Regardless of what method you use to measure churn, all methods rely heavily on using pre-determined time intervals (like months). One challenge of this approach is that it can hide trends over time by mixing together many different types of customers. For example, in a given month you might lose three customers who have been with you for over a year and 10 customers that have been with you only a month. You’d likely want to know why you lost those three long-term customers, but they are likely lost in the noise of your churn metric.
For this reason, churn is almost always measured and visualized according to customer cohorts. This kind of cohorting can allow you to see how churn is changing over time as you improve your product, adjust pricing and make other changes to your business. For example, a cohort could be the month in which a customer started using your product.
Let’s look at a numeric example of how customers churn by their monthly acquisition cohort. In this example, I’ll use the Retention Rate (which is the inverse of the Churn Rate), because it can be more intuitive to see retention falling over time instead of churn increasing. It’s up to you which you find easier to understand:
|Month Became a Customer||Month 0||Month 1||Month 2||Month 3||Month 4||Month 5||Month 6||Month 7|
For each month (March through September), this chart shows how customers churn each month after they first become customers. The first row says that for customers who started in March, the retention rates for each month thereafter. Month 0 will always be 100% because that is the month the first month the customer was a customer so they cannot have churned. Going across the row, the retention rate for each cohort is always less than or equal to the value in the previous column because the cohort cannot grow as time passes.
Note that not all rows have values for all 7 months after, since those months might not have happened yet! This particular chart is looking at your data assuming that it is currently October, so customers acquired in September have had only one month from which we could measure their retention.
Thinking about churn by cohort makes it easy to spot patterns. Looking down the Month 1 column, starting in March, our customer retention was around 2% until July, when it started dropping – falling all the way to 1.21% for customers acquired in September. Clearly something we changed in the business is resulting in higher churn and we need to fix it!
In Review: Churn rates can be tricky to calculate, but are an incredibly valuable tool in understanding and improving your customer retention over time. No matter what kind of business you are in, you can and should be measuring your churn rate as a key performance indicator.
Next Week: Now that you understand your churn rate, it’s time to think about acquisition! Churn rates measure the downside of customer engagement, i.e., losing them. The acquisition rate measures the upside, calculating how quickly you are adding customers. Combined, these tell you your growth rate, which is a measure of how fast your business is growing. We’ll cover that, and why it’s not as easy as it sounds, next week.
Do you need help understanding your customers? Outlier is a product designed to help! Outlier looks deep into who your customers are and how they behave to highlight changes that might indicate potential causes of churn. If you’re interested in seeing a demo, schedule a time to talk to us.
Quote of the Day: “In ancient times, a cohort was a military unit, one of ten divisions in a Roman legion.” – Origin of the word “cohort”