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Product Market Fit Analysis

  • 7 installs
  • 80 repo stars
  • Updated May 18, 2026
  • manojbajaj95/gtm-skills

Helps with ai & agent building tasks.

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product-market-fit-analysis is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • product-market-fit-analysis
  • AI & Agent Building
  • AI-coding skill

Product Market Fit Analysis by the numbers

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repo stars80
Last updatedMay 18, 2026
Repositorymanojbajaj95/gtm-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Product Market Fit Analysis

Comprehensive framework for assessing, achieving, and scaling product-market fit.

Quick Reference

SituationUse This Skill For
Measuring PMFSean Ellis Survey
Retention analysisRetention Curves
PMF validationLeading Indicators
Segment-specific PMFSegment Analysis
Scaling decisionsPost-PMF Strategy

---

Part 1: What Is PMF?

Definition

Product-market fit is the condition where a product satisfies a strong market demand. It's not binary — it's a spectrum.

Key Insight

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."

PMF Levels

LevelCustomersFocus
Nascent3-5Satisfaction
Developing5-25Demand
Strong25-100Efficiency
Extreme100+Scaling

---

Part 2: PMF Measurement Frameworks

Sean Ellis "Disappointment" Survey

The Question:

"How would you feel if you could no longer use this product?"
- Very disappointed
- Somewhat disappointed
- Not disappointed

The Benchmark:

40% "very disappointed" = on the right track

Focus on the "very disappointed" segment as the core value indicator.

Retention Curves

Uri Levine's Definition:

"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."

What to look for:

  • Curves that flatten over time (not decaying to zero)
  • "Smile curve" — engagement increases over time (strongest signal)

Key retention points:

  • Day 7 retention
  • Day 30 retention
  • Day 90 retention

Reference Customers

Christian Idiodi:

"The holy grail is really a reference customer - somebody who loves it enough to tell people about it."
MarketTarget References
B2B6-8 reference customers
B2C15-25 reference customers

---

Part 3: Leading Indicators

Signs of True PMF

IndicatorWhat It Means
"Very disappointed" > 40%Strong core value
Retention curve flatteningProduct creates ongoing value
Customer "pull"Market is pulling product
Outrage during outagesProduct is mission-critical
Customer driving next stepsIntent, not polite interest

Customer "Pull" Signals

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
  • Questions about pricing/timelines
  • Urgency in communication

Outage Response Test

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.

---

Part 4: Segment-Specific PMF

Key Insight

Karri Saarinen: "The way we think about it is, 'Do we have the fit in specific segments?' and how strong that fit is."

PMF exists in segments, not universally.

Finding Your Segment

Start narrow, then expand: 1. Find PMF in one segment (e.g., early-stage startups) 2. Double down where you see natural pull 3. Expand to adjacent segments

Common Segments

  • Company size / stage
  • Industry vertical
  • Use case / workflow
  • Geography
  • Team size

---

Part 5: Distribution + PMF

Critical Insight

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: 1. ✅ Retaining product 2. ✅ Scalable, built-in distribution

Without both, you don't have true PMF.

---

Part 6: Pre-PMF Navigation

What to Do Pre-PMF

  • Focus on retention, not acquisition
  • Talk to every customer personally
  • Iterate rapidly on product
  • Find the segment with strongest pull
  • Don't spend on paid acquisition

Warning Signs

  • Growth comes from "launch spikes" not organic
  • Users aren't coming back
  • No customers willing to be references
  • Market is pushing product, not pulling
  • You're guessing why users churn

---

Part 7: Post-PMF Scaling

Protecting PMF

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."

Scaling Checklist

  • [ ] Retention curve has flattened (positive)
  • [ ] Have target reference customers
  • [ ] Clear segment with strongest fit
  • [ ] Built-in distribution mechanism
  • [ ] Team structure supports growth

PMF Can Be Lost

Markets shift, competitors improve, user expectations rise. Continuously monitor and protect PMF.

---

Part 8: Questions to Assess PMF

Diagnostic Questions

1. If users couldn't use your product anymore, what percentage would be 'very disappointed'? 2. What does your retention curve look like at day 7, 30, and 90? 3. Do you have customers willing to be references and tell others about you? 4. Is the market pulling the product from you, or are you pushing it on them? 5. Are customers driving next steps or just being politely interested? 6. What specific segment do you have the strongest fit in?

---

Part 9: Common Mistakes

What to Avoid

MistakeReality
Confusing launch spikes with PMFSustained organic growth matters
Ignoring retention dataIf they don't come back, no PMF
Scaling too earlyPaid growth before PMF burns cash
Conflating TAM with PMFLarge market ≠ fit within it
Listening to "somewhat disappointed"Focus on "very disappointed"

---

Part 10: Decision Framework

Should You Scale?

SignalAction
40%+ "very disappointed" + flattening retentionReady to scale
< 40% "very disappointed"Keep iterating
No reference customersBuild them first
No distribution mechanismFind channels first

Scale vs. Iterate Decision

FactorScaleKeep Iterating
PMF survey> 40% very disappointed< 40%
RetentionCurve flatteningDecaying to zero
ReferencesTarget achievedNot yet
DistributionChannels identifiedUnknown

---

Related Skills

  • growth-strategy: For growth frameworks post-PMF
  • customer-success-and-retention: For retention improvement
  • conversion-rate-optimization: For activation optimization

Related skills

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