Cohort LTV Calculator
Most lifetime value models ask you to pick a customer lifespan, and almost nobody has measured one. This works the other way round: give it the share of customers who order again, and it derives the expected number of orders from the retention curve itself — then shows how much of that value actually lands inside a horizon you can finance.
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Lifetime value you collect in year four is a forecast. Lifetime value you collect in year one is cash.
- The calculator
Lifetime value from a retention curve, not a guess
The one input that matters is the repeat rate — the share of customers who place another order in the next period. Everything else follows from it.
With a repeat rate r per period, the expected number of orders after n periods is (1 − rn) ÷ (1 − r), and over an unlimited horizon it is simply 1 ÷ (1 − r). Multiply by contribution per order and you have lifetime value without ever assuming a lifespan. Half the value is in by solves log(0.5) ÷ log(r) for the number of periods. This is a constant-rate model: it assumes the share who reorder stays the same period to period, which is close enough for planning but slightly pessimistic in the tail, because customers who have already reordered several times tend to keep going. No discount rate is applied here — the customer lifetime value calculator handles present value if you need it.
- Reading it
The repeat rate does almost all the work
Small changes in one input move the answer more than everything else combined.
Expected orders is one divided by one minus the repeat rate, and that curve is steep. At a 40% repeat rate a customer places 1.67 orders. At 55% it is 2.22. At 70% it is 3.33, and at 80% it is five. Which means a ten point improvement in repeat purchasing is worth more than almost any change you can make to average order value — and it is the single number most stores have never measured properly.
The horizon tile is the one finance cares about. A lifetime value of sixty-two dollars is only useful if you can wait for it; if you are funding acquisition from cash flow, what matters is the fifty-six dollars that arrives inside twelve months. When the share collected drops below half, the model is telling you that most of the value you are counting on sits beyond any period you can reasonably plan or borrow against.
- Measure the repeat rate from a real cohort, not from all customers at once
- Use the median gap between orders as the period, not the mean
- Run it per acquisition channel — the cheapest channel often repeats worst
- Getting it wrong
Four ways a retention curve gets mismeasured
The model is only as good as the repeat rate, and the repeat rate is easy to calculate wrongly.
Measuring across all customers at once
Customers acquired last week have not had time to reorder, so including them drags the rate down. Take one cohort, give it a full period, and measure only that cohort.
Picking the period from the average gap
A handful of customers who reorder after two years pull the mean well past anything typical. The median gap between first and second order is the number that belongs in the period field.
Assuming the rate stays flat forever
It does not. The share who reorder rises with each repeat, because people who have bought four times are far likelier to buy a fifth. A constant rate is a safe planning assumption and a pessimistic one.
Counting revenue instead of contribution
Lifetime revenue is not lifetime value. Only the contribution belongs to you, and on a 40% margin the difference between the two is a factor of two and a half.
Our retention and email guide covers building cohort tables and reading a retention curve, which is where the repeat rate in this calculator should come from.
- Questions
LTV FAQs
The questions this calculator usually raises.
What is the difference between this and your customer lifetime value calculator?
Two different starting points for the same idea. The customer lifetime value calculator asks you for a lifespan and an order frequency, then discounts the result to present value — the right tool when you already know how long customers stay and you need the finance-grade number. This one asks only for a repeat rate and derives the number of orders from the retention curve, which is the right tool when the lifespan is exactly the thing you cannot honestly state. Use this to find the shape, that one to price it.
What is a good repeat rate?
Across eCommerce as a whole, somewhere between 20% and 30% of customers place a second order, but the spread by category is enormous. Consumables and subscriptions run far higher; considered one-off purchases such as furniture or mattresses run in single digits and are not unhealthy for it. The comparison that means something is your own rate, by cohort and by acquisition channel, over time.
Why does a small change in repeat rate move LTV so much?
Because expected orders is 1 ÷ (1 − r), and that function accelerates. Going from 50% to 60% takes expected orders from 2.0 to 2.5, a 25% gain. Going from 80% to 90% takes it from 5 to 10. The practical consequence is that retention work gets more valuable the better you already are at it, which is the opposite of how most acquisition channels behave.
Should I discount future value?
If you are making a financing decision, yes — and the customer lifetime value calculator does that. This tool deliberately leaves it out and gives you the horizon tile instead, because for most planning conversations the useful question is not “what is this worth in today’s money” but “how much of it will I have collected before I need to spend it again”.
How does this compare to a predictive LTV model?
Honestly, it is a simplification. Proper probabilistic models estimate a separate purchase rate and dropout probability per customer and will beat a constant-rate geometric series on accuracy. What this gives you is the same shape from one number you can calculate in a spreadsheet in ten minutes, which is enough to decide whether retention deserves the next quarter of effort.
Can I use it for a subscription business?
Yes, and it fits neatly: set the period to your billing cycle and the repeat rate to one minus your churn rate for that cycle. Expected orders then becomes expected billing periods, and the half-life tile tells you the median subscriber lifetime in months.
- Next
Related tools and reading
CALCULATOR
The finance-grade version: lifespan and frequency in, present value and payback months out.
CALCULATOR
The other side of the ratio: blended and paid CAC, and the break-even your margin supports.
GUIDE
Cohort tables, flows and campaign cadence — how to move the repeat rate this calculator runs on.
Using a repeat rate nobody has measured?
Most stores are. The free audit builds the cohort table from your own order history and tells you what the curve actually looks like, by channel and by first product bought.