PMF Survey (Product-Market Fit Survey)

What It Is

The PMF Survey is a method to measure and systematically improve product-market fit. The core insight: you can put a number on product-market fit, and you can use that number to write your roadmap.

The key question: "How would you feel if you could no longer use this product?"

• Very disappointed - "I'd be devastated. I need this."
• Somewhat disappointed - "I'd be bummed but I'd find something else."
• Not disappointed - "I wouldn't really care."

Sean Ellis discovered that companies with 40% or more "very disappointed" responses almost always grew successfully, while those under 40% struggled. This benchmark has held across thousands of companies.

Rahul Vohra at Superhuman took this further: he built an engine that uses survey responses to algorithmically generate a roadmap guaranteed to increase PMF score.

When to Use It

Use the PMF Survey when you need to:

• Quantify product-market fit before making major investment decisions
• Decide whether to pivot or double down
• Prioritize your roadmap based on what will actually move the needle
• Identify your best customer segment (who loves you most)
• Track PMF over time as you iterate
• Make the case to investors with data, not gut feeling

When Not to Use It

• You have fewer than 30 active users (sample too small)
• Users haven't had enough time to experience value (survey too early)
• The product is employer-mandated (users had no choice)
• You want to validate a hypothesis without building (use JTBD instead)

Patterns

Detailed examples showing how to apply the PMF Survey correctly. Each pattern shows a common mistake and the correct approach.

Critical (get these wrong and you've wasted your time)

Pattern	What It Teaches
survey-question-wording	Use the exact wording - variations invalidate the benchmark
who-to-survey	Only survey users who experienced the core value
forty-percent-benchmark	40% is a threshold, not a target - understand what it means
ignoring-somewhat-disappointed	The "somewhat disappointed" segment is your growth engine
segment-before-action	You must segment responses before acting on feedback

High Impact

Pattern	What It Teaches
sample-size-myths	40-50 responses is enough - don't wait for statistical perfection
wrong-timing	Survey after first value, not after signup
acting-on-not-disappointed	Stop trying to convert the "not disappointed" users
main-benefit-filter	Only act on feedback from users who love your core value
doubling-down-vs-fixing	Half your time on strengths, half on objections
high-expectation-customers	Learn your ideal customer profile from users who love you
pivot-vs-persevere	Check for segment-level PMF before deciding to pivot

Medium Impact

Pattern	What It Teaches
tracking-over-time	How to measure PMF progress without invalidating comparisons
follow-up-questions	The three questions that unlock the roadmap algorithm
enterprise-vs-consumer	Adapting the survey for B2B vs B2C contexts

Deep Dives

Read only when you need extra detail.

• references/pmf-survey-playbook.md: Expanded framework detail, checklists, and examples.

Resources

Articles:

• How Superhuman Built an Engine to Find Product-Market Fit by Rahul Vohra (First Round Review) - the definitive guide
• Sean Ellis's original PMF survey methodology

Books:

• Hacking Growth by Sean Ellis - context on growth and PMF metrics
• The Lean Startup by Eric Ries - complementary framework for validation

Podcasts:

• Lenny's Podcast episode with Rahul Vohra - deep dive on the methodology and how Superhuman applied it

Credits:

• Sean Ellis - Created the original PMF survey question and discovered the 40% benchmark
• Rahul Vohra - Popularized the methodology and built the "PMF Engine" algorithm for systematically improving the score