The product-market fit glossary
Every term you need to measure, read, and prove product-market fit — defined in plain English. Start with the Sean Ellis 40% rule, or measure your score with the free PMF calculator.
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Product-market fit (PMF)
The degree to which a product satisfies strong market demand. Marc Andreessen's classic framing is "being in a good market with a product that can satisfy that market." Crucially, PMF is measurable — not just a feeling — most reliably via the Sean Ellis survey. See four definitions from the people who coined it.
Sean Ellis test
A survey method for quantifying product-market fit. You ask users a single question — "How would you feel if you could no longer use [product]?" — with the options very disappointed, somewhat disappointed, not disappointed, and N/A. The result is your PMF score. Read the full method in the Sean Ellis 40% rule.
The 40% rule
Sean Ellis's benchmark, drawn from surveying hundreds of startups: products where at least 40% of users would be "very disappointed" to lose the product have historically been able to grow sustainably. Below 40% usually means fit isn't there yet — 30–39% is close, and under 30% means keep iterating.
PMF score
The single number from the Sean Ellis test: the percentage of "very disappointed" responses among valid answers (the N/A bucket is excluded). The formula is very ÷ (very + somewhat + not disappointed) × 100. Calculate yours with the free PMF score calculator →
"Very disappointed" segment
The users who would be genuinely upset to lose your product — your core market and evangelists. Their share of valid responses is your PMF score. Serving this segment better, and finding more people like them, is the fastest route to fit.
Ideal Customer Profile (ICP)
The specific segment that gets the most value from your product — the users who most often answer "very disappointed." Building for them, and acquiring more of them, is how you raise your PMF score. PMFtracker derives your ICP automatically from survey responses.
High-Expectation Customer (HXC)
Julie Supan's concept: the most discerning person in your target market who will love your product for its core benefit and tell others. The HXC is a sharper version of your ICP, derived from your "very disappointed" segment, and a useful north star for positioning and roadmap.
Retention curve
A cohort chart showing the share of users still active over time. A curve that flattens — rather than decaying toward zero — is one of the strongest signals of product-market fit, and complements the survey score. See retention curve vs the PMF survey.
Net Promoter Score (NPS)
A metric of how likely existing users are to recommend a product (0–10). NPS measures advocacy; the PMF survey measures how essential a product is. They answer different questions, and PMF is the better leading indicator of fit. See NPS vs product-market fit.
Activation
The point at which a new user first experiences the product's core value. Only activated, engaged users should be surveyed for PMF — measuring sign-ups who never returned produces a meaningless score.
Churn
The rate at which users stop using or paying for a product. High churn alongside a low PMF score is the classic "no market need" pattern; rising PMF typically shows up first as flattening churn and a healthier retention curve.
PMF survey sample size
The number of valid responses needed for a trustworthy PMF score. Aim for at least ~40 valid responses from engaged users; more increases confidence. With too few responses the score swings wildly and shouldn't be relied on. See the survey template for who to ask and when.
Leading vs lagging indicators
Lagging indicators — revenue, churn — confirm fit after the fact. Leading indicators — the PMF survey score and the shape of your retention curve — reveal it earlier. Tracking your PMF score over time is a leading-indicator practice: it tells you where you're heading, not just where you've been.
Product death cycle
A trap where a team builds only the features customers explicitly request, never addressing the deeper job-to-be-done — quietly killing the product while feeling busy and responsive. See the product death cycle.
"No market need"
The most common reason startups fail — around 42% — i.e., building something nobody sufficiently wants. Measuring product-market fit early, and tracking it, is how founders catch this before it's fatal. See why "no market need" is still the #1 startup killer.
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