Customer lifetime value (LTV)
Customer lifetime value (LTV or CLV) estimates the total revenue or margin a customer generates across their whole relationship. It is a forecast built on assumptions about retention, purchase frequency, and margin — not a measured number. Treated as fact it misleads; treated as a model with stated assumptions it guides acquisition spend.
What this means
LTV asks: across the whole time someone is a customer, how much value do they bring? A simple form multiplies average order value by purchase frequency by an estimate of how long they stay. Because 'how long they stay' is a forecast, LTV is inherently an estimate, not a recorded total — historical LTV looks backward, predictive LTV looks forward with even more assumptions.
Why it misleads
The retention assumption dominates the result: small changes in how long customers are assumed to stay swing LTV widely. Using a single blended LTV hides that segments differ enormously. And comparing LTV to acquisition cost only works if both use the same time horizon and the same revenue-versus-margin basis.
State the horizon, the basis (revenue or margin), and the retention assumption every time you quote an LTV. Without them the number is not interpretable.
- LTV is a forecast, not a measured fact
- The retention assumption dominates the result
- State horizon, basis, and assumptions when quoting it
How it appears in analytics and logs
An LTV figure is a model output, sensitive to its retention and margin inputs. Two analysts with different assumptions get different LTVs from the same data, so the assumptions matter as much as the number.
Diagnostic use case
Use LTV to compare the long-run value of customer segments and to bound acquisition spend — while stating the retention and margin assumptions behind it.
What WebmasterID can help detect
WebmasterID records the first-party conversion and purchase events that an LTV model is built on, so the inputs reflect your own funnel.
Common mistakes
- Presenting LTV as a measured fact rather than a forecast.
- Comparing LTV to acquisition cost on different horizons.
- Using one blended LTV across very different segments.
Privacy and accuracy notes
LTV is computed from aggregate revenue and retention assumptions, not individual profiling. WebmasterID measures the conversion events that feed such a model first-party.
Related pages
- Retention rate
Retention rate measures how many users from a starting cohort come back in a later period. It depends entirely on definitions: what counts as 'returning', over what window, and which cohort. A 7-day and a 30-day retention rate answer different questions, and neither is comparable to a churn figure computed a different way.
- Average order value (AOV)
Average order value (AOV) is total revenue divided by the number of orders. It is simple but easy to misread: a few large orders pull the mean upward, refunds and taxes change what 'revenue' means, and mixing currencies without conversion corrupts it. For skewed order sizes, the median order value is often more honest.
- Churn rate
Churn rate measures how many customers (or how much recurring revenue) you lose in a period. Like retention, it is defined by choices: the window, what counts as 'churned', and whether you count customers or revenue. Customer churn and revenue churn can diverge sharply, so the basis must be stated.
- Attribution analytics
Relate value back to acquisition sources directionally.
Sources and verification notes
- Google — Predictive metrics including LTV (GA4)GA4 documents a predictive LTV; the assumptions behind any LTV model remain the analyst's to state.
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.