Shopify Customer Cohort Analysis — Understanding Repeat Purchase Behaviour

What Cohort Analysis Reveals That Other Reports Hide

Standard Shopify reports show you aggregated numbers — total revenue, total customers, average order value. These aggregates are useful for monitoring store health but they obscure one of the most important dynamics in ecommerce: whether the customers you acquired last month behave differently from the customers you acquired six months ago. Cohort analysis solves this. By grouping customers by the period in which they made their first purchase, you can compare the retention and revenue trajectories of different acquisition batches and understand precisely where your customer relationship is winning or losing over time.

What Is a Customer Cohort?

A cohort is simply a group of customers who share a common characteristic — in most ecommerce analysis, that characteristic is the month of their first purchase. The January 2025 cohort is every customer who made their first purchase in January 2025. The February 2025 cohort is everyone who first purchased in February 2025, and so on. You then track each cohort forward in time to see what percentage of them made a second purchase in month two, a third purchase in month three, and so on.

This time-based comparison reveals patterns that aggregate data completely hides. You might find that customers acquired during your Black Friday sale have a 90-day repurchase rate of only 8 percent — half your average — because they were deal-seeking one-time buyers. Meanwhile customers acquired through organic search in non-promotional periods might show a 90-day repurchase rate of 25 percent. These two groups should be treated very differently in your retention marketing and in how you account for their acquisition cost.

Accessing Cohort Data in Shopify

Shopify includes a basic cohort analysis tool in the Analytics section under Reports. The Customer Cohort Analysis report (available on Shopify plan and above) shows monthly cohorts across the left column and tracks what percentage of each cohort made a purchase in subsequent months. Reading the grid is straightforward: each row is an acquisition month, and each column to the right of it represents the following months. The numbers show the percentage of that cohort who purchased in each period.

For stores on Basic Shopify, you can replicate cohort analysis by exporting your customer CSV, filtering by first order date, and building the retention table in Google Sheets. It is more manual but the insight is identical.

How to Read a Cohort Table

When you open your cohort report, look at the Month 1 column first — this is the percentage of each acquisition cohort who made a second purchase within their first 30 days after the initial order. This number varies enormously by product category. Consumables and supplements with natural replenishment cycles often see 15 to 25 percent second-order rates within 30 days. Single-purchase or infrequently replaced products like furniture or mattresses may show near zero for the first few months before a longer-tail repurchase signal appears around month 12 to 18.

Once you understand your category baseline, look horizontally across each cohort to see how quickly the repurchase rate stabilises. Most cohorts follow a similar curve — a burst of second orders in the first 30 to 60 days, then a slower trickle for the remainder of the year. The shape of this curve tells you a great deal about your natural product purchase cycle and whether your retention emails are accelerating that cycle or not.

Identifying Winning Cohorts

Compare cohorts that were acquired during different periods or through different channels to identify which acquisition approaches produce the most valuable customers. Look for cohorts where the Month 3 and Month 6 repurchase rates are meaningfully higher than your average. Ask what was different about those periods. Were you running a specific type of campaign? Did you feature particular products? Was there a brand collaboration or press mention that brought in a different audience? Winning cohorts are your template — they tell you the conditions that produce your best customers.

Diagnosing Weak Cohorts

Weak cohorts — those with below-average repurchase rates — deserve equally careful attention. A weak cohort acquired during a heavy discount period often confirms what most experienced store owners already suspect: discount-led acquisition attracts price-sensitive buyers who leave the moment the discount disappears. If your data confirms this pattern, it is a strong argument for reducing promotional depth and redirecting that margin investment into channels that attract intrinsically motivated buyers.

A weak cohort from an influencer campaign might suggest the influencer’s audience was not genuinely aligned with your product despite high surface-level engagement metrics. Cohort data is one of the best ways to evaluate the true quality of an influencer partnership beyond the initial sales spike.

Using Cohort Insights to Improve Retention

Once you understand where your cohort curve flattens — the point at which a cohort stops generating new repurchases — you have a target for your email and SMS retention programmes. If the average customer makes their second order between day 45 and day 75, build a retention email flow that delivers the highest-impact touchpoint at day 30. If Month 6 shows a meaningful secondary repurchase spike, add a winback campaign at Month 5 to pull that repurchase forward.

Post-purchase sequences are the most direct lever. A well-timed product recommendation email based on what customers in a similar cohort bought second — sent at the point in the purchase cycle when second orders typically cluster — can shift your Month 1 and Month 2 retention rates meaningfully. Even a one or two percentage point improvement in 30-day second-order rate, compounded across your full customer base, represents significant annual revenue.

Cohort Analysis for Subscription Products

If you sell subscriptions through Shopify, cohort analysis takes on a different form. Instead of tracking repurchase rate, you track subscription retention — what percentage of each cohort’s subscribers are still active at Month 1, Month 3, Month 6, and Month 12. The subscription cohort curve is your churn map. Where the curve drops sharply — say between Month 1 and Month 2 — is where you need to focus your onboarding and engagement efforts. A sharp early drop usually indicates a gap between customer expectations set during acquisition and the actual experience of the product.

Tools Beyond Shopify Native for Deeper Cohort Work

For stores that want more granular cohort analysis — segmented by acquisition channel, product category, or geographic market — tools like Lifetimely, Triple Whale, or Northbeam provide deeper cohort capabilities than native Shopify reports. These platforms pull in your Shopify order data alongside your ad spend data to calculate channel-specific LTV curves, showing you not just what your average cohort looks like but what a Facebook-acquired cohort versus a Google-acquired cohort looks like at 12 months. That level of channel-specific LTV insight directly informs how much you should be willing to bid on each platform.

Understanding your cohort data deeply is the foundation of a profitable scaling strategy. Without it, you are making acquisition and retention decisions based on averages that obscure the behaviour of your actual best customers. If you would like support setting up cohort analysis or interpreting what your data is showing, our ecommerce services team can help. View examples of our work at our portfolio or reach out directly.