| 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)
High Impact
Medium Impact
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
