LTV:CAC compares customer lifetime value with customer acquisition cost. The LTV to CAC ratio formula, a cohort example, the 3:1 benchmark, five traps.
By Sergey BruhPublished 9 min read
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Start the free courseLTV:CAC, the LTV to CAC ratio, divides what an acquired user earns you (customer lifetime value, LTV) by what it cost to acquire them (customer acquisition cost, CAC). A ratio of 1 means the user has paid back their own acquisition cost; 3 means they brought three times as much. The number is only honest when three things are fixed: LTV counts gross profit, not revenue; both numbers are per acquired user, not per payer; and LTV has a stated age, such as 5 or 12 months since sign-up. Below: the formula, a two-channel example, benchmarks and traps.
For one acquisition cohort (the users a channel brought in one month) at age months:
Gross profit is cumulative, from sign-up to month . The user counts cancel out, which gives two useful facts:
So LTV:CAC, ROI and payback are three views of the same curve.
Gross profit, not revenue. App-store commission, payment fees and the cost of serving the user come off before you see anything. In the example below they take 20% of revenue.
Every acquired user, not only payers. You paid for every install, including the majority who never subscribe. Dividing by payers inflates LTV many times over: for one cohort in the course, gross profit per cohort user after six months is 1.15 dollars, while per paying user it's 23.20.
A stated age. A young cohort has no "lifetime" yet. Read it from the LTV curve, the cumulative gross profit per user by month since sign-up, and say "LTV at month 5" or "12-month LTV". To extrapolate, the LTV curve lesson borrows the shape of an older, comparable cohort instead of drawing a straight line.
Skip the shortcut "profit per payer ÷ churn": it assumes churn never changes, which real curves rarely allow (how to calculate retention rate), and counts payers, not installs.
CAC is the spend of one channel in one period divided by the new users it brought in that period. Say which users you mean (installs or paying customers) and keep the same unit in LTV. Three versions of CAC answer different questions:
Halves is a bill-splitting app with a paid subscription. In January 2026 it bought users through two paid channels: paid social and search ads inside the app stores. Start with one cohort and follow it as it ages: the 570 users that store search ads brought in that month for 892.60 dollars.
The working at month 5:
The ratio first reaches 1 at month 4 (), so that's the payback month. By month 5 the cohort's ROI is .
Now put the other channel next to it at the same age:
Paid social was 40% cheaper per user, yet at month 4 its cohort was still at an ROI of . February repeats the pattern: store search ads at by month 4, paid social just short at . Store searchers arrive with a need, and more of them subscribe. A lower CAC isn't a better channel when its users pay back more slowly.
Across past cohorts, a paid-social user makes about 1.46 dollars of gross profit in the first 12 months. Against a CAC of 0.94 that's a 12-month LTV:CAC of .
Halves users also invite friends into their groups. Don't add those invited users to the ratio until a holdout confirms they wouldn't have come anyway; once it does, invited users can lower your effective acquisition cost. How to measure that honestly is in the post on the viral coefficient.
"3:1" is a rule of thumb from subscription software. The logic holds: gross profit must still cover salaries and estimation error, so a ratio just above 1 leaves nothing over. But the benchmark hides its assumptions:
A more useful reading for your own product:
For Halves, a cheap consumer app, about 1.55 at 12 months on paid social is workable if the cash can wait for payback.
In August 2026 Halves raised the paid-social budget by 1,000 dollars a week and watched the installs:
With the 12-month LTV of 1.46 dollars, the average ratio at 3,000 dollars a week still looks fine: . But the third 1,000 dollars bought users at 2.10 each: , a loss on every one. The second 1,000 only about break even (); the fourth lose more (). Budget on the marginal ratio: the average mixes cheap first users with expensive last ones. The channels lesson works through this ramp.
The cohort analysis example shows how to build and read the cohort table behind all of this.
Above 1 at a horizon you trust, with a payback period your cash can carry, plus a margin for overhead and estimation error. The popular 3:1 fits subscription software with multi-year lifetimes; a consumer app can run well on less, at a 12-month horizon.
Use the age you have: "LTV:CAC at month 3". To project, multiply by the growth ratio of an older cohort with the same season, prices and channels, and stop at the oldest age you've observed.
Same curve: the ratio says how much a cohort returns, payback says when. Short of cash? Watch payback. Deciding how far to scale? Watch the marginal ratio.
Not when you judge a channel. A blended CAC (all spend ÷ all users) suits a company-wide view but hides whether paid pays for itself.
Module 7 of the free course Product Analytics: From Data to Decisions covers this on Halves data: build the LTV curve by cohort, compute CAC, payback and ROI for both paid channels, then decide whether to raise the price or change the paywall. New to the field? Start with what product analytics is.
