap in a way no amount of internal debate can.

• Brand tracking and awareness studies

  Tracking studies are recurring (monthly or quarterly) and need consistency across waves — same questions, same scales, same panel composition — so trend lines are interpretable. The template needs to capture unaided brand recall ("name three brands in [category]"), aided brand awareness ("which of these brands have you heard of?"), brand attribute associations (Likert ratings for each brand on dimensions like quality, innovativeness, value, trust), consideration set ("which would you consider buying?"), and purchase intent. For agencies serving brand clients (Kantar, Ipsos, Nielsen, GfK, YouGov, IBOPE Inteligência or Box1824 for Brazilian studies, IRIS or AIMC for Iberian), the data export needs to support cross-tabulation by demographics, and the survey instrument itself becomes a contract deliverable with brand stakeholders.

• Pricing research (Van Westendorp, Gabor-Granger, conjoint)

  Pricing research is methodologically specific. Van Westendorp's Price Sensitivity Meter (PSM) uses four anchor questions (too cheap / cheap / expensive / too expensive) to derive an acceptable price range. Gabor-Granger uses a ladder of prices to find the optimal point. Choice-based conjoint analysis (CBC) presents the respondent with a series of competing offers (price + feature combinations) and uses the trade-offs to derive willingness-to-pay for each attribute. Each of these is a structured question pattern that can be implemented in a survey form, though the analysis side typically requires a tool like Conjoint.ly, Qualtrics XM, Sawtooth, or Quantilope for the post-survey calculations. The form captures the raw responses; the analysis tool derives the insights.

• Agency-side research panels and syndicated studies

  Research agencies running custom or syndicated studies need a survey instrument that can be deployed across multiple clients and waves, with consistent quality controls. The template needs to support standardized screening (so the same agency-side ICP definition is used across studies), modular sections (a brand client adds a few brand-specific questions to a syndicated study), incentive structure tied to the agency's panel platform (Lucid, Prodege, Pure Spectrum, Cint, or panels owned directly), and ESOMAR-aware quality assurance (attention checks, speed traps for respondents racing through, IP-deduplication, panel-membership-history checks). The output needs to support analyst handoff into the agency's analysis tools (typically SPSS, Q, MarketSight, or a custom internal stack).

• User research at scale (UX research, customer insights teams)

  Quantitative user research at the scale of 500-5,000 respondents per study, alongside the qualitative interview cycle. The template captures usage segmentation (which features they use, frequency, primary use cases), satisfaction across the customer journey (signup, onboarding, day-1 usage, weekly usage, support interactions), and feature-prioritization questions (rank or rate the value of upcoming features in the roadmap). The data integrates with the team's existing stack — Dovetail or Reduct for qualitative themes, Hotjar or PostHog for behavioral signal, Productboard for prioritization. For tech companies with mature user-research orgs, the survey is one input alongside session recordings, support tickets, and product analytics.

• Academic and government research

  Academic studies (university research, dissertation work) and government surveys (Census-adjacent, health departments, transportation authorities) have stricter compliance requirements — IRB approval for academic studies in the US, equivalent ethics review in EU/UK universities, and government-specific protocols for public-sector research. The template needs to support informed consent capture (a checkbox with explicit consent language, time-stamped), de-identification of sensitive responses, retention-period documentation (often 5-7 years for academic studies, 1-3 years for routine government surveys), and accessibility (screen-reader compatibility, plain-language alternatives, multi-language deployment for population-representative samples).

Adapt the survey to your research methodology

Start by defining the sample frame — who specifically qualifies for this study — and write the screening questions to enforce that frame at the top of the survey. Disqualifying early saves panel cost (third-party panels charge per complete, not per attempt) and protects your data quality. Decide whether to randomize question order within each section; for attitude scales where item ordering creates bias, randomization is non-optional. Insert at least one attention check in any survey above 5 minutes long; trap items (a question with an obvious correct answer that catches respondents who aren't reading) are more defensible than instructed-response items if you'll be challenged on data validity. For pricing studies, choose your method (Van Westendorp, Gabor-Granger, conjoint) based on what you need to decide — Van Westendorp tells you an acceptable range, Gabor-Granger optimizes a single price, conjoint quantifies willingness-to-pay for specific features. For paid surveys, set the incentive at a level that matches your audience's time value — $3-5 for a 5-minute consumer survey, $20-50 for a 15-minute B2B specialist survey, $100+ for an hour-long interview with executives — and use a panel platform that handles the payment automation. For ESOMAR-aware studies (any research where you'll publish or share results outside your company), add the standard disclosures: study sponsor, data handling, withdrawal rights, contact for data access requests under GDPR or LGPD.

Market research survey FAQ

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