home / skills / wdavidturner / product-skills / pmf-survey

pmf-survey skill

/skills/pmf-survey

This skill helps quantify product-market fit using the PMF Survey to guide data-driven roadmaps and prioritization.

npx playbooks add skill wdavidturner/product-skills --skill pmf-survey

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md
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---
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](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

Overview

This skill lets you run a Product-Market Fit (PMF) Survey and turn the results into a prioritized roadmap. It operationalizes the Sean Ellis / Rahul Vohra approach: measure how disappointed users would be without your product, use the 40% “very disappointed” benchmark, and generate concrete actions to improve PMF. The output helps you decide whether to double down, iterate, or pivot.

How this skill works

You send the canonical PMF question to a vetted sample of users who have experienced the core value: "How would you feel if you could no longer use this product?" Responses are categorized (very, somewhat, not disappointed) and segmented by customer attributes. The skill calculates the PMF score, highlights high-impact segments, and recommends roadmap moves using follow-up answers to identify friction, desired features, and the primary benefit to retain.

When to use it

  • Before major investment decisions to quantify product-market fit
  • When deciding whether to pivot, persevere, or double down
  • To identify which customer segments love your product most
  • To prioritize roadmap items that will move the PMF score
  • To track PMF progress across releases and iterations

Best practices

  • Use the exact PMF wording and only survey users who experienced core value
  • Gather at least 40–50 valid responses before acting; segment responses before decisions
  • Ask the three follow-ups (primary benefit, top missing feature, and reason for disappointment) to generate actionable fixes
  • Treat 40% as a diagnostic threshold, not an absolute target—aim to learn why scores are low
  • Split time between doubling down on strengths and addressing objections from "somewhat disappointed" users

Example use cases

  • A startup measures PMF before raising a priced funding round to provide data to investors
  • A product team surfaces the segment with highest love and builds features tailored to them
  • A company runs the survey after a new onboarding flow to check if initial value is delivered
  • A team uses follow-up answers to create a prioritized 90-day roadmap aimed at lifting PMF
  • A PM tests whether to pivot by comparing segment-level PMF across user cohorts

FAQ

Is 40% a hard rule?

40% is an empirical threshold signaling strong PMF historically; use it as a diagnostic to guide deeper segmentation and action, not as a rigid pass/fail.

Who should I survey?

Only users who have experienced the product’s core value and had enough time to form an opinion; avoid employer-mandated users and brand-new signups.