Customer retention - target repurchase and cohort analysis

What do the customer retention statistics show?

Under Statistics → Customer retention, you can see how good you are at retaining your customers over time. The site uses cohort analysis to show if and when your customers return to shop again.

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 a webshop with ambitions for long-term growth.

The four key metrics at the top

• Reorder rate - the percentage of customers who have shopped more than once. A reorder rate of 20-30% is typical for many webshops, but it varies greatly by industry.
• Average time to repurchase - how long it typically takes from a customer's first purchase until they return. This insight is valuable for timing follow-up emails.
• Best cohort - the month group of new customers with the highest retention rate. It can reveal which campaigns or seasons attract the most loyal customers.
• New customers - the number of first-time customers in the selected period, so you can see if you are 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 first shopped in your online store. For example, the "January 2026" cohort is all customers who placed their very first order in January 2026. By grouping customers like this, you can compare how different groups behave over time.

How to read the heatmap

The heatmap is a grid with color-coded cells:

• The rows represent cohorts - groups of customers based on when they first shopped (e.g. "Jan 2026", "Feb 2026")
• Columns represent months after first purchase - M0 is the month they first shopped, M1 is the month after, M2 is two months after, etc.
• The cell value shows the percentage of the cohort that shopped again in that month
• Colors range from light (low retention) to dark (high retention)

The M0 column always shows 100% because by definition all customers shopped in their first month. Look at M1, M2, M3, etc. to see how many are returning. A typical online store sees a large drop from M0 to M1, and then a more gradual flattening.

For example

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

Tips to improve customer retention

• Use average time to repurchase to time 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 promotion, a new product or a seasonal effect?
• Enable a loyalty program - a points system gives customers an incentive to return. It can have a noticeable effect on your re-order rate.
• Segment your customers - use customer statistics to understand who your best customers are and target your communication.
• Focus on the customer experience - fast delivery, good customer service and an easy returns process are key to keeping customers coming back. 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.