Active vs passive churn, and why the split matters

In this mini-episode of Datadrivet, Joni Lindgren and Jasmin Yaya split churn into two kinds that look the same in the numbers but have nothing in common underneath.

Active churn is when a customer makes a conscious choice to leave. They decide they no longer want to be a customer and they cancel or stop paying. Jasmin’s example is cancelling her own Netflix subscription: a deliberate act.

Passive churn is when a customer leaves without meaning to, which is why the hosts also call it unintentional churn. The customer doesn’t even know they’ve left. The classic cause is a failed payment, for example a credit card that has expired while the customer still wants the service.

The reason to keep these two apart is that the cause is different, so the fix is different. A customer who decided to leave has a value problem you have to address before they reach the cancel button. A customer who left by accident has a billing problem you can often recover with a reminder or a card-update prompt, no persuasion required. Lumping both into one churn rate hides which one is actually draining you.

The takeaway: before you try to lower churn, separate the customers who chose to go from the ones who slipped away by accident, because you can’t fix both with the same move.

The hosts invite listeners to share how their own companies measure and reduce churn over on LinkedIn.

Listen to the full episode of Datadrivet for the full breakdown. If you want to know whether your churn rate is normal for your model, that is what the benchmark tool is for: https://benchmark.scilla.studio