Shopify cohort analysis: a practical guide

A Shopify cohort analysis groups customers by the month of their first order and then tracks how many of them order again in each month that follows. Each group is a cohort. Comparing cohorts shows whether retention is improving, flat or getting worse, which a single store-wide number cannot show.

For example, the January cohort is every customer whose first order was in January. If 1,000 people joined in January and 90 of them ordered again in February, the January cohort’s month 1 retention is 9%.

A store-wide repeat purchase rate mixes customers you won five years ago with customers you won last week. When you acquire many new customers, the rate drops even if they behave exactly like earlier customers, because they have not had time to come back yet. When acquisition slows, the rate rises even if loyalty is falling.

Cohorts remove that noise. Each customer is compared only with people who started at the same time, so a change in a cohort’s curve reflects a real change in behaviour, such as a new product, a different discount or a shift in which channel brought them in.

A retention table has one row per cohort and one column per month since the first order. Each cell shows the share of the cohort that placed an order in that month. Reading across a row shows how one cohort behaves over time. Reading down a column compares cohorts at the same age, which is the comparison that matters.

The table below uses illustrative numbers to show the shape, not benchmarks. Retention drops sharply after the first month and then flattens. The level where the curve flattens is your loyal base: the customers who keep buying without being won again.

Month 1 retention fell from about 10% in February to 6% in April, while the number of new customers grew. A store-wide repeat rate would hide that drop behind the larger April cohort. The next question is what changed for April buyers: a new ad audience, a heavier discount, a different best-selling first product or a delivery problem.

Cohorts do not answer that question on their own. Split the same table by acquisition channel, first product or discount code, and the cause usually becomes visible within a few rows.

You can build a basic cohort table in a spreadsheet from a Shopify order export. The steps below assume one row per order with a customer ID, an order date and an order status.

Monthly retention counts customers who ordered in a given month. It is useful for spotting when people come back, which helps you time a second-purchase email. Cumulative retention counts customers who have ordered again at any point up to that month. It always rises and makes it easier to compare cohorts at a glance.

Most teams look at cumulative retention to judge whether a cohort is healthy and at monthly retention to plan campaigns. Whichever you choose, use the same definition every time so cohorts stay comparable.

Cohort analysis is useful when it changes a decision. Three decisions it informs well are where to spend on acquisition, when to send retention campaigns and which products to lead with.

Avantages builds the cohort retention table from your Shopify orders automatically and keeps it up to date every day. Each row follows one month of first-time buyers, and the daily AI briefing tells you when a new cohort starts to behave differently from the ones before it.