Glossary

Your go-to resource for acronyms, jargons, terminology, and useful words for product and customer experience teams.

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Revenue churn

What is revenue churn?

Revenue churn is the best way to measure how much a company is losing in potential earnings from clients or customers who choose not to renew their subscriptions over a set period of time. This number, when contrasted with customer churn (which depicts how many clients or customers a company retains) provides CEOs and other business leaders with a valuable perspective from which they can gauge the relative health of their customer base.

How to calculate revenue churn?

Revenue churn can be measured in several ways, but one of the most popular methods is called Net Revenue Retention (NRR). To calculate it, you take into account the value of all accounts at the beginning of a set period, then look at any changes that occur during that period (i.e. from upsells, down sells, and customers who churn). If the revenue gained from upsells exceeds the revenue lost from down sells and churned customers, then your Net Revenue Retention Rate will be greater than 100%. This is often referred to as negative churn, and it’s considered very impressive in the business world – especially in subscription-based SaaS companies. A Net Revenue Retention Rate of 110% or higher is considered world-class in this industry.

Should I measure revenue churn or customer churn?

Churn is an important metric for businesses to track for a number of reasons. Revenue churn and customer churn are two ways to measure churn, and by comparing the two, a business can see if retention is consistent across its customer base. If small customers have higher customer churn but lower revenue churn, or if large customers have lower customer churn but higher revenue churn, this could indicate that there are issues with retention in one or both groups of customers. By tracking multiple measures of overall customer health, an organization can prevent over-reliance on one metric and be more likely to detect data issues before they become problematic.