C4 · Economics & metrics · model-dependent

Customer Lifetime Value

Also: LTV

Definition. LTV is the present value of all revenue (or margin) expected from a customer over the lifetime of the relationship. The naive formula (ACV / churn rate) assumes a geometric survival function; the sBG (shifted Beta-Geometric) model by Fader and Hardie corrects for heterogeneous churn and typically reduces naive LTV estimates by 20–60%.

model-dependent — sBG requires sufficient cohort data; naive formula is overconfident Last updated 2026-06-18 Source: Fader & Hardie (2007) 'How to Project Customer Retention'; OpenView SaaS Benchmarks 2024; Bessemer Venture Partners State of the Cloud 2024

Formula

Customer Lifetime Value model-dependent

L T V_{naive} L T V_{sBG} = \frac{A C V}{c} = A C V t = 1 \sum \infty S (t ∣ α, β)

Plain English: LTV (naive) = ACV / annual churn rate

Notation: LTV_naive = ACV / c; LTV_sBG = ACV × Σ_{t=1}^{∞} S(t|α,β) where S(t) is the sBG survival function with parameters α, β fit to observed cohort data

Benchmark by stage

Source: Fader & Hardie (2007) 'How to Project Customer Retention'; OpenView SaaS Benchmarks 2024; Bessemer Venture Partners State of the Cloud 2024

Stage	Customer Lifetime Value	Notes
SMB (naive LTV)	3–5× ACV	Annual churn 20–30% implies 3–5 year naive lifetime; sBG correction typically 20–40% lower
Mid-market (naive LTV)	5–8× ACV	Annual churn 12–20%; sBG correction 15–30% lower than naive
Enterprise (naive LTV)	8–15× ACV	Annual churn 5–10%; long tails make sBG correction critical — can exceed 40%
Best-in-class gross margin LTV	LTV:CAC > 3:1 on gross margin	Use gross-margin-adjusted LTV (LTV × GM%) for ratio calculations

Naive vs corrected

Version	Formula
Naive	LTV = ACV / churn_rate — assumes all customers have identical, constant churn probability each period (geometric survival), which overestimates lifetime for heterogeneous customer bases
Corrected	sBG model (Fader & Hardie, 2007): fit Beta distribution parameters α and β to observed cohort survival data; S(t\|α,β) = B(α, β+t)/B(α,β) where B is the Beta function. Requires at least 2–3 cohort vintage years of data to fit reliably.

Common errors

Using aggregate churn rate rather than cohort-level survival data (masks early-period dropout)
Not discounting future cash flows to present value (overstates LTV in high-discount-rate environments)
Using revenue LTV rather than gross-margin LTV in LTV:CAC comparisons
Assuming constant churn rate (geometric model) when actual churn is front-loaded
Not separating LTV by ICP segment — blended LTV averages hide wide variance across customer types

Where this sits

Part of the Economics & metrics (C4) cluster in the GTM World Model. Related to the model's "LTV_sBG = ACV × [α/(α+β)] × [1 + β/(α+β+1) + β(β+1)/((α+β+1)(α+β+2)) + ...]; simplifies to ACV × α/(α+β-1) when α > 1, β > 1" equation.

cac →ltv cac ratio →nrr →grr →

How to cite this

@misc{shalvi_gtm_metric_ltv_2026,
  author = {Singh, Shalvi},
  title  = {Customer Lifetime Value — GTM World Model Metrics},
  year   = {2026},
  url    = {https://shalvisingh.com/gtm/metrics/ltv}
}

Singh, Shalvi. "Customer Lifetime Value — GTM World Model Metrics." shalvisingh.com, 2026. https://shalvisingh.com/gtm/metrics/ltv