Meelu

Customer lifetime value calculator

You want to know what you can afford to pay for a customer, and the usual answer is one average that describes nobody. Upload a CSV of your orders — customer id, order date, amount — and get a value for each customer going forward, which of them have quietly stopped buying, and which groups cost more to win than they give back. It runs in your browser, so the file never leaves your machine.

Drop a transactions CSV here, or

One row per order: customer id, order date, amount. Up to 200,000 rows. Extra columns — source, plan, acquisition cost — get used, they do not get in the way.

The file is read and modelled in this browser tab. It never reaches our server, so there is nothing for us to keep.

  • No account
  • Nothing uploaded
  • Value per customer
  • LTV:CAC per group
  • CSV export

What this CLV calculator gives you

A value per customer, not one average

What each individual customer is likely to be worth to you from here on, based on how often they buy and what they spend — not one blended number that describes nobody.

Spots the ones who have quietly gone

In a shop nobody tells you they have left. You get the odds each customer is still buying at all, so a fortnightly buyer gone quiet for six months is treated differently to a twice-a-year one.

Which customers pay for themselves

Your customers grouped by what they are worth, each group next to what it cost to win them. A group giving back less than it costs is the finding worth acting on.

What your best customers have in common

Any extra columns in your file — source, plan, first order value — ranked by how well they tell your best group apart from your worst. Useful for deciding where to spend.

Says no rather than guessing

If your file is too thin or your customers do not behave the way this assumes, you get what they have already spent instead, clearly labelled. A confident wrong number is the worst thing it could give you.

Download it, nothing uploaded

The per-customer numbers and the group summary as CSVs. Your file is read inside your browser tab and never reaches us. Free, no sign-up.

How to calculate customer lifetime value from your own data

  1. Step 1

    Export your orders to CSV

    One row per order, with a customer id, an order date and an amount. A Shopify, WooCommerce or Stripe export already has all three. If you also know what each customer cost to win, or anything else about them, bring those columns too.

  2. Step 2

    Drop the file in

    The columns are picked out of your headers and everything happens inside your browser tab. Nothing is uploaded and there is no account to make.

  3. Step 3

    Start at the top of the results

    The first panel tells you whether you are looking at a forecast or at money already spent, and why. Then work down: the value groups, how concentrated your revenue is, and what sets the groups apart.

Why it asks for a file instead of three boxes

If you sell subscriptions you know exactly when somebody left, because they cancelled. In a shop or a marketplace nobody tells you. A customer who has not ordered for four months might be gone for good, or might buy twice a year and be perfectly happy. You cannot tell those two apart from the date alone — only from how often each of them used to buy.

That is what your order history is for. A fortnightly buyer who has gone quiet for six months gets written down hard. A twice-a-year buyer quiet for the same six months barely moves. Same silence, different conclusions — and a calculator with three boxes in it cannot make that distinction.

The customer lifetime value formula, and when it lies to you

The formula in every article is:

LTV = average order value × purchase frequency × customer lifespan

It is not wrong. It is an average, and averages are fine when you want one number for a slide. You get it here alongside everything else, so you can see the gap between the two.

When it is fine

  • You have one product at one price and a repeat rate that has not moved in a year.
  • You want a rough ceiling on what you can afford to pay for a customer and you are going to sanity-check it against reality anyway.
  • You have under a few hundred customers, where there is not enough history for anything cleverer and the arithmetic is at least honest about being a guess.

When it misleads

  • Per-customer decisions. The formula gives one number to everybody. Deciding who to call, who to discount and who to let churn needs a number per customer, and an average cannot be disaggregated back into one.
  • Heavy tails. In most businesses the top ten per cent of customers are half the revenue or more. The average then sits in a gap where almost none of your actual customers are. The results show you how lopsided yours is.
  • The lifespan term. Nobody actually knows how long their customers last, because most of them have not finished yet. What gets put in that slot is either how long they have been around so far, which is too short by definition, or a churn rate someone worked out from whatever window was handy.
  • Dead customers. The formula happily projects future revenue from somebody who last ordered in 2023. Here they are marked as almost certainly gone, with a value to match.

The LTV:CAC ratio, per group rather than blended

One blended LTV:CAC for a whole business is an average of customers who have nothing in common. It is the number that gets put on a board slide and the number that hides the finding. If your ratio is a healthy 3.4:1 overall and your bottom group is at 0.6:1, you are funding one group of customers with another and nobody has decided to.

So you get the ratio for each value group instead. If your file has a column for what each customer cost to win, that gets used. If it does not, type one figure into the box — less precise, still far more useful than a single company-wide number.

