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Customer Lifetime Value Calculator

Measure what a customer is worth over their lifetime — and decide how much you can afford to spend acquiring one.

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Customer lifetime value

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Leverage

Formula

LTV = Average revenue per period × Gross margin × Customer lifespan

Understanding customer lifetime value

Reference material — the calculator above stays the primary tool.

What lifetime value measures

Customer lifetime value is the total margin a customer contributes over their entire relationship with you. It combines how much they pay, how profitable that revenue is, and how long they stay — the three forces that decide whether acquisition pays off.

It is the ceiling on acquisition spend: a business can profitably spend up to a fraction of LTV to win a customer, which is why it anchors every paid-growth decision.

How to read your result

Higher is better, judged against an acquisition-aware benchmark:

Low — lifetime value below benchmark; acquisition spend is hard to justify. Average — near benchmark; retention and margin work pays off. Strong — at or above benchmark; you can outspend competitors on acquisition.

The three levers of LTV

LTV moves on three independent inputs: average revenue per period, gross margin, and lifespan. Retention (lifespan) usually compounds hardest because it multiplies every future period; margin improvement flows straight to contribution; revenue per period lifts through pricing and expansion. Model each above to see which adds the most value.

LTV and the acquisition budget

Lifetime value sets what you can pay to acquire. Pair it with CAC to get the LTV:CAC ratio — the single clearest read on whether growth is profitable. A high LTV with room above a 3:1 ratio means there is headroom to spend more on acquisition and still win.

Is LTV a forecast?

It is a planning estimate built on assumed margin and lifespan that may shift as the business changes. Treat it as the value a customer is worth at current economics, refresh the inputs as retention and pricing evolve, and pressure-test it against cohort data before betting large acquisition budgets on it.