Product-Market Fit Assessment
Evaluate PMF using multiple frameworks
- • Review survey results, retention, and economics, including where the signals disagree.
- • Cohorts over averages: flattening point, natural usage frequency, and where the curve actually stabilizes.
- • Segment truth: PMF can be strong for one segment and weak elsewhere—say it out loud.
- • Metrics with baselines: payback, LTV/CAC, win rate, and pipeline quality.
- • A clear “what now”: if no PMF, narrow; if close, fix blockers; if strong, scale with guardrails.
- • Declaring PMF on top‑line growth while cohorts quietly decay.
- • Using averages that hide power users vs. tourists; segment or you’ll fool yourself.
- • Surveying only happy users; selection bias turns everything into a victory lap.
- • Hand‑waving economics: “We’ll make it up in volume” is not a plan.
- • An assessment without a decision or a plan to resolve uncertainty.
What’s the quickest sanity check for PMF?
Review the Sean Ellis survey, cohort retention, and unit economics together. The 40% very-disappointed threshold is a heuristic, not proof of fit. Match retention windows and payback expectations to your product. See the Superhuman founder’s account for an example of the survey approach.
Our top‑line is growing fast. Is that PMF?
Growth can come from acquisition even when retention is weak. Compare cohorts and acquisition sources before drawing a conclusion about fit.
How do I run the Sean Ellis survey without bias?
Define eligible users before sampling, seek responses beyond your strongest advocates, and report the response count. Add a follow-up asking why and compare segments.
What’s a good Day‑30 retention number?
Use the product’s natural usage frequency and a clearly defined cohort. Day-30 retention for a daily product is not directly comparable with a weekly or occasional-use product.
Our NPS is 60 but retention stinks. What gives?
An NPS of 60 and weak retention can coexist. Check who answered the survey, when they answered, and whether their subsequent behavior matches the score.
What economics scream “not ready to scale”?
Review payback against available cash, retention trends, discounting, and dependence on a few large accounts. A single threshold cannot determine readiness to scale.
Do I need PMF for every segment?
You can start with one segment where the evidence for value and retention is strong. Report its results separately instead of assuming they apply to every segment.
What if we’re “close” to PMF—what’s the play?
Identify the gaps in activation or repeat use and test focused changes. Expand to other segments when the evidence supports doing so.
Executive wants to scale now. Should we?
Show the survey results, retention cohorts, unit economics, and remaining uncertainty. Compare the risks of scaling with the evidence that further research or product work could provide.
How do we present PMF credibly without a 40‑slide deck?
One‑pager: survey result + why, cohort chart with flattening point, 3 economics bullets, and a call—no PMF/approaching/strong—with 3 concrete next steps, owners, dates. That’s it.
When to use it
Evaluating readiness to scale
Before you use the output
- •Fill in the variables with the facts and constraints you have.
- •Check the output against your source material and revise any mistakes.
- •Enable web search where available and check the dates and sources it returns.
Expected output
PMF assessment with recommendations