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CAC

Also called: customer acquisition cost, CPI, cost per install, blended CAC

Customer acquisition cost: marketing spend divided by the new users (or paying customers) it brought. State which of the two you mean.

Example

In September Theo spends $6,000 on paid social ads for Halves. Halves gets 6,000 new installs in total; 2,400 of them are attributed to paid social, and 96 of those become paying Halves Plus subscribers.

Metric
Working
Result
CPI, paid social
$6,000 ÷ 2,400
$2.50
Conversion install → paid
96 ÷ 2,400
4%
CAC per payer, media only
$6,000 ÷ 96
$62.50
CAC per payer, fully loaded (+$900 creatives and tools)
$6,900 ÷ 96
$71.88
Blended CPI, all installs
$6,000 ÷ 6,000
$1.00

The blended $1.00 looks four times cheaper than paid social, only because 3,600 installs came from invitations and store search at no media cost. The number to put next to paid social's LTV is $2.50 per install, or $62.50–$71.88 per payer.

Common mistakes

  • Not saying "per what". CPI, cost per trial and CAC per payer differ by an order of magnitude; label every number.
  • Blended CAC for budget decisions. Free organic and invite users hide how expensive a paid channel really is.
  • Media spend only. Leaving out creatives, agency fees and tools makes the model look better than it is.
  • Timing mismatch. September spend often turns into payers in October, after the trial; attach payers to the cohort that was acquired, not to the calendar month they paid in.
  • Trusting the channel's own count. Platforms tend to claim more conversions than they cause; check with the store's attribution data and an incrementality test.

Learn it in the course

  • CAC, payback and ROI · Judge a paid channel by the cohort it buys: CAC, payback period and cohort ROI at a stated age, and why a month's profit divided by that month's spend flatters ads.
  • Channels and marketing attribution · Channel capacity and marginal CAC, a break-even cost that values the invitees a paid user brings, marketing attribution through the stores' privacy frameworks, and incrementality from a geo holdout.
  • Forecasting active users · New users × retention: build a monthly active users forecast from cohorts, with a separate curve for summer cohorts, and price a cut in paid acquisition in users.