Read a group below 1:1 as a decision rather than a failure. An entry tier that does not pay for itself might be a deliberate marketing expense that feeds the tier above it. The point is that it should be a choice you made rather than something you found out about eighteen months later.

What it does not do

  • It is not right for subscriptions. This assumes customers can come back at any time and leave without telling you. If you run fixed monthly subscriptions and know your cancellation dates, a cohort retention view describes your business better than this does.
  • It does not do margin. Everything here is revenue. If you want contribution value, multiply the exported figures by your gross margin — the tool will not guess it for you.
  • It does not tell you what causes what. The ranking of what sets your best group apart from your worst tells you what they have in common, not what would happen if you changed it. Two things that go together will both show near the top.
  • It does not join files. One CSV, one run. If your customer traits live in a different export from your orders, you will have to join them yourself — or use the MCP server below, which does that part.
  • It does not keep anything. No account, no storage, no scheduled re-run. Close the tab and the analysis is gone; download the CSVs if you want to keep it.

Frequently asked questions

What is customer lifetime value?

Customer lifetime value is the total profit you expect from a customer across the whole of their relationship with you, not just their first order. It tells you what a customer is worth, which in turn tells you what you can afford to spend winning one. People write it as LTV or CLV; the two mean the same thing.

How is customer lifetime value calculated?

The standard formula is average order value × purchase frequency × customer lifespan, then multiplied by gross margin to get profit rather than revenue. A customer spending £60 an order, four times a year, for three years at a 40% margin is worth £288. For subscriptions the shortcut is average revenue per account ÷ churn rate, so £50 a month at 2% monthly churn gives £2,500. Both assume the past repeats, which is why predictive models are used when the stakes are higher.

What is a good LTV to CAC ratio?

3:1 is the widely used benchmark — each customer should return about three times what it cost to acquire them. Below 1:1 you are losing money on every sale. Much above 5:1 usually means you are underspending on growth rather than running a brilliant business, and a competitor with a looser wallet will take the market. Use gross-margin LTV, not revenue, or the ratio flatters you.

What is the difference between LTV and CAC payback period?

LTV:CAC tells you whether a customer is profitable in the end; payback period tells you how long your cash is tied up before that happens. A business can have a healthy 4:1 ratio and still run out of money because payback takes 30 months. The usual target is under 12 months for SMB and under 18-24 months for enterprise. Look at both, because they fail in different ways.

What is predictive customer lifetime value?

Predictive LTV forecasts what a customer will spend in future rather than totalling what they have already spent. It fits a model to each customer's purchase history — how often they buy, how long since the last order, how much they spend — and produces an expected value along with the probability they are still an active customer. It is more useful than the historical average because it separates a quiet customer who will return from one who has silently left.

How do you increase customer lifetime value?

There are only three levers: get customers to spend more per order, buy more often, or stay longer. Staying longer is usually the biggest of the three and the cheapest to move, because retention compounds through the whole calculation. Practical work means better onboarding, sensible cross-sells, subscription or replenishment options, and recovering failed payments. Raising prices lifts LTV directly but only while it does not push churn up more than it lifts margin.

Should LTV be based on revenue or profit?

Profit. Revenue-based LTV counts money that never reaches you: cost of goods, payment fees, delivery, returns and the support cost of serving the account. A 20%-margin ecommerce business using revenue LTV will overstate customer worth by five times and cheerfully overspend on ads. Multiply by gross margin, and subtract ongoing service costs if they are material.

What is a good customer lifetime value?

There is no absolute number, because LTV only means something next to your acquisition cost. £200 is excellent if customers cost £40 to win and terrible if they cost £300. Compare it to CAC, compare it to your own figure last year, and compare cohorts against each other. A rising LTV in recent cohorts is a better sign than any benchmark from another company.

The version that follows up

This page does one thing with one file, in your browser, and then stops. It cannot join your orders to your support tickets, it cannot answer the question you think of after reading the table, and it cannot tell you why group D looks the way it does.

For the follow-up questions, run the same analysis inside your AI assistant with meelu-analytics-mcp. It is free and open source, it runs on your machine, and your data stays there for the same reason it does here. See the LTV use case.

Other free tools on the same data

  • Cohort analysis — the same transactions file, grouped by the month people arrived, so you can see which intake held up.
  • RFM analysis — recency, frequency and monetary scoring, which is the descriptive cousin of what this page models.
  • Churn prediction — for a subscription business, where you know the cancellation dates and this page is the wrong shape of tool.