Short answer: Member lifetime value estimates the economic value a member may generate across a defined relationship. A simple revenue proxy multiplies net revenue per period by expected active periods, but decision-grade LTV should use contribution margin, retention probability, refunds, payment losses, service costs, and an explicit time horizon.
Method note: This page was revised on September 1, 2026. It uses the customer-lifetime-value framework discussed by Fader, Hardie, and Jerath in Customer-Base Analysis in a Discrete-Time Noncontractual Setting for methodological context. The worked numbers below are transparent author scenarios, not industry benchmarks or promised results.
Member lifetime value, or LTV, is an estimate of the future economic contribution associated with a member relationship. The definition matters. Some dashboards report gross billings, some use cash collected, and others estimate contribution after variable costs. Those values answer different questions and should not share the same label.
For a recurring-membership business, the simplest proxy is:
Gross-billing proxy = average net recurring billing per active period × expected active periods
This shortcut can help with an initial cohort review, but it is not profit. It excludes card failures, refunds, discounts, taxes collected for authorities, instructor or service-delivery costs, support costs, and the uncertainty of future retention.
| Measure | Basic calculation | Useful for | Important limitation |
|---|---|---|---|
| Gross-billing proxy | Net recurring price × active periods | Simple cohort comparisons | Not profit or cash contribution |
| Collected-revenue proxy | Cash collected minus refunds | Billing and collection review | Still excludes service-delivery costs |
| Contribution LTV | Expected collected revenue minus variable delivery and servicing costs | Acquisition and retention decisions | Depends on cost allocation and retention assumptions |
| Discounted expected LTV | Probability-weighted future contribution discounted to the present | Longer-horizon planning | More sensitive to model assumptions |
Use one definition consistently within a decision. Comparing gross billings for one segment with contribution for another creates a false difference even if both calculations are individually correct.
Start with a cohort whose membership start and end rules are explicit. Decide whether a pause counts as active, how upgrades and downgrades are handled, and whether the observation window is complete. Then calculate net recurring billing and active periods from the same cohort.
Illustrative scenario: suppose a defined cohort produces $120 of net recurring billing per active month and the observed average relationship is 14 months. The simple gross-billing proxy is $120 × 14 = $1,680.
If the assumed contribution margin after variable delivery and servicing costs is 70%, a simplified contribution proxy would be $1,680 × 70% = $1,176. Both figures are scenario arithmetic. The 70% assumption is not a studio benchmark and should be replaced with the business’s own cost data.
An average tenure hides the shape of retention. A stronger model estimates the probability that a member remains active in each future period, multiplies that probability by expected contribution for the period, and then sums the probability-weighted values across a stated horizon.
At minimum, report:
Do not convert a short observation window into a long forecast without showing the assumption. Members whose relationships have not ended are censored observations; treating all of them as completed can bias tenure estimates.
Segmentation can reveal meaningful differences, but the segment rule must be set before interpreting the result. Useful dimensions may include membership product, acquisition source, location, start month, service mix, or intro offer. Each segment should have enough observations for a stable estimate and should use the same LTV definition.
The previous version claimed a large segment difference without a verifiable cohort, period, or calculation file. That claim has been removed. A segment comparison should publish the inputs and sample sizes that allow another reviewer to reproduce the result.
Compare acquisition cost with contribution LTV, not gross billings. Also review payback timing and cash constraints. A ratio can look attractive while the business waits too long to recover the acquisition spend or while the estimate depends on retention that has not yet been observed.
This page does not use a universal “good” LTV-to-CAC ratio. Appropriate guardrails depend on contribution margin, cash runway, acquisition capacity, sales cycle, cohort variance, and risk. Set the threshold from the business’s own economics and document it.
It is a simple gross-revenue proxy when both inputs are defined consistently. It is not a profit estimate because it omits costs, payment losses, refunds, and uncertainty about future retention.
Yes. Use cash collected or a clearly defined net-billing measure, and distinguish initially failed charges from amounts later recovered. Otherwise the model may overstate realized value.
Recalculate when enough new cohort data exists to change the decision, and keep a consistent reporting cadence. Compare each revision with prior assumptions rather than silently replacing them.
LTV is generally more defensible as a cohort or probabilistic estimate than as a certain value assigned to one person. Individual outcomes vary, and the model should not be treated as a promise.
The most common structural mistake is mixing gross revenue, collected cash, and contribution under one label. Define the measure before comparing products, cohorts, or acquisition channels.
Mako brings client, membership, billing, and operating records into one workflow. Verify the current product scope and use your own cohort definitions before relying on any dashboard value.
Related reading: Member Retention Strategies: How Top Gyms Keep Clients for Years; Gym Loyalty Programs That Move LTV; Churn Prediction for Gyms: Using CRM Data to Save Members Before They Quit.
