Compute customer lifetime value (LTV), realized or predicted, by cohort or segment, with optional gross-margin and discount-rate adjustments. Use when the user asks "what is a customer worth", wants to size a CAC budget, or compares channels by long-term value.
Instalación
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Compute customer lifetime value (LTV), realized or predicted, by cohort or segment, with optional gross-margin and discount-rate adjustments. Use when the user asks "what is a customer worth", wants to size a CAC budget, or compares channels by long-term value.
Decide which LTV
Realized (historical) — actual revenue from a closed cohort. Bounded but truthful.
Predicted (forward-looking) — extrapolate from retention and ARPU curves. Useful for live cohorts and budgeting; sensitive to assumptions.
Cohort-based realized: LTV(t) = sum of revenue from cohort across [0, t] / cohort size
Discounted: apply a monthly/annual discount factor to future revenue (typical: 10–15% annual)
Always do
Slice by cohort — LTV from 5 years ago does not predict LTV today. Show LTV by acquisition month/quarter.
Show the curve, not just a point — LTV at month 6, 12, 24, 36 — so the reader can see whether it's still growing.
Use gross margin, not revenue — LTV that ignores cost of serving the customer is overstated.
Pitfalls
Truncated cohorts — recent cohorts haven't aged; comparing their LTV to old cohorts at the same calendar date is invalid. Compare at the same cohort age.
Immortal-customer assumption — ARPU / churn_rate implicitly assumes constant churn forever. Check whether churn is stable or rising.
Pricing changes — if pricing changed mid-cohort, LTV shifts mechanically; segment by pricing era.
Mixing one-time and recurring — separate transactional revenue from subscription revenue when they behave differently.
