pmf-survey

name

pmf-survey

description

Use when asked to "PMF survey", "measure product-market fit", "40% rule", "Sean Ellis test", "Rahul Vohra method", or "how disappointed would you be". Helps quantify product-market fit and systematically improve it. The PMF Survey framework (created by Sean Ellis, popularized by Rahul Vohra at Superhuman) measures how disappointed users would be without your product and turns that data into a roadmap.

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