Pmf Survey

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](patterns/survey-question-wording.md) | Use the exact wording - variations invalidate the benchmark | | [who-to-survey](patterns/who-to-survey.md) | Only survey users who experienced the core value | | [forty-percent-benchmark](patterns/forty-percent-benchmark.md) | 40% is a threshold, not a target - understand what it means | | [ignoring-somewhat-disappointed](patterns/ignoring-somewhat-disappointed.md) | The "somewhat disappointed" segment is your growth engine | | [segment-before-action](patterns/segment-before-action.md) | You must segment responses before acting on feedback |

High Impact

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

Medium Impact

| Pattern | What It Teaches | |---------|-----------------| | [tracking-over-time](patterns/tracking-over-time.md) | How to measure PMF progress without invalidating comparisons | | [follow-up-questions](patterns/follow-up-questions.md) | The three questions that unlock the roadmap algorithm | | [enterprise-vs-consumer](patterns/enterprise-vs-consumer.md) | 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: wdavidturner
• Source: wdavidturner/product-skills
• License: MIT

Install and usage instructions live in the source repository linked above.