Cohort Payback Curves: Why Month-0 ROAS Misleads Repeat-Purchase Brands

Month-0 ROAS says the campaign is a failure: 0.9x, money burned. Twelve months later the same cohort has paid back threefold — the "failure" was the best acquisition of the year. For any business where customers buy more than once — subscriptions, repeat D2C, consumables — snapshot metrics judge a movie by its first frame.

Cohort payback curves fix this: group customers by when you acquired them, track cumulative contribution month by month, and read payback as the crossing point where the curve clears CAC. This post builds the curve, shows how to read it, and explains why blended LTV:CAC keeps lying to you in the meantime.

The short version: vintages, not snapshots

A cohort payback curve plots cumulative contribution per acquired customer against months since acquisition, one line per acquisition cohort. Month-0 starts negative (minus CAC); the line climbs as repeat margin accumulates; payback is where it crosses zero. Two cohorts are comparable only at equal ages — month-3 of January vs month-3 of February — never January-total vs February-partial.

Building the curve in SQL

Plot cum_contrib_per_customer against age_month, one line per cohort_month, with a horizontal line at CAC. The crossing is the payback — readable at a glance, auditable to the rupee.

Reading the curve: four shapes and what they mean

Three reading rules:

1. Compare at equal ages only. A 6-month-old cohort will always beat a 1-month-old cohort cumulatively. Truncate every curve to the youngest cohort's age before comparing — the chart should look like a race from a shared start line.
2. Young cohorts are censored, not bad. The last two cohorts have no mature data; their curves stop early. Do not extrapolate their tails from hope — project from the retention slope of mature cohorts, discounted.
3. Watch slope changes, not just levels. A cohort tracking 10% above last quarter's at month 2 but flattening faster is a retention regression wearing an acquisition improvement's clothes. Slopes belong to the product team; starting points belong to marketing.

Why this beats LTV:CAC for budget decisions

The operational use is the spend ceiling from the payback post: monthly acquisition spend cannot sustainably exceed working capital divided by payback months. The curve supplies the payback honestly — median across mature cohorts, not the best one — and flags when it deteriorates before cash does.

Frequently Asked Questions

How many cohorts before the curves are trustworthy?

Six mature cohorts minimum for a stable payback read; three to spot slope direction. Below that, use payback assumptions from category benchmarks, labelled as assumptions, and let the curves graduate into decisions as they mature.

Do one-time-purchase businesses need this?

Less — without repeat behaviour the curve is a single point (month-0 margin vs CAC), which is just contribution-margin ROAS. The method earns its keep exactly where repeat or subscription revenue exists. If under 15% of revenue is repeat, deprioritise this.

Should CAC in the curve be blended or channel?

Match the curve's scope: channel-split curves use channel CAC (with the double-counting caveat from the blended-vs-channel post); the business-level curve uses blended CAC. Mixing channel revenue with blended CAC flatters every channel simultaneously.

Summary & Next Steps

Snapshot metrics punish young cohorts and flatter old ones. Cohort payback curves — cumulative contribution per customer by acquisition vintage — separate acquisition quality from retention slope and give payback as a readable crossing point.

• Use cohort payback curves as the budget instrument for repeat-revenue businesses.
• Use payback period as the cash-constraint number derived from the curves.
• Use LTV:CAC as the worth gate, never as the timing plan.