Customer lifetime value, often shortened to CLV or LTV, estimates the economic contribution a customer relationship is expected to generate over a defined horizon. A practical model uses customer revenue minus the variable costs required to serve that customer, then accounts for purchase frequency and relationship duration or retention. The formula and time horizon must be stated because different models can produce very different answers.
How do you calculate customer lifetime value?
Start with the decision the estimate needs to support. For a repeat-purchase business, a simple model can multiply average contribution per order by expected orders per customer during a defined horizon. For a subscription, the model may use contribution per period and the expected number of retained periods. A contract business may model expected contribution across signed and realistically renewable terms.
Prefer contribution to gross revenue. Subtract variable delivery, payment, fulfillment, support, and other costs that rise with serving the customer. Keep acquisition cost separate when comparing CLV with CAC unless the model explicitly labels a net value after acquisition. Apply one definition consistently across cohorts and show the underlying revenue, margin, frequency, retention, and horizon assumptions.
- Choose a finite forecast horizon when the relationship lifetime is uncertain.
- Use contribution margin rather than revenue when service costs are material.
- Calculate by cohort or segment when customer behaviour differs.
- Replace assumptions with observed retention and purchase data as it accumulates.
How should CLV be used with CAC?
Compare values measured on compatible boundaries. If CLV is contribution over twelve months, compare it with the acquisition cost of customers entering the same cohort and allow for the time needed to earn that contribution. A ratio can be a useful summary, but it can hide slow payback, uncertain renewals, or wide differences between customer segments.
Use the estimate to test acquisition, pricing, onboarding, and retention choices. A channel with a higher CAC may still be attractive when its customers retain longer and contribute more. Conversely, a high projected CLV should not justify unlimited acquisition spending when the forecast relies on immature cohorts or optimistic renewal assumptions.
Which assumptions deserve the most scrutiny?
Relationship duration is often the most sensitive input. Do not turn one early retention rate into an indefinite lifetime without testing how cohorts change. Average order value can also conceal a few very large customers, and average margin can conceal expensive service needs. Show a conservative, expected, and optimistic case when uncertainty could change the decision.
CLV is an estimate, not cash already earned. Track actual cohort revenue, contribution, retention, refunds, expansion, and service cost against the forecast. Update the model when the product, pricing, customer mix, or delivery model changes, and keep the prior assumptions so decision-makers can understand why the estimate moved.
A fictional contribution-based CLV estimate
A subscription service earns ₹6,000 per customer each month and incurs ₹2,000 in variable delivery and support costs, leaving ₹4,000 monthly contribution. Based on comparable mature cohorts, it plans with an expected retained duration of 15 months. Estimated CLV is ₹4,000 multiplied by 15, or ₹60,000 before acquisition cost. If fully loaded CAC is ₹20,000, the model indicates ₹40,000 of contribution after acquisition before fixed costs, tax, financing, and forecast error. The team also tests twelve- and eighteen-month scenarios and compares the estimate with actual cohort results. The figures are illustrative, not a target.