Deconstructs customer cohort behavior with retention curves, LTV progression, and vintage-over-vintage comparison analysis. Use when analyzing cohort data, assessing customer quality, or modeling lifetime value trajectories.
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Deconstructs customer cohort behavior with retention curves, LTV progression, and vintage-over-vintage comparison analysis for growth equity and expansion capital due diligence.
Normalize cohort data — Align all cohorts to a common T+0 starting point. Index each cohort's revenue to 100 at month 0 (or first full month). Confirm cohort definitions are consistent (e.g., sign-up date vs. first invoice date).
Build retention curves — Plot logo retention and net dollar retention for each cohort on a common time axis. Calculate:
Calculate LTV progression — For each cohort, compute cumulative revenue per customer at each period. Estimate terminal LTV using observed retention rates extrapolated to steady state. Compare LTV to CAC to derive payback period and LTV/CAC ratio.
Run vintage-over-vintage comparison — Overlay cohort curves to identify whether newer cohorts retain better or worse than older ones. Flag any cohort that deviates materially (>5pp retention difference at equivalent maturity). Investigate drivers: product changes, customer mix shift, pricing, or macro factors.
Segment and decompose — Cut cohorts by customer size, channel, or product to isolate whether retention trends are broad-based or driven by a specific segment. Identify if enterprise vs. SMB mix shifts explain apparent retention improvement.
Assess unit economics trajectory — Determine whether LTV/CAC is improving, stable, or degrading. Evaluate whether expansion revenue is masking underlying churn problems (GDR vs. NDR divergence). Calculate implied steady-state NDR at maturity for recent cohorts.
Stress-test management projections — Compare management's forward revenue model against actual cohort behavior. Apply the most recent cohort's retention curve (rather than blended historical) to new customer additions to build a bottom-up revenue bridge.
Structure the analysis report with:
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