Churn prediction: reduce customer churn with historical data

As the name implies, churn prediction predicts how many customers run an increased chance of dropping out in the future. That may sound like a negative take, but it also has a positive side. 

If you utilise churn prediction effectively it can be a fantastic way to build loyalty with the customers who do stay and to increase retention. Churn prediction insights direct you to the customer segments you should focus on, and signal which behaviours you want to prevent. 

In addition, churn prediction — like other forms of Data modelling — saves your organisation time, money, and energy by letting the model dig into your mountains of data, instead of doing it manually. 

The result: a more efficient organisation that spends less time on everyday tasks, and an has objective way to identify which customers need extra attention. 

Churn prediction is particularly interesting for organisations with a subscription model, in the broad sense of the word. 

We are talking about organisations with customers that return structurally, such as a streaming service, an insurer, or an energy supplier. But it can also be relevant for e-commerce players and non-profit organisations that rely on recurring contributions. 

With churn prediction, organisations can predict with more accuracy who is most at risk of abandoning their "subscription" — and take the necessary steps to keep those people with them as customers or members.