Cohort analysis playbook: retention curves, LTV forecasting, and payback

A practical cohort analysis workflow: build retention curves, forecast LTV, and translate retention quality into payback and growth decisions.

Updated 2026-01-28

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Retention Curve Calculator

Retention curve example (2% monthly churn, $800 ARPA, 80% margin, 36 months)

Why cohorts matter

Blended churn hides the truth. Cohorts show where customers fall off (activation, onboarding, pricing fit) and whether newer cohorts are improving.

A repeatable cohort workflow

Start with logo retention by cohort start month; then add revenue retention (GRR/NRR) for expansion effects.
Look for the biggest early drop (month 1-2) and the long-run slope (steady-state churn).
Translate retention into unit economics: retention -> LTV -> payback -> scale constraints.

What to segment (minimum set)

Plan/price point (pricing fit).
Acquisition channel (growth quality).
Customer size/use case (product fit).

Common mistakes

Forecasting LTV from a short window without recognizing churn decay or seasonality.
Mixing logo churn and revenue churn without stating which one is used.
Using NRR alone and missing logo churn (NRR can look great while the base erodes).

FAQ

Should I use GRR or NRR for LTV-

For conservative planning, use GRR-driven retention (no expansion). Use NRR for growth planning only when expansion is durable and repeatable for your segment.

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Two-stage churn: modeling early drop-off vs steady-state retention

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