Product Execution Used 0 times

PMF Survey (Product-Market Fit Survey)

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.

Name: pmf-survey

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.

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)

Resources

Articles:

  • How Superhuman Built an Engine to Find Product-Market Fit by Rahul Vohra

Books:

  • Hacking Growth by Sean Ellis
  • The Lean Startup by Eric Ries

Quick Install

Add this skill to your AI assistant in 3 simple steps. No coding required!

1

Create the skill file

Run this command to create the directory and SKILL.md file:

mkdir -p .claude/skills/pmf-survey-product-market-fit-survey && touch .claude/skills/pmf-survey-product-market-fit-survey/SKILL.md

This creates the directory and an empty SKILL.md file.

2

Open the skill file

Open the SKILL.md file in your favorite editor:

nano .claude/skills/pmf-survey-product-market-fit-survey/SKILL.md

Or use code .claude/skills/pmf-survey-product-market-fit-survey/SKILL.md for VS Code

3

Add the content

Copy the skill content and paste it into the SKILL.md file:

Then save the file. Now you can use the skill by typing /pmf-survey-product-market-fit-survey in your AI assistant, or it will automatically use it when relevant.

Using a different AI assistant?

Claude Code: .claude/skills/
OpenCode: .opencode/skills/

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