Product-Market Fit Assessment
Business Planning & Strategy
Quick Answer
Enter your retention data, growth metrics, and customer feedback signals. Paste into Claude or ChatGPT. Receive an honest PMF assessment with score and specific improvement actions.
What You Get
A structured PMF assessment covering retention analysis, growth signal evaluation, customer sentiment review, and a PMF score with specific evidence for and against fit plus the top 3 actions to improve PMF.
Who Is This For
Founders evaluating whether their product has achieved PMF, startup teams deciding whether to scale or iterate, and investors assessing PMF signals in portfolio companies.
About This Template
Product-market fit is the single most important milestone for any startup, yet most founders are uncertain whether they have achieved it. This template guides you through the key signals that indicate PMF — retention rates, organic growth, customer sentiment, NPS, and feature usage patterns — and uses AI to produce an honest PMF assessment with a specific score, the evidence for and against PMF, and the highest-leverage actions to strengthen fit.
Fill In Your Details
What your product does and who it serves
e.g. 92 percent monthly revenue retention, 78 percent monthly active user retention
Word of mouth referrals, organic signups, unsolicited press mentions
NPS score if known, or describe common customer feedback themes
How often do customers use your product? Which features do they use most and least?
Top reasons customers cancel or stop using the product
Best things customers have said about your product — their exact words if possible
e.g. 6 months since launch, 18 months since first paying customer
Gather these details then use them to fill in the prompt below.
AI Prompts
Generate PMF assessment
Paste this prompt into Claude or ChatGPT with your data filled in.
You are a startup advisor conducting a product-market fit assessment. Using the data below, produce an honest and specific PMF assessment. Product: [product_description] Monthly retention: [monthly_retention] Organic growth signals: [organic_growth] NPS or sentiment: [nps_or_sentiment] Usage patterns: [usage_patterns] Churn reasons: [churn_reasons] Customer quotes: [customer_quotes] Time in market: [time_in_market] Produce: 1. PMF Score (1-10 with specific rationale) 2. Strong PMF Signals (evidence that PMF exists) 3. Weak PMF Signals (evidence that PMF is not yet achieved) 4. Retention Analysis (interpret the retention numbers in context) 5. The Core PMF Gap (the single most important thing preventing stronger PMF) 6. Top 3 Actions to Strengthen PMF (specific and achievable in 90 days) Be honest. A false positive PMF assessment that leads a founder to scale prematurely is more damaging than a hard truth. Use the Sean Ellis benchmark: strong PMF typically shows 40 percent or more of users would be very disappointed if the product disappeared.
Sample Output
This is an example of what AI produces when you use this template.
PMF Score
PMF Score: 6.5/10 — Early PMF signals are present but not yet strong enough to support aggressive scaling. The 92 percent monthly revenue retention is excellent and the strongest signal of value delivery. However the weak organic growth and high churn in the SMB segment indicate the product is delivering strong value to a specific sub-segment (enterprise) but not yet to the broader target market.
Core PMF Gap
The core PMF gap is segment mismatch. The retention and NPS data suggest strong PMF with enterprise customers (500+ employees) but weak fit with the SMB segment (under 50 employees) that represents 70 percent of your current customer base. The SMB churn reasons — too complex to set up, too expensive for the value — indicate the product is not designed for their workflow. The highest-leverage action is to either simplify aggressively for SMB or focus exclusively on the enterprise segment where you demonstrably have fit.