pmf-survey
npx skills add https://github.com/wdavidturner/product-skills --skill pmf-survey
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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