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Customer lifetime value calculator
Built on your actual cohort behaviour rather than AOV × frequency × lifespan — and it shows you both, because the gap between them is usually the reason an acquisition budget stopped working.
What customer lifetime value is
Customer lifetime value is the contribution — revenue after cost of goods — that an average customer produces over the period you are willing to forecast. Computed properly it comes from cohort behaviour: everyone places one order, a measured share place a second, and the probability of each further order decays. The common shortcut, AOV × frequency × lifespan, assumes that decay never happens.
Your cohort.
Prefilled with an illustrative store. Same four inputs as the repeat purchase calculator, plus margin — one model, two questions.
The third-order count is optional and worth finding — it replaces the model’s decay assumption with a measurement from your own data.
The lifespan only feeds the comparison figure — it is what the conventional formula asks you to assume, and three years is the number most often typed in. Nothing else on this page uses it.
Lifetime contribution per customer
$59.73
1.42 expected orders at 62% margin.
- The usual formula says 4.9/yr × 3yr × AOV × margin
- $623.85
- Which overstates by because nothing in it decays
- 10.4×
- Second-order ratethe measured part
- 25.0%
- Decay per ordermeasured from your third-order count
- 40.0%
Contribution by month
- m1 $42
- m3 $53
- m6 $57
- m12 $59
- m18 $60
- m24 $60
- m36 $60
Dashed rows are projected. Your inputs evidence roughly 5 months.
Why the usual formula overstates
AOV × frequency × lifespan is not wrong arithmetic. It is the wrong assumption, applied consistently.
- 1
It treats a blended rate as a permanent property
A 25% repeat rate does not mean a quarter of customers repeat forever. It means a quarter reached order two — and of those, fewer reach three, fewer still reach four. Compounding a rate that only ever applied to the first transition is where the overstatement comes from.
- 2
Lifespan is usually invented
Ask where the “3 years” came from and the answer is rarely a measurement. This model has no lifespan input at all; the horizon falls out of the decay and the order gap.
- 3
Revenue gets compared against cost
A revenue CLV set against a cash CAC produces a ratio that flatters. Both sides have to be in contribution, which is why margin is a required input here rather than an optional multiplier.
- 4
And then it is used to justify spend
That is the expensive part. An overstated CLV raises what looks affordable to pay for a customer, and the gap only shows up months later as a payback period nobody planned for. The LTV:CAC calculator takes this number and asks that question directly.
The model, in full
Four lines. Published so you can disagree with a specific one.
| Step | Formula | Status |
|---|---|---|
| Second-order rate | second-order customers ÷ cohort | Measured from your input |
| Decay ratio | P(order 3) ÷ P(order 2) | Measured if you supply a third-order count; otherwise assumed equal to the second-order rate |
| Expected orders | 1 + p₂ ÷ (1 − decay) | Derived |
| Lifetime contribution | expected orders × AOV × margin | Derived |
No benchmark appears on this page. Lifetime value depends so heavily on category, price point and margin structure that a cross-brand average would mislead more than it informs, and we have not measured one. metric to confirm. Model reviewed 2026-09-09.
What this will not tell you
What a single customer is worth. This is a cohort average, and cohorts contain both people who ordered once and people who order monthly. Segment by acquisition source if you can — a discount cohort and a full-price cohort are two different businesses averaged into one number.
What happens after your evidence runs out. The projected rows are a model. Treat the far end as a shape rather than a forecast.
Whether the trend is improving. One cohort is a snapshot. Run this quarterly on the same window and the direction is worth more than the level.
Definitions
- Customer lifetime value (CLV or LTV)
- Contribution produced by an average customer over a defined horizon.
- Contribution
- Revenue after cost of goods. The only version of CLV worth comparing against acquisition cost.
- Decay ratio
- How much less likely each order is than the one before it. The single number that separates an honest CLV from an inflated one.
- Cohort
- Customers acquired in the same period, followed forward together.
- Horizon
- The period you are willing to forecast. “Lifetime” in practice always means a horizon somebody chose.
Questions about lifetime value
What is the customer lifetime value formula?
The one you will find everywhere is AOV × purchase frequency × customer lifespan, sometimes with a margin multiplier. It is easy to compute and it overstates ecommerce lifetime value, often by a multiple, because nothing in it decays — it takes the observed gap between orders and assumes the customer keeps buying at that rate for a lifespan somebody typed in, conventionally three years. This calculator uses a cohort model instead — every customer places one order, a measured share place a second, and subsequent orders decay — and shows both numbers side by side so you can see the size of the gap.
Why is my CLV lower here than in other calculators?
Because the others are almost certainly using the frequency-times-lifespan formula, and because this one is denominated in contribution rather than revenue. A £200 lifetime revenue figure at 62% gross margin is £124 of actual contribution, and the difference is not academic when you are deciding what you can pay to acquire someone.
Should CLV use revenue or margin?
Margin, for any decision about acquisition spend. Revenue-based CLV tells you how much money passes through; contribution tells you how much stays. Brands that compare a revenue CLV against a cost-based CAC arrive at a ratio that looks healthy and is not, which is the single most common way a well-run acquisition programme quietly loses money.
What time horizon should I use?
A finite one you can defend. Lifetime in practice means the period you are willing to forecast — 12 or 24 months for most ecommerce. This page shows the curve at fixed points rather than a single lifetime figure, and marks everything past what your inputs evidence as projected, because a 36-month CLV computed from one quarter of data is a guess wearing a decimal point.
Do you have benchmark CLV figures by category?
No. Lifetime value depends so heavily on category, price point, margin structure and acquisition mix that a cross-brand average would be actively misleading, and we have not measured one. What is worth comparing is your own CLV against your own CAC, and both against last quarter.
How does the third-order count change the result?
It replaces an assumption with a measurement. With only a second-order count, the model assumes the decay ratio equals the second-order rate. Give it a third-order count and the ratio is measured from your data instead, which usually shifts the projected tail noticeably. If you have the number, use it.
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