Product-Market Fit Assessment
Evaluate PMF using multiple frameworks
- • Multiple lenses, one story: survey signal, retention reality, and economics—all pointing the same way.
- • 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.
- • Hard numbers with baselines: payback, LTV/CAC, win rate, and pipeline quality—not vibes.
- • 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.
- • No next step. PMF assessment without decisions is theater.
What’s the quickest sanity check for PMF?
Three things: 1) Sean Ellis >40% “very disappointed” for your core segment, 2) Day‑30 retention curve that flattens above “tourist” levels, 3) Healthy unit economics trend (payback < 12 months moving down). If you only have one of these, you don’t have PMF—you have a good week.
Our top‑line is growing fast. Is that PMF?
Maybe. Or maybe paid is doing all the work and cohorts leak like a sieve. Plot cohorts by signup month. If they don’t flatten, you’re pouring water into a colander. Fix activation/retention first.
How do I run the Sean Ellis survey without bias?
Random sample, recent active users, include mild/negative voices, and don’t bury the question. Add a “Why?” free‑text and segment responses. If only your champions answer, congrats—you measured fandom, not PMF.
What’s a good Day‑30 retention number?
Depends on natural usage frequency. For weekly tools, a healthy flattening might be 20–30% WAU returning at Day‑30; for daily tools, higher. Don’t chase someone else’s benchmark—anchor to your job‑to‑be‑done and power user pattern.
Our NPS is 60 but retention stinks. What gives?
You’re probably surveying the choir or solving a real pain that isn’t urgent/recurring. NPS is a supporting signal. Retention is the truth serum. Fix repeatable value delivery before marketing tries to outrun churn.
What economics scream “not ready to scale”?
Payback > 18 months with declining retention, LTV/CAC < 2, heavy discounting to close, and expansion driven by one whale. That’s a brake, not a gas pedal.
Do I need PMF for every segment?
No. You need a beachhead where usage is natural and referrals happen without bribery. Document who that is and stop averaging them with weak segments. Focus wins.
What if we’re “close” to PMF—what’s the play?
Nail activation and a killer “aha.” Ruthlessly remove friction in the first session/week. Ship small bets that make repeat value obvious. Expand segments later. Ask me how I know.
Executive wants to scale now. Should we?
Show the PMF scorecard and cohort plot. If curves don’t flatten and payback hurts, every extra dollar just buys faster churn. Scale learning, not ad spend—for now.
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
Evaluating readiness to scale
Pro Tips
- •Be specific with your variable inputs for better results
- •Review and iterate on the AI output as needed
- •Enable web search for the most current information
Expected Output
PMF assessment with recommendations