Customer retention - target repurchase and cohort analysis

What do the customer retention statistics show?

Under Statistics → Customer Retention, you can see how well you’re retaining your customers over time. The page uses cohort analysis to show whether your customers return and make repeat purchases—and when they do so.

It’s far cheaper to retain an existing customer than to acquire a new one. That’s why customer retention is one of the most important metrics for an online store with ambitions for long-term growth.

The four key metrics at the top

• Repurchase Rate — the percentage of customers who have made more than one purchase. A repurchase rate of 20–30% is typical for many online stores, but it varies greatly by industry.
• Average time to repeat purchase — how long it typically takes from a customer’s first purchase until they return. This insight is invaluable for timing follow-up emails.
• Best cohort — the monthly group of new customers with the highest retention rate. This can reveal which campaigns or seasons attract the most loyal customers.
• New customers — the number of first-time customers during the selected period, so you can see if you’re attracting new customers at a healthy rate.

What is a cohort?

A cohort is a group of customers who share a common trait—in this case, the month they made their first purchase in your online store. For example, the “January 2026” cohort consists of all customers who placed their very first order in January 2026. By grouping customers this way, you can compare how different groups behave over time.

How to Read the Heatmap

The heatmap is a grid of color-coded cells:

• The rows represent cohorts—groups of customers based on when they made their first purchase (e.g., “Jan 2026,” “Feb 2026”)
• The columns represent months after the first purchase—M0 is the month they made their first purchase, M1 is the following month, M2 is two months later, and so on.
• The cell value shows the percentage of the cohort that made another purchase in that month
• The colors range from light (low retention) to dark (high retention)

The M0 column always shows 100% because, by definition, all customers made a purchase in their first month. Look at M1, M2, M3, etc., to see how many return. A typical online store sees a sharp drop from M0 to M1, followed by a more gradual leveling off.

Example

Imagine that the January cohort has 200 new customers. If M1 shows 12%, that means 24 of the 200 customers made a purchase again in February. If M2 shows 8%, 16 of the original 200 customers made a purchase in March. By comparing cohorts, you can see whether newer customers are more or less loyal than older ones.

Tips for Improving Customer Retention

• Use the average time to repurchase to time your follow-up —if customers typically return after 45 days, send a follow-up email via the newsletter module around day 40 with relevant product suggestions or an offer.
• Compare cohorts —if one cohort performs significantly better than others, investigate what was different that month. Was there a specific campaign, a new product, or a seasonal effect?
• Activate a loyalty program —a points system gives customers an incentive to return. It can have a noticeable effect on your repeat purchase rate.
• Segment your customers —use customer statistics to understand who your best customers are and tailor your communication accordingly.
• Focus on the customer experience —fast delivery, good customer service, and an easy returns process are crucial to whether customers return. It’s not just about price and products.

Customer retention is a long-term investment. Use cohort analysis to continuously evaluate whether your initiatives are working, and adjust your strategy based on data—not gut feelings.