What is the 40% rule for Product-Market Fit?

The 40% rule is a benchmark where if 40% or more of surveyed active users say they would be 'very disappointed' if they could no longer use your product, you have reached a threshold indicating sustainable product-market fit.

How does this survey differ from NPS?

While NPS measures advocacy and likelihood to recommend, the PMF survey measures 'essentiality.' It focuses on how much a user relies on the product rather than just how much they like it.

Why is segmentation important in PMF analysis?

Often, an overall PMF score may be low, but specific segments (like Founders or Sales Leads) may have very high scores. Segmentation helps you identify which specific market niche you have actually conquered.

Which users should be included in a PMF survey?

You should only survey 'active' users who have experienced the core value of your product. A common criterion is users who have used the product at least twice in the past 14 days and have been customers for at least one week.

Product-Market Fit Survey

Name: Product-Market Fit Survey
Author: lev-os

bylev-os

0•

Analytics & Monitoring

Implements the Sean Ellis 40% rule to measure product-market fit through targeted user sentiment analysis and segmentation.

The Product-Market Fit (PMF) Survey skill provides a rigorous, data-driven framework for quantifying the historically vague concept of product-market fit. Based on the Sean Ellis methodology, it guides product teams through the process of surveying active users to determine if at least 40% would be 'very disappointed' if the product were discontinued. This skill moves beyond surface-level metrics like NPS to identify true essentiality, helping teams decide whether to scale growth investments or continue iterating on core value. It includes comprehensive instructions for audience definition, survey design, segmented data analysis, and qualitative feedback synthesis.

Key Features

01Quarterly tracking methodology for monitoring PMF trends

02Multi-dimensional segmentation by user role and persona

03Qualitative analysis framework for 'Main Benefit' responses

04Active user population filtering to prevent signal dilution

05Quantitative PMF scoring based on the Sean Ellis 40% threshold

060 GitHub stars

Use Cases

01Identifying high-PMF customer segments to refine target personas

02Validating readiness for scaling growth and marketing spend

03Prioritizing feature roadmaps based on 'must-have' user feedback

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lev-os/agents product-market-fit-survey

For use in Claude.ai and ChatGPT

Download Skill