Customer lifetime value calculator

CLV on gross margin, not revenue — for stores and for subscriptions — and the acquisition cost it can carry.

Runs entirely in your browser — nothing is uploaded, nothing is stored. Updated 2026-09-30.

Customer lifetime value is the gross profit a customer brings in over the whole relationship: average order value × orders per year × years as a customer × gross margin. A $60 average order, 2.5 orders a year, 3 years and a 40% margin make a CLV of $180.

Many CLV calculators stop one step earlier and report lifetime revenue — $450 in the same example — which is the figure that makes a 3:1 LTV to CAC ratio look easy to hit. This one shows both side by side and turns the margin figure into the most you can pay to acquire a customer at the ratio you choose. For a subscription business the second panel uses the churn form instead: monthly revenue per account × gross margin ÷ monthly churn.

Revenue CLV and margin CLV are different numbers

Lifetime revenue answers "how much will this customer pay us?" Lifetime value answers "how much will this customer leave us with?" — and only the second can be compared with what you spent to acquire them. At a 40% margin the two differ by a factor of 2.5. Compare CAC with revenue CLV and every channel looks two and a half times more profitable than it is.

Margin here means what you keep after cost of goods, shipping, payment fees and returns, the same margin that sets your break-even ROAS. It is not net profit: overheads are paid whether or not you acquire this customer, so they stay out.

Years as a customer is where CLV calculators overstate

Order value and margin are facts you can read from last month. How long a customer keeps buying is a forecast, and it moves the answer in proportion — change three years to five and CLV goes up by two thirds. A young store has no data to support five, and a formula does not know that.

Two habits keep it honest. Use observed repeat behaviour by cohort — of the customers acquired a year ago, how many ordered again, and how often — rather than one lifetime average. And pick a horizon you would actually bet the acquisition budget on; many operators cap CLV at 12 or 24 months for exactly this reason, and some discount later years because money arriving in year three is worth less than money today.

Historical CLV, predictive CLV, and which one you need

The formula on this page is historical: it describes the average customer from past behaviour. Predictive models go further and estimate each customer's future purchases from their own order history — the best known is the BG/NBD model of Fader, Hardie and Lee — which matters when you want to treat customers differently.

For deciding what you can pay to acquire a customer, historical CLV by cohort is enough. The error that costs money is rarely the choice of model; it is revenue in place of margin, or a lifespan nobody checked.

From CLV to what you can pay for a customer

The usual convention in subscription businesses is an LTV to CAC ratio of about 3:1 on gross-margin LTV: of every $3 of margin a customer brings, $1 goes to acquiring the next one. The first panel divides CLV by the ratio you choose — at $180 and 3:1 that is $60 — and that is the ceiling to compare with the paid CAC per channel in the CAC calculator.

The ratio is not the only constraint. Payback — how many months of margin it takes to earn the acquisition cost back — decides whether you can afford to grow. A 3:1 ratio earned over three years still needs three years of cash to fund each customer.

Computing this from your own accounts

Every calculator on this page takes numbers you typed. The inputs CLV needs — who ordered, how often, for how long — live in your store, not in your ad accounts: TableBI's live connectors sync ad and analytics data at campaign level, with no customer IDs. So the honest route is the one every store platform already offers: export your orders as a CSV, upload it, and the repeat-purchase numbers become a query that sits next to your acquisition costs.

# orders are not a synced field: upload the store export that carries them
tablebi connect csv --file orders.csv --platform shop_orders

# every column of the file lands in shop_orders_raw, named as in the export
# (customer_id, order_date, order_total here — use your export's column names)
tablebi ask "WITH c AS (
     SELECT customer_id, COUNT(*) AS orders, SUM(order_total) AS revenue,
            MIN(order_date) AS first_order
     FROM shop_orders_raw GROUP BY customer_id)
   SELECT date_trunc('quarter', first_order) AS cohort,
          COUNT(*) AS customers,
          AVG(orders) AS orders_per_customer,
          AVG(revenue) AS revenue_per_customer
   FROM c GROUP BY 1 ORDER BY 1"

Read the table by cohort, not as one average: the customers you acquired two years ago show how many orders a customer actually places, and the recent cohorts show whether that is changing. Put it next to cpa() from the ad accounts and the LTV to CAC ratio stops being an assumption. No hosted model does the reasoning; your own Claude Code or Codex drives the CLI.

Questions people ask about customer lifetime value calculators

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