Cohort Revenue Retention Curve Benchmarks
A practical guide to the cohort revenue retention curve: realistic month-by-month benchmarks by vertical, and how to tell decay from compounding.
Cohort Revenue Retention Curve
The cumulative revenue each acquisition cohort generates over time, indexed to its first-month spend.
The cohort revenue retention curve plots, for each monthly acquisition cohort, the cumulative revenue those customers have generated by month 1, month 2, month 3 and so on — usually expressed as a percentage of their first-month spend. It is the revenue twin of the LTV curve: where a survival curve tracks how many customers stick around, the retention curve tracks how many euros they bring back.
The shape tells you whether your business is leaky (steep decay), stable (slow flattening above zero) or compounding (climbing past 100% as repeat orders stack up). Most stores read it alongside the new vs returning revenue mix and the underlying cohort revenue analysis.
Each row of a cohort revenue table is one acquisition month. Each column is months-since-acquisition. The cell holds the revenue that cohort generated in that period, and the retention curve is the cumulative version of that row — month 2 adds month 1, month 3 adds month 2, and so on.
Indexing matters. If you plot raw euros, large cohorts dwarf small ones and you can't compare shapes. Divide every cell by the cohort's month-1 revenue and you get a curve that starts at 100% — now a January cohort and a September cohort sit on the same axis and you can see which acquisition month produced healthier repeat behaviour.
Typical cumulative revenue retention by vertical (indexed to month 1 = 100%)
| Vertical | M3 | M6 | M12 | M24 |
|---|---|---|---|---|
| Beauty & skincare (consumables) | 165% | 230% | 340% | 520% |
| Apparel & accessories | 130% | 175% | 240% | 330% |
| Home & lifestyle | 115% | 145% | 190% | 250% |
| Consumer electronics | 108% | 120% | 140% | 165% |
| Pet food & supplies (subscription-friendly) | 210% | 330% | 560% | 950% |
| Furniture & high-AOV one-shots | 104% | 110% | 120% | 135% |
Read the table by repurchase cycle, not by brand prestige. A pet-food brand crosses 200% by month 3 because the product runs out in six weeks. A furniture store barely moves past 110% because the next sofa is five years away — and that's fine, as long as the first-order margin pays for the acquisition.
Three retention shapes you'll see in a cohort revenue curve
Compounding (consumables / subscription)
Healthy decay (apparel / lifestyle)
One-shot flatline (furniture / electronics)
How to read the shape of your curve
Three diagnostics matter: the slope between month 1 and month 3, the gradient at month 6, and whether the curve is still climbing at month 12. A steep month 1-to-3 slope means your post-purchase flows are doing their job — replenishment emails, second-order discount, the welcome series are all earning their keep.
A curve that goes flat by month 6 but sits comfortably above 100% is normal for considered-purchase categories. A curve that flattens below 130% in a consumables category is a problem — that's a retention leak, not a category constraint, and it shows up before churn rates do because cohort revenue analysis aggregates frequency, AOV and repurchase in one line.
Don't compare cohorts of different ages on the same x-axis without trimming
A January cohort has 11 months of observed revenue; a November cohort has 1. If you average them, the curve collapses at the right edge for purely arithmetic reasons. Either truncate every cohort to its shortest observed window, or report each cohort separately and let the eye do the work.
Benchmarks by vertical and repurchase cycle
The benchmark numbers above are starting points, not targets. The right comparison is your own curve quarter-over-quarter: is the Q3 cohort retaining better at month 6 than the Q1 cohort did? That's the signal you care about, because it isolates what's changed in your post-purchase experience from the noise of seasonality and channel mix.
Pair the curve with LTV measurement and your CAC payback target. If month-12 retention sits at 240% and your first-order contribution margin is 35%, you're earning roughly 84 cents of margin per euro of month-1 revenue across the first year — enough to support a CAC that's 80% of AOV and still pay back inside twelve months.
Frequently asked questions
They're built from the same data but answer different questions. The retention curve indexes each cohort to its month-1 revenue so you can compare shapes across cohorts; the LTV curve reports cumulative euros per customer so you can compare it directly to CAC. Use the retention curve to spot trend changes, the LTV curve to make spend decisions.
Net of refunds and returns, but before discounts that vary by channel. Including refunds in the curve hides one of the biggest drivers of repeat behaviour, especially in apparel where return rates push 30-40%. Most stores also exclude shipping revenue to keep the curve comparable to contribution margin.
Six months gives you a readable short-term shape. Twelve months is the threshold for confident LTV decisions because it captures one full seasonal cycle. If you've only got three months of data, you can still spot relative cohort-over-cohort movement, but absolute benchmarks won't be reliable yet.
Almost always acquisition mix, not product. Black Friday cohorts skew toward discount-seekers and gift-buyers who don't repurchase, so the month-3 slope is flatter. Segment the curve by first-order discount tier and the effect usually pops out.
No — it's cumulative, so it can only stay flat or climb. If you see it going down, you're plotting period revenue (month-over-month), not cumulative. The two are easy to confuse; the cumulative version is the one that compares cleanly to LTV.
The new vs returning revenue mix is a store-level snapshot; the retention curve is a cohort-level history. A rising mix of returning revenue should show up in steeper retention curves for recent cohorts. If the mix is shifting but the curves aren't, you've got a denominator effect — usually slower new-customer acquisition rather than better retention.
Monthly is enough for most stores. Recompute on the first of the month with the prior month closed out. Weekly refreshes add noise without adding signal because cohorts move slowly.
Yes. Subscription cohorts have a fundamentally different shape — near-linear climbs that compound until churn — and mixing them with one-time-purchase cohorts averages out the most interesting part of both. Plot the two side by side instead.
200-260% is a typical range for healthy mid-market apparel brands. Below 180% suggests the second-order conversion is the bottleneck — usually a thin post-purchase email program or no replenishment cue. Above 280% is excellent and usually involves an active loyalty program or a strong sub-brand expansion.
Identify which cohort started flattening and what changed that quarter — channel mix, discount depth, product launches, post-purchase flow edits. Then run a controlled test on the suspected lever (second-order discount, replenishment reminder timing) on the next cohort and compare month-3 retention against the prior baseline.
Track CAC, channels, and funnel conversion in one place
Metricuno connects ad spend, funnel events, and revenue so you can see CAC by channel, cohort, and campaign — without stitching together five tools.