What does the CLV Calculator estimate?

It estimates the value of an average customer relationship with one transactional model: average purchase value × purchase frequency per year × customer lifespan in years gives revenue CLV, which is then margin-adjusted. There is no separate subscription (ARPU/churn) mode — lifespan is a direct input.

It does not perform cohort-level CLV from raw purchase logs, and it does not implement Pareto/NBD or BG/NBD probabilistic CLV models.

Can I verify it with a worked example?

Yes. With avg_purchase_value = $100, purchase_frequency_per_year = 4, customer_lifespan_years = 2.5, gross_margin_pct = 30, acquisition_cost = $150: annual value = $400, revenue CLV = $1,000, margin-adjusted CLV = $300, LTV:CAC = 2.0, payback ≈ 15 months.

Methodology: CLV Calculator

1. Scope

The CLV Calculator estimates the revenue and gross profit an average customer generates, using a single repeat-purchase model: average purchase value × purchase frequency × customer lifespan, then margin-adjusted. It does not have a subscription (ARPU / churn) mode, does not discount future cash flows to net present value, and does not run probabilistic cohort models (Pareto/NBD, BG/NBD).

2. Inputs and outputs

Inputs: average purchase value, purchase frequency per year, customer lifespan in years (entered directly, not derived from churn or retention), acquisition cost (CAC), and gross margin (%).

Outputs: annual value per customer, monthly value, lifetime revenue (CLV), margin-adjusted CLV (gross-profit lifetime value), the LTV:CAC ratio, and CAC payback in months.

Engine source: src/lib/customer-lifetime-value-calculator/engine.ts.

3. Formula / scoring logic

annual_value        = avg_purchase_value * purchase_frequency_per_year
monthly_value       = annual_value / 12
clv                 = annual_value * customer_lifespan_years        # lifetime revenue
margin_adjusted_clv = clv * (gross_margin_pct / 100)               # gross-profit LTV
ltv_cac_ratio       = margin_adjusted_clv / acquisition_cost
payback_months      = acquisition_cost / (monthly_value * gross_margin_pct / 100)

4. Assumptions

Customer lifespan is a direct input. You enter the average number of years a customer stays; the tool does not derive it from churn or a retention rate.
Constant purchase value and frequency. Average purchase value and purchases per year are held flat across the whole lifespan.
Gross margin is variable-cost margin. Include inference, hosting, payment-processing, fulfilment. Exclude fixed overhead.
No discounting. Lifetime value is undiscounted — a dollar in year three counts the same as a dollar today.
No expansion revenue. Upsells, cross-sells, and price-tier migrations must be folded into the average purchase value.

5. Data sources

The tool ingests no external data — every figure is user-entered. The formula is the standard repeat-purchase lifetime-value model taught in marketing and managerial-accounting curricula. Probabilistic CLV model references, for readers who need cohort-level CLV:

Schmittlein, Morrison, Colombo — Counting Your Customers: Who Are They and What Will They Do Next? Management Science, 1987 (Pareto/NBD).
Fader, Hardie, Lee — "Counting Your Customers" the Easy Way: An Alternative to the Pareto/NBD Model, Marketing Science, 2005 (BG/NBD).

6. Known limitations

Aggregate, not cohort-level. The formulas treat all customers as equivalent. For a business with heterogeneous cohorts (different channels, tiers, regions), aggregate CLV misleads — compute per-cohort CLV from timestamped data.
Fixed lifespan is a simplification. A single average lifespan hides the spread between one-time buyers and loyalists. If your retention curve is steep early and flat later, a flat average under-states the sticky tail.
No customer-heterogeneity modelling. Probabilistic CLV (Pareto/NBD, BG/NBD) accommodates heterogeneous purchase rates and drop-out — out of scope here.
The Reichheld "5% retention lift = 25–95% profit boost" claim is context-dependent. It applies to businesses where retention is a major profit driver and cost-to-serve declines with tenure. We do not use it as a benchmark.

7. Reproducibility

Input
avg_purchase_value = $100, purchase_frequency = 4/year, customer_lifespan = 2.5 years, gross_margin = 30%, CAC = $150.

Expected output
annual_value = $400, monthly_value ≈ $33.33. clv (revenue) = $1,000 (= $400 × 2.5). margin_adjusted_clv = $300 (= $1,000 × 0.30). LTV:CAC = 2.0 (= $300 / $150). CAC payback ≈ 15 months (= $150 / ($33.33 × 0.30)).

8. Change log

2026-04-24methodology page first published. Subscription and transactional modes documented with limits.

Worked example

Run live against the same engine this site ships (/engines/customer-lifetime-value-calculator.js). The inputs and outputs below are recomputed on every build and independently re-verified in CI — they are never hand-authored.

Input

tool: customer_lifetime_value
avg_purchase_value: 50
purchase_frequency_per_year: 4
customer_lifespan_years: 3
acquisition_cost: 100
gross_margin_pct: 60

Output

clv: 600
annualValue: 200
monthlyValue: 16.666666666666668
marginAdjustedClv: 360
ltcRatio: 3.6
paybackMonths: 10

Frequently asked questions

What does the CLV Calculator estimate?: It estimates the value of an average customer relationship with one transactional model: average purchase value × purchase frequency per year × customer lifespan in years gives revenue CLV, which is then margin-adjusted. There is no separate subscription (ARPU/churn) mode — lifespan is a direct input.
What does it not do?: It does not perform cohort-level CLV from raw purchase logs, and it does not implement Pareto/NBD or BG/NBD probabilistic CLV models.
Can I verify it with a worked example?: Yes. With avg_purchase_value = $100, purchase_frequency_per_year = 4, customer_lifespan_years = 2.5, gross_margin_pct = 30, acquisition_cost = $150: annual value = $400, revenue CLV = $1,000, margin-adjusted CLV = $300, LTV:CAC = 2.0, payback ≈ 15 months.