Product-Market Fit Scoring Model
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Determine the probability and score of achieving product-market fit based on NPS and cohort retention data.
NOTEAbout This Model
## Determine the probability and score of achieving product-market fit based on NPS and cohort retention data. This model estimates **Adjusted Pmf Score** based on uncertain inputs: Nps Mean, Nps Variance, Retention Cohort 3 Month. Run the simulation to see the distribution of possible outcomes.
VARIABLENps Mean
poisson(50)
Net Promoter Score average, scaled 0-100 logic. Represented as non-negative count for division in equation.
nps_mean
VARIABLERetention Cohort 3 Month
beta(7.5, 12.5)
Long-term retention plateau is the strongest PMF signal. Expressed as a decimal.
retention_cohort_3_month
VARIABLENps Variance
beta(6, 14)
Standard deviation/spread of NPS scores as a percentage of the total scale. High variance reduces confidence.
nps_variance
FORMULAPmf Score Index
(nps_mean / 100) * 0.4 + (retention_cohort_3_month * 0.6)
Weighted product-market fit indicator combining NPS and retention stability.
pmf_score_index
FORMULAAdjusted Pmf Score
pmf_score_index * (1 - nps_variance)
Risk factor based on NPS variance; higher variance penalizes the score stability.
adjusted_pmf_score
OUTPUTAdjusted Pmf Score
adjusted_pmf_score
Composite product-market fit score. Higher values indicate stronger fit. Scores above 40 (the Sean Ellis test threshold) suggest you have found product-market fit worth scaling.
What is Carlo?
Carlo is a visual tool for Monte Carlo simulation. Model uncertainty by dragging probability distributions, connecting them visually, and running thousands of scenarios instantly.