ltv-analysis

name	ltv-analysis
description	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.

Standard formulas

• Simple SaaS: LTV = ARPU × gross_margin / churn_rate
• 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

1. Slice by cohort — LTV from 5 years ago does not predict LTV today. Show LTV by acquisition month/quarter.
2. Show the curve, not just a point — LTV at month 6, 12, 24, 36 — so the reader can see whether it's still growing.
3. 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.