Customer lifetime value and LTV:CAC calculator
Compare modeled customer contribution to acquisition cost.
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How it works
Model customer lifetime contribution and compare it with acquisition cost under explicit retention assumptions. The finite projection weights each future month by the probability that a customer remains active. The long-run option extends the same constant-churn model indefinitely. Contribution LTV differs from lifetime revenue because the entered variable cost margin is applied before comparing with CAC.
A few things to know
A constant-probability model, not observed customer lifetime value or a valuation. Assumes contribution in month 1 before the first churn opportunity. Excludes reactivation, expansion, seasonality, discounting, changing margins, fixed costs and taxes. Customer churn must be monthly and between 0% and 100%; negative net revenue churn is not a valid survival probability. Zero CAC has no defined LTV:CAC ratio. Zero churn with positive contribution has no finite infinite-horizon LTV; use a finite horizon for a bounded scenario. Monetary values use one currency.
Results use your inputs. Keep all money amounts in the same currency.
When to use this tool
Customer lifetime value becomes more useful when its retention assumptions are visible. This calculator models contribution from an active customer month by month, weights future contribution by a constant churn probability, and compares the result with acquisition cost. Choose a finite horizon for a bounded planning scenario or inspect the theoretical long-run model without treating it as a guaranteed lifetime forecast.
- Compare acquisition costs with expected customer contribution over a defined planning horizon.
- See how a change in monthly customer churn affects a subscription cohort’s modeled value.
- Separate contribution-based LTV from a lifetime-revenue estimate that ignores the cost of serving customers.
How to use customer lifetime value and ltv:cac calculator
Enter customer revenue and margin
Use monthly revenue per active customer and the contribution margin remaining after the variable costs you include. Enter acquisition cost separately; do not subtract it once in the margin and again as CAC.
State the retention assumption
Enter monthly customer churn between 0% and 100%. The model assumes the customer earns month 1 contribution and can leave at each month end. Revenue churn or negative net churn is not a customer survival probability.
Choose the comparison horizon
Set a 1–120 month projection and choose finite or theoretical long-run LTV for the ratio. The downloadable table always covers the finite horizon, making the difference between a bounded scenario and the long-run estimate explicit.
A 12-month contribution model
Inputs
Monthly revenue per active customer 60; contribution margin 50%; monthly customer churn 10%; CAC 100; horizon 12 months; finite basis.
Result
Monthly active-customer contribution is 30. Expected 12-month contribution is 215.27. Modeled LTV:CAC is 2.1527:1, and modeled value after CAC is 115.27. The theoretical long-run contribution is 300, or 3:1 against the same CAC.
What it means
Future months receive declining survival weights. The finite result therefore falls below 12 × 30 = 360, which would assume every customer remains active throughout. Neither result deducts fixed overhead or the time value of money.
Common mistakes to avoid
Using net revenue churn as customer churn
Net revenue churn can be negative because of expansion. That is not a probability of a customer leaving. Use customer churn for survival and model expansion separately when your business requires it.
Presenting the long-run estimate as observed value
A constant-churn formula extends today’s assumptions indefinitely. Actual cohorts can have changing retention, pricing and servicing costs. Compare the model with realized cohort contribution before using it to set budgets.
Subtracting acquisition cost twice
The contribution margin here is before acquisition. CAC is deducted separately for the value-after-CAC result. A margin that already includes acquisition spending will understate the modeled comparison.
Frequently asked questions
Definitions, edge cases, and practical next steps for this tool. Search all tool FAQs ↗
How does this calculator model customer lifetime contribution?
It multiplies monthly revenue by contribution margin, then weights month t by the probability the customer is still active at that month’s start. The first month has a weight of 1. With 10% monthly churn, the following weights are 0.9, 0.81 and so on.
What is the difference between finite and long-run LTV?
Finite LTV sums expected contribution over the entered horizon. The long-run model extends the same geometric survival pattern indefinitely; with positive churn it equals monthly contribution divided by monthly churn as a decimal. The selected basis determines the headline LTV:CAC ratio, while the table remains finite.
Why is zero-churn long-run LTV unbounded?
At zero churn the model keeps every customer active forever. With positive monthly contribution, adding an unlimited number of identical months cannot produce a finite sum. The tool labels this unbounded assumption directly. Select a finite horizon instead when you need a bounded planning scenario.
What happens with 100% monthly churn?
The timing assumption gives the customer month 1 contribution before the first month-end churn opportunity. At 100% churn there is no contribution from month 2 onward. With revenue 60 and margin 50%, modeled contribution is therefore 30, regardless of a longer finite horizon.
Is this lifetime revenue or profit?
It is modeled contribution before acquisition spending. The entered margin deducts the variable costs you choose, so it differs from pure lifetime revenue. Fixed overhead, taxes and discounting are not modeled. The separate value-after-CAC metric subtracts acquisition cost but still is not a full company profit forecast.
What does an undefined LTV:CAC ratio mean?
CAC of zero leaves no positive denominator for the ratio, even if modeled contribution is positive. Likewise, an unbounded long-run LTV has no finite ratio. The calculator does not manufacture an infinite performance score. You can still inspect the finite contribution projection and its assumptions.
Can I use annual churn in the monthly churn field?
No. This model applies the entered probability at each month end. For a constant survival assumption, an annual customer churn rate a corresponds to monthly churn 1 − (1 − a)^(1/12), not a divided by 12. Prefer measured monthly cohort behavior when it is available.
What is a good LTV:CAC ratio for my business?
A single universal target would ignore the horizon, margin definition, collection timing and uncertainty in retention. Compare like-for-like cohorts and stress-test churn and cost assumptions. Use CAC payback alongside this result to understand how long recovery takes, and cash runway to judge whether the business can fund that wait.
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Sources and method
Use the references below to check definitions and platform guidance. The model’s scope and limits are explained above. Read our editorial standards.