December 14th, 2011
(written by lawrence krubner, however indented passages are often quotes). You can contact lawrence at: firstname.lastname@example.org
The problem here is that the [number of churns over period] value is affected by the entire period but the [number of customers at beginning of period] value is a snapshot from the beginning of the period. This might not have much impact if new customers only make up a small percentage of your user base but for a company that’s growing this can lead to some major misinterpretations.
Consider you are calculating your churn rate for July and August. Let’s say in July you started with 10,000 customers, lost 500 of them (5% of 10,000), gained 5,000 but lost 125 of those (you only lose 2.5% of the 5,000 because you gain those 5,000 over the course of the month). Now in August you start with 14,375 customers and lost a similar amount of 719 (5% of 14,375), gained another 5,000 and again lost 125 of them. So July’s churn rate would be 6.25% and August’s would be 5.87%. That’s a shift of 38 basis points for the exact same behaviour in both months (here the standard deviation is about 20~25 basis points depending on how you calculate it). You would have told yourself that your churn rate is improving when nothing has changed. Misreporting your performance is a bad problem to have.
And beyond that, what if July had been a dead month with only 100 new customers, 2 of which churned, and then August picked up again. Then July would have a churn rate of 5.02% and August would have a churn rate of 6.3%. This is because this definition of churn rate is directly influenced by the number of new customers you acquire. But the whole point of a churn rate is to understand churn behaviour normalized for growth and size.