Customer lifetime value (CLV or LTV): formula and example

Customer lifetime value (CLV or LTV) summarizes the revenue or gross margin associated with a customer over a defined relationship. The figure can describe purchases already recorded or estimate future purchases. State which one you calculated; they answer different questions.

Observed value: add up what happened

For a cohort and observation window, divide the cohort’s included revenue by the number of customers in it. If you want gross margin, apply the defined margin to that revenue before dividing. Keep customers who did not buy again in the starting cohort; removing them would raise the average without describing what happened to the whole group.

Imagine a fictional cohort of 8 customers observed during their first 12 months. Each customer made an average of 4 orders at CLP 36,000. In this example, that amount is product revenue after discounts and returns, before taxes and shipping. That is 32 orders and CLP 1,152,000 in eligible sales:

Historical revenue CLV = CLP 1,152,000 ÷ 8 customers = CLP 144,000 per customer over the first 12 months.

If the defined gross margin on those products is 55%, the observed gross margin is CLP 144,000 × 0.55 = CLP 79,200 per customer over the same window. The 55% is an assumption in this example. Here it means sales less direct product cost, before acquisition, support, operating expenses, or taxes. Changing the costs included changes the margin and the result.

Estimated value: project future purchases

For a store with repeat orders, one simple estimate is:

Estimated revenue CLV = average order value × orders per customer per year × estimated relationship years.

This formula follows individual orders. For a subscription, separately define recurring revenue, margin, the time period, and how you estimate departures; do not interchange the two calculation bases.

Using the same fictional case: CLP 36,000 per order × 4 orders per customer per year × 2 years = CLP 288,000 in estimated revenue per customer over two years. At the same assumed 55% gross margin: CLP 288,000 × 0.55 = CLP 158,400 in estimated gross margin per customer.

The purchase frequency and two-year relationship are inputs to the projection, not observed results for those eight customers. Estimating relationship length takes cohorts with enough history; the observation window should match the business’s buying cycle. This multiplication also does not discount future money or subtract acquisition, support, or other expenses. It is not cash available today or final profit. A Management Science study estimates purchase timing and amounts alongside the risk that a customer stops buying; its membership-based direct-marketing case shows why a longer relationship does not guarantee higher CLV. Read the study abstract.

State the basis before comparing values

Record these details next to each CLV:

• Cohort and window: which customers count and how long you observed their purchases.
• Included value: sales before or after discounts, returns, or other adjustments, according to your stated rule.
• Margin: which direct costs it subtracts and which expenses remain outside the calculation.
• Projection: purchase frequency, relationship length, and the data cutoff behind those inputs.

Do not compare historical revenue CLV with a gross-margin projection. Do not compare groups with different customer ages as if they had the same chance to buy again. An average can also hide that a small number of customers account for many orders.

The LTV in GA4’s User lifetime exploration is calculated for users identified by that property’s reporting setup and includes user-lifetime data; it does not define each business’s financial value per customer. For average revenue per user over a period, see ARPU. CLV here uses the cohort, revenue, and horizon each business states. For the definition by order, see the average order value formula.

The eight customers, orders, CLP 36,000, 55% margin, and estimated two-year relationship are hypothetical. They show the arithmetic; they are not HeyMark data, a business result, or budget advice.

The cover is an AI-generated illustration.