measuring-product-market-fit
refoundai/lenny-skills
Assess and achieve product-market fit using frameworks from 46 product leaders.
What is measuring-product-market-fit?
Help users determine if they have product-market fit by measuring retention, running the Sean Ellis survey, and identifying customer pull. Use this when someone needs to diagnose their stage, decide whether to scale or iterate, and validate PMF in specific segments.
- Run the Sean Ellis disappointment survey to identify core value (40% "very disappointed" threshold)
- Analyze retention curves and engagement patterns to detect true product-market fit signals
- Validate PMF through reference customer counts (6-8 for B2B, 15-25 for B2C)
- Diagnose whether users show genuine pull (customers driving next steps) vs. polite interest
- Identify which customer segments have strongest fit before scaling broadly
- Flag common mistakes like confusing launch spikes, vanity metrics, or TAM with actual PMF
How to install measuring-product-market-fit
npx skills add https://github.com/refoundai/lenny-skills --skill measuring-product-market-fitHow to use measuring-product-market-fit
- 1.Ask the user about their current stage: customer count, retention metrics, and signals they're observing
- 2.Diagnose their situation: determine if they're pre-PMF, have PMF in a segment, or are confusing vanity metrics with real fit
- 3.Apply the appropriate framework based on their stage (Sean Ellis survey for early validation, retention curves for established products, reference customer counts for B2B/B2C)
- 4.Guide next steps: help them decide whether to scale, iterate, or focus on a specific segment
Use cases
- Early-stage founder unsure if they have PMF and whether to scale or keep iterating
- Product manager analyzing retention curves and engagement data to assess product-market fit
- Startup team running the Sean Ellis survey to measure the percentage of "very disappointed" users
- B2B company validating PMF by securing reference customers willing to advocate
- Team detecting signals of lost PMF (declining retention, reduced customer pull) and adjusting strategy
- Founders and CEOs evaluating product-market fit
- Product managers measuring retention and engagement metrics
- Early-stage startup teams deciding between scaling and iteration
- Growth leaders validating PMF before investing in paid acquisition
measuring-product-market-fit FAQ
According to Sean Ellis, if 40% of users say they would be "very disappointed" if they could no longer use your product, you're on the right track. Focus on understanding what makes this core segment love you.
Retention is the ultimate metric—if users aren't coming back, you don't have PMF. However, PMF also requires distribution: a retaining product without scalable user acquisition channels is not true PMF.
Reference customers are a strong validation signal. Aim for 6-8 references in B2B or 15-25 in B2C before scaling. If you don't have willing advocates, it's a red flag.
PMF often exists in specific segments first (e.g., early-stage startups). Look for the strongest retention, highest "very disappointed" percentage, and most customer pull in one segment, then expand from there.
True pull means customers are driving next steps—asking about pricing, timelines, and implementation—without you pushing. Polite interest is customers saying "this is interesting" but not taking action.
Full instructions (SKILL.md)
Source of truth, from refoundai/lenny-skills.
name: measuring-product-market-fit description: Help users assess and achieve product-market fit. Use when someone is trying to determine if they have PMF, measuring user engagement and retention, running the Sean Ellis survey, or figuring out if they should scale or keep iterating.
Measuring Product-Market Fit
Help the user assess and achieve product-market fit using frameworks from 46 product leaders.
How to Help
When the user asks about product-market fit:
- Understand their stage - Ask how many customers they have, what their retention looks like, and what signals they're seeing (or not seeing)
- Diagnose the situation - Determine if they're confusing vanity metrics with PMF, if they have PMF in a specific segment, or if they're clearly pre-PMF
- Apply the right framework - Help them use the Sean Ellis survey, retention curves, or reference customer counts depending on their situation
- Guide next steps - Help them decide whether to scale or continue iterating based on the evidence
Core Principles
Use the Sean Ellis "disappointment" survey
Sean Ellis: "How would you feel if you could no longer use this product? Very disappointed, somewhat disappointed, or not disappointed. If 40% say 'very disappointed,' you're on the right track." This is a leading indicator of PMF before long-term retention data is available. Focus on the "very disappointed" segment as your core value indicator.
Retention is the ultimate metric
Uri Levine: "Product market fit has one metric. Retention. If you create value, they will come back. If they're not coming back, you're not creating value." Look for retention curves that flatten over time rather than decaying to zero. The "smile curve" - where engagement increases over time - is the strongest signal.
PMF is obvious when you have it
Matt MacInnis: "Product market fit is something where you absolutely know it when you see it. Therefore if you don't absolutely know it, you don't have it." If there's doubt, you likely don't have it. Look for the market pulling the product out of your hands.
PMF is not static - it can be lost
Casey Winters: "Protecting what you've built is increasingly important once you build scale. You might fall out of product market fit in a year or five years if you're not continually making your product better." Markets shift, competitors improve, and user expectations rise.
Reference customers validate PMF
Christian Idiodi: "The holy grail is really a reference customer - somebody who loves it enough to tell people about it. I want 6-8 references for B2B, 15-25 for B2C as an indication of PMF." Don't launch publicly until you have secured the target number of references from early users.
PMF exists in segments, not universally
Karri Saarinen: "The way we think about it is, 'Do we have the fit in specific segments?' and how strong that fit is." Find PMF in one segment first (e.g., early-stage startups) before expanding. Double down where you see natural pull.
PMF requires distribution, not just retention
Casey Winters: "If you have a product that retains well and you can't find more users for it, I don't think that's product market fit." True PMF requires both a retaining product AND a scalable, built-in distribution mechanism.
PMF is multi-stage, not binary
Todd Jackson: "There's essentially four levels: nascent, developing, strong, extreme." Level 1 (3-5 customers), Level 2 (5-25 customers), Level 3 (25-100 customers), Level 4 (100+ customers). Sequence focus: satisfaction at Level 1, demand at Level 2, efficiency at Level 3.
Look for customer "pull"
Raaz Herzberg: "We felt the questions change - 'How are you pricing this? When can we start a POV?' That's real intent." True pull is characterized by customers driving next steps, not just saying "this is interesting."
A lack of outrage during outages = no PMF
Jeff Weinstein: "During those 20 minutes our customers weren't furious. That was the signal we did not have product market fit." If your product goes down and nobody notices or complains, you haven't solved a mission-critical problem.
Questions to Help Users
- "If users couldn't use your product anymore, what percentage would be 'very disappointed'?"
- "What does your retention curve look like at day 7, 30, and 90?"
- "Do you have customers willing to be references and tell others about you?"
- "Is the market pulling the product from you, or are you pushing it on them?"
- "Are customers driving next steps (asking about pricing, timelines) or just being politely interested?"
- "What specific segment do you have the strongest fit in?"
Common Mistakes to Flag
- Confusing launch spikes with PMF - Product Hunt success or press coverage doesn't mean you have PMF. Look for sustained organic growth
- Ignoring retention data - If users aren't coming back, you don't have PMF regardless of how many you acquire
- Scaling too early - Paid growth before PMF just burns cash and can damage your brand
- Conflating TAM with PMF - A large market opportunity doesn't mean you've achieved fit within it
- Listening to "somewhat disappointed" users - Focus on what makes "very disappointed" users love you, not what would make lukewarm users slightly happier
Deep Dive
For all 64 insights from 46 guests, see references/guest-insights.md
Related Skills
- Designing Growth Loops
- Retention & Engagement
- Conducting User Interviews
- Startup Pivoting
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