Customer Economics Calculator

LTV:CAC Calculator

Estimate customer lifetime value and compare it against acquisition cost using revenue, purchase frequency, margin and customer lifespan.

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Customer Economics

Enter your average customer economics to estimate contribution LTV and CAC efficiency.

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How this LTV model works

Lifetime RevenueAOV × Orders per Year × Customer Lifespan
Contribution LTVLifetime Revenue × Gross Margin
LTV:CACContribution LTV ÷ CAC
Estimated PaybackCAC ÷ Monthly Contribution

What is a good LTV:CAC ratio?

There is no universal target. Higher ratios generally indicate more contribution generated relative to acquisition cost, but very high ratios can also indicate that a business may be under-investing in growth.

The appropriate ratio depends heavily on cash flow, retention, operating expenses, growth targets and how accurately customer lifetime behavior can be forecast.

Simplified model:This calculator assumes average purchase frequency, customer lifespan and gross margin remain stable. It does not model cohort retention, churn curves, discount rates, refunds, overhead or changes in customer behavior over time.

Model assumptions matter

LTV:CAC is only as reliable as the value and retention assumptions behind it.

Revenue LTV is not contribution LTV

Lifetime revenue can make acquisition look healthier than it really is. For paid media decisions, contribution value after product or service costs is usually more informative because that is the value available to recover acquisition cost.

Payback matters even when the ratio looks healthy

A strong lifetime ratio can still create cash-flow pressure if value arrives slowly. Payback period helps show how long the business is financing acquisition before the customer contribution recovers CAC.

Example: the same LTV:CAC ratio can hide different risk

Two customer groups can both have a 3:1 LTV:CAC ratio, but one may repay CAC within two months while the other takes eighteen months. The long-payback cohort carries more retention and cash-flow risk even though the headline ratio matches.

Use cohort data where possible

  • Separate new customers from repeat purchases already in the base.
  • Use observed retention or repeat-order behaviour instead of an optimistic lifetime.
  • Account for gross or contribution margin rather than revenue alone.
  • Revisit the model when pricing, churn, order frequency or product mix changes.