Market Research Brief - Free Template + Examples

Edited and reviewed by Avi Yashchin, CEO and Co-Founder · August 21, 2026

A senior insights buyer picking a market research brief template has one real decision to make: whether the standard seven-field format is enough, or whether the brief also needs to specify how the resulting number gets checked against reality. Free templates covering that seven-field format exist from FlexMR, Fieldworkhub, Milanote, HolaBrief, Vision One, and Conjointly, and any one of them works as a starting point. None of them ask you to name the causal claim you're testing or the validation standard the vendor must clear before you trust what comes back.

• A standard brief template (background, objective, audience, methodology, timeline, budget) is table stakes; free versions exist from FlexMR, Fieldworkhub, and others, and any competent vendor expects one.
• A well-scoped brief still buys an unreliable answer if the study relies on stated preference alone: a published vaccination-behavior study found the negative predictive value of stated "no" answers was just 26%, meaning most stated refusals didn't hold up against actual behavior. The number is specific to vaccination behavior; the underlying gap between a stated answer and real behavior is not (PMC).
• Panel fraud and bot contamination force researchers to discard a large share of fielded survey data: one industry estimate puts the average at 38% of collected data, up to 70% in some studies. That figure comes from a vendor blog without a published methodology, so treat it as directional, not precise (User Intuition).
• The fix is not a longer template. It's two added fields: the causal claim being tested, and the validation standard the vendor must clear before the finding ships.
• A senior buyer's real decision is whether to accept a vendor's stated-preference number on faith or require it be checked against a documented replication standard, such as the one published on the leaderboard.

What does a standard market research brief template include?

A standard brief template asks for seven things: background context, a single-sentence objective, the target audience, the methodology, deliverables, a timeline, and a budget. That structure is necessary but not sufficient: it says nothing about whether the study's output reflects real behavior. Two rules sharpen the template itself. FlexMR's guidance on the objective line: describe the knowledge gap you need closed, not the method, and keep it to one sentence so the brief doesn't drift into scope creep (FlexMR). Fieldworkhub's sequencing rule: start from the business decision the brief exists to support, then work backward into research questions (Fieldworkhub).

Why isn't a well-scoped brief enough to trust the answer?

A well-scoped brief can still commission a study that gets the underlying behavior wrong, because scoping and validity are different problems. A published comparison of stated and revealed preference in vaccination behavior found the two matched in 80% of respondents overall. That headline number hides a lopsided failure pattern: positive predictive value (a stated "yes" turning into real behavior) was 85%, while negative predictive value (a stated "no" holding up) was just 26% (PMC). In plain terms, when respondents said they wouldn't do something, they were wrong roughly three times out of four. Vaccination behavior is not pricing or product positioning, and the exact miss rate won't carry over to every market research question. But the mechanism is domain-general: a self-report about future behavior is not the behavior itself, and a textbook-perfect brief has no field that asks a vendor to check the gap between the two.

Why does data quality also belong in the brief?

Data quality belongs in the brief because a clean scope says nothing about whether the fielded responses came from real people. One industry account puts the average discard rate to AI-generated panel fraud and bots at 38% of collected data, with some studies losing up to 70% (User Intuition). That figure comes from a vendor blog post without a published methodology or sample size, so treat it as directional, not precise. A brief optimized purely for scoping clarity doesn't touch this problem. It specifies who to study and what to ask. It says nothing about whether the fielded sample is real people, or whether the method used on real people produces answers that hold up against behavior.

What should a causal-scoped brief include instead?

A causal-scoped brief adds two fields to the standard template: the causal claim being tested, and the validation standard the vendor must clear. Naming the causal claim means stating what you're trying to identify, not just what you're trying to measure. For example: "does price framing X change purchase intent, independent of who sees it" rather than "understand reactions to price framing X." That distinction matters because discrete choice models, Mixed Logit, and ICLV are estimators, not causal methods on their own. Identification comes from the randomized manipulation built into the experiment design; the estimator just fits the model to the choices that design produced. A brief that specifies "use a discrete choice survey" without specifying what's being randomized and against what baseline is specifying a technique, not a causal claim.

What validation standard should a vendor have to clear before you trust the number?

A vendor should have to show its method's output correlates with a measured human baseline, with the ratio and its denominator stated, not a bare percentage. On one published study, the best-performing configuration reached 87% of the measured human ceiling. That's a 0.832 rank correlation against the published human result, against a ceiling of 0.959 measured between two independent samples of real humans answering the same study. Across all 43 studies that passed the design filters, the mean was lower: 0.73 of ceiling (causal fidelity paper). That's a validation result on studies already run, not a guarantee for a new market you haven't tested. It also carries a standing caveat worth putting in your brief: published human studies used as a validation benchmark may already sit in a model's training data. That's why replication protocols compare against held-out results rather than treating any single match as proof. The leaderboard publishes this validation ratio study by study, method by method, so a buyer can check a vendor's number against others before trusting it. Background on how discrete choice, Mixed Logit, and ICLV get validated against human baselines is covered on the methods and validation hub.

Standard brief template vs. causal-scoped brief

Both columns start from the same seven fields. The difference is two additional lines and the discipline to ask a vendor to show its work against a human baseline before the number goes into a decision memo.

Take your existing brief template and add two lines to the objective section: the causal claim you're testing, stated as an intervention and an outcome, and the validation standard your vendor has to report before you accept the result. If you want a second pair of eyes on how to phrase the causal claim itself, talk to the team.