Calculator inputs
Use the market signals below. The form uses a three-column layout on large screens, two columns on smaller screens, and one column on mobile.
Example data table
This example shows one realistic scenario and the resulting normalized scores.
| Metric | Example Input | Normalized Score | Weight | Weighted Contribution |
|---|---|---|---|---|
| Sean Ellis Signal | 42% very disappointed | 100.00 | 25 | 25.00 |
| 30-Day Retention | 34% | 85.00 | 18 | 15.30 |
| NPS | 36 | 68.00 | 12 | 8.16 |
| CSAT | 82% | 82.00 | 10 | 8.20 |
| Activation Rate | 58% | 58.00 | 10 | 5.80 |
| WAU/MAU Engagement | 0.54 | 90.00 | 10 | 9.00 |
| Referral Rate | 12% | 60.00 | 8 | 4.80 |
| MRR Growth | 9% | 76.00 | 7 | 5.32 |
| Example PMF Score | 81.58 | |||
Formula used
Main scoring formula
Each metric score is normalized to a 0 to 100 scale first. The final score remains on a 0 to 100 scale.
Metric normalization rules
Score interpretation
- 80 to 100: Strong product-market fit
- 65 to 79.99: Emerging product-market fit
- 50 to 64.99: Partial product-market fit
- Below 50: Early or weak product-market fit
How to use this calculator
- Enter your survey and behavior metrics from the same period.
- Check that weekly active users are not higher than monthly active users.
- Adjust the weights if your business model values some signals more heavily.
- Click Calculate PMF Score to generate the score, chart, and breakdown.
- Review your weakest components first. They often show the real bottlenecks.
- Export the results to CSV or PDF for team review, reporting, or planning.
Frequently asked questions
1. What does a product-market fit score show?
It summarizes how strongly your product matches customer demand using survey, retention, engagement, referral, and revenue signals. It is a directional decision tool, not a universal truth.
2. Why is the Sean Ellis signal included?
The “very disappointed” question is a widely used market-fit signal. If many users would miss the product deeply, your offering is usually solving a meaningful problem.
3. Can I change the metric weights?
Yes. The calculator lets you change every weight. It automatically normalizes them, so the score still works even when the weights do not total exactly 100.
4. What is a good WAU/MAU ratio?
A higher ratio generally means users return often. This calculator uses 0.60 as a strong benchmark for habit strength, but your ideal level depends on the product category.
5. Why can a strong growth rate still produce a weak score?
Growth alone can hide weak retention or poor satisfaction. If acquisition is strong but users do not stay, the broader product-market fit picture remains fragile.
6. Should I use one period for every input?
Yes. Use the same timeframe whenever possible. Mixing different periods can make the score misleading because behavior, satisfaction, and growth may describe different business conditions.
7. Is this calculator useful for early-stage products?
Yes. Early teams can use it to compare hypotheses, segments, onboarding changes, or pricing experiments. The score becomes more reliable as sample size and data quality improve.
8. Can I use this score for board or investor reporting?
Yes, as a supporting metric. It works best beside cohort retention, revenue quality, activation, and customer research instead of replacing those deeper analyses.