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.
Installation
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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.
