A calendar month mixes customers who arrived yesterday with customers who arrived three years ago. A cohort follows one group from its first order forward. Only one of them can tell you whether anything you changed worked.
Most ecommerce reporting is by calendar month: revenue in March, repeat rate in March, customers in March. It feels natural and it hides almost everything. March’s revenue includes new customers you paid for last week and loyal customers you won in 2022. If the welcome offer got worse in January, March can still look fine, because the old customers are carrying it.
A cohort is everyone whose first order fell in the same period. Follow a cohort forward and you can see what those customers did in their first 30, 90, 180 and 365 days. Compare cohorts and you can see whether customers you won this spring are behaving better or worse than those you won last spring. That’s the only fair test of a change in offer, creative, channel or flows.
Calendar reports tell you how the business did. Cohort reports tell you whether it’s getting better at what it does.
From my work: the most common cohort mistake I see is a report that’s labeled 90-day, 180-day and 365-day lifetime value but is really three calendar windows stacked back from today. The giveaway is the customer count. In a true cohort view, the count falls as the window gets longer, because only customers acquired at least 365 days ago can have a 365-day value. If the count rises with the window, it’s not a cohort report. The First Offer walks through how to rebuild it; Appendix A has the query.
Sort every customer you’ve ever had by lifetime spend and the shape is almost always lopsided. Picture a brand with 20,000 customers and $2 million of lifetime revenue.
| Group | Customers | Share of revenue | What it means |
|---|---|---|---|
| Top 1% | 200 | 15% | Named people. Treat them like accounts. |
| Top 10% | 2,000 | 45% | The base the business stands on. |
| Bought once | 14,000 | 35% | Seven in ten customers, a third of revenue. |
PictureMade-up round numbers with a shape I see often. Pull your own; the tool below uses them.
That shape suggests two separate programs, with different owners and different numbers. A top-customer program, whose goal is to lose none of them. And a one-time-buyer program, whose goal is to move as many as possible to a second order.
The first program is small in headcount and large in money. Give it a named owner and a list of actual people, not a segment definition. Watch recency on every one of them: when someone who normally orders every six weeks hasn’t ordered in ten, that’s a phone call or a personal note, not an automated winback with a code. Give them first access to launches, a direct line to a person, and the occasional unexpected thank-you. Measure it on one number, the share of last year’s top customers who are still active this year, and aim to lose almost none.
The second program gets easier to fund once you price it. Compare the average lifetime spend of customers with exactly one order and customers with exactly two. The difference is the step-up: roughly what a one-time buyer is worth if they place a second order. Multiply by the number of one-time buyers and a reactivation rate and you have a dollar figure for the program.
Treat that figure as a ceiling, not a forecast. Customers who reorder on their own are the more committed ones, so a customer you nudge back is probably worth less than the average two-order customer. Measure the real effect with a holdout, as in chapter 8, and use the step-up to decide whether the program is worth building at all.
| Back | People | Revenue | Net |
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This is one chapter of The Whole Machine, which is free and readable in full on a single page with no form in front of it.