Churn (also known as the cancellation or attrition rate) measures how many customers are lost over a given period. With subscriptions, it’s straightforward: the customer cancels. In retail and e-commerce, customers don’t give notice when they leave, so churn is defined by a rule—for example, “hasn’t made a purchase in the last 12 months.”

That rule depends on your business: a grocery store is not the same as a furniture store.

Why does it matter?

High churn forces you to acquire more and more new customers just to maintain sales. Reducing it—even slightly—improves LTV and the return on your acquisition investment.

Furthermore, churn is rarely sudden. Before leaving, customers buy less, space out their purchases, or stop opening your messages. Detecting those signs early on is the difference between retaining customers and having to win them back.

How is it calculated?

Formula: (customers lost during the period ÷ customers at the beginning of the period) × 100.

For example, if you had 2,000 active customers at the beginning of the year and 500 of them did not make another purchase in the following 12 months, your annual churn rate is (500 ÷ 2,000) × 100 = 25%.

Story mode

Martín used to buy sportswear every two months from an online store. He hasn't returned in five months. The store notices this because he has deviated from his usual purchasing pattern and sends him a personalized message featuring new items from his favorite brand.

Martín is buying again. Without that alert, he would have become another customer lost this year.

How to Use It with Data4Sales

Data4Sales helps you measure and reduce churn using unified data from all your channels:

• The churn probability model estimates which customers are most likely to leave.
• The lifecycle categorizes customers as "At Risk" and "Inactive," with segments that are updated daily.
• Reactivation flows are triggered automatically via WhatsApp, email, or SMS.
• Orbis Creative crafts a unique message for each client based on their data.

Related terms

• Retention Rate
• Churn Probability
• Customer at risk
• Lost Customers