Market Research
Market research exists to replace assumption with evidence before money is committed — to a product launch, a market entry, a price change, or an investment thesis. It splits into two data types (primary, collected first-hand for the specific question at hand; secondary, already collected by someone else for a different purpose) and two method families (qualitative, which explains why and how, via interviews, focus groups, and ethnographic observation; quantitative, which measures how many and how much, via surveys, experiments, and statistical analysis of transaction or usage data). Good market research almost always triangulates: it starts with secondary research to frame the problem cheaply, then uses primary research to close the specific evidence gaps secondary sources cannot answer, and it pairs qualitative depth with quantitative reach rather than relying on either alone. The output feeds directly into the market-sizing, segmentation, and strategic-framework methods elsewhere on this site — those methods are only as reliable as the research data underneath them.
What this method is.
A precise definition, its boundaries, and when it applies -- before any formula or worked example.
Definition
Market research is the systematic process of gathering, recording, and analyzing data about a market's customers, competitors, and broader environment in order to reduce uncertainty in a business decision. The American Marketing Association's official definition frames it as "the function that links the consumer, customer, and public to the marketer through information—information used to identify and define opportunities and problems; generate, refine, and evaluate actions; monitor performance; and improve understanding of it as a process" (AMA, definition approved 2017). In practice, market research is the data-collection and validation layer underneath every other method on this site: before you can size a market (see Market Size, TAM/SAM/SOM), segment it, or run a PESTLE or Porter's Five Forces read on it, you need defensible primary and/or secondary data about who the customers are, what they need, who else is competing for them, and what external forces will shape demand.
Scope and exclusions
In scope: the methodology of gathering original data (primary research — surveys, interviews, focus groups, field observation, experiments) and of locating and validating existing data (secondary research — government statistics, industry association reports, company filings, syndicated data services); sampling and sample-size discipline; qualitative vs quantitative method selection; survey and interview instrument design; and the ethical/legal guardrails professional researchers operate under (informed consent, data protection, non-coercion). Out of scope, and covered by sibling method pages instead: the arithmetic of turning research data into a market-size figure (see Market Size and TAM, SAM and SOM); the strategic frameworks applied to research findings once collected (see Market Analysis, PESTLE Analysis, Porter's Five Forces, Value-Chain Analysis); segmentation logic itself (see Market Segmentation); and financial or legal due diligence, which follows different standards (audit, GAAP/IFRS, disclosure law) than market research does. Market research also does not include internal operational data analysis (e.g., analyzing your own CRM funnel) except where that data is being used as one input alongside external research.
When to use it
- Before launching a new product or entering a new country/city market, to validate that real demand exists before building or spending against it
- Before finalizing a market-size estimate (TAM/SAM/SOM) or growth forecast, since sizing math is only as good as the underlying demand and pricing data feeding it
- When writing a business plan, investment memo, or board deck that will be scrutinized on its evidence base, not just its logic
- To test pricing, packaging, or positioning with real prospective buyers before a launch, rather than after
- To track brand perception, customer satisfaction, or competitive share over time via repeated (tracking) surveys
- When internal data (CRM, product analytics) can explain what existing customers do but cannot explain why non-customers are not converting, or what the total addressable population looks like beyond your existing funnel
- Before a market-entry or M&A decision, to sanity-check target-market assumptions against independent primary and secondary evidence rather than the deal team's own priors
How to apply it.
A repeatable step-by-step procedure, the underlying formula where one exists, and a worked example using illustrative numbers.
Step by step
Formula
Cochran's sample-size formula (for quantitative survey research)
n₀ = Z² × p(1 − p) / e² — with finite-population correction: n = n₀ / [1 + (n₀ − 1)/N]
required sample size for an infinite/unknown population
z-score for the desired confidence level (1.96 for 95% confidence, the standard used in most market and social research)
expected proportion of the population with the attribute being measured; use 0.5 when unknown, since p(1−p) is maximized at p=0.5, which yields the most conservative (largest) sample size
acceptable margin of error, expressed as a decimal (e.g., 0.05 for ±5 percentage points)
known size of the finite target population, used to correct n₀ downward when the population itself is small relative to n₀
William G. Cochran, Sampling Techniques, 3rd edition (Wiley, 1977)
This formula sizes a quantitative survey sample for a given confidence level and margin of error; it is not a market-sizing formula — for turning research data into a dollar market-size estimate, see the Market Size and TAM, SAM and SOM method pages.
Worked example ILLUSTRATIVE
A B2B SaaS company sells operations software to mid-market logistics firms and has identified a total addressable population of 8,000 companies in its target country. Before greenlighting a new route-optimization module, it wants to survey this population to validate willingness to pay, at 95% confidence and a ±5 percentage-point margin of error.
- Step 1 — base sample size (infinite population assumption): n₀ = Z² × p(1−p) / e² = 1.96² × 0.5 × 0.5 / 0.05² = 3.8416 × 0.25 / 0.0025 = 384.16, rounded up to 385.
- Step 2 — finite population correction, since the target population (N = 8,000) is known and finite: n = n₀ / [1 + (n₀−1)/N] = 385 / [1 + 384/8000] = 385 / 1.048 ≈ 367.
- Step 3 — inflate for expected non-response: if the company's prior surveys of this segment saw a 25% completion rate, it needs to reach roughly 367 / 0.25 ≈ 1,468 companies to end up with 367 completed responses.
- Step 4 — complement with qualitative depth: alongside the quantitative survey, the company runs 12–15 in-depth interviews with prospective buyers to understand the 'why' behind stated willingness to pay; per Guest, Bunce & Johnson (2006), thematic saturation in reasonably homogeneous samples is often reached within a similar range of interviews, though the team keeps interviewing past the point new themes stop appearing rather than stopping at a pre-fixed number.
The company needs approximately 367 completed survey responses (from an outreach pool of roughly 1,468, assuming a 25% completion rate) to be 95% confident that its measured willingness-to-pay figure is accurate to within ±5 percentage points, plus 12–15 qualitative interviews to explain the reasoning behind that number.
Where analysts go wrong.
The most frequent errors made when applying this method, so you can check your own work against them.
Common errors
Related methods and tools.
Other frameworks that pair with this one, and the calculators/tools that implement it.
Related methods
Related tools
Not yet available.
Further reading
- Kotler, P. & Keller, K.L., Marketing Management (Pearson) — Chapter on the marketing research process (the 6-step problem-definition → plan → collect → analyze → report → follow-up sequence referenced above)
- ICC/ESOMAR International Code on Market, Opinion and Social Research and Data Analytics (2025 edition) — the global self-regulatory standard for research ethics and methodology
- Cochran, W.G., Sampling Techniques, 3rd ed. (Wiley, 1977) — originating source for the sample-size formula above
- Guest, G., Bunce, A. & Johnson, L. (2006), "How Many Interviews Are Enough? An Experiment with Data Saturation and Variability," Field Methods 18(1)
Sources and review.
Every important figure on this page is traceable to a dated source. This page was last human-reviewed on an unrecorded date.