Churn prediction refers to the data-based prediction of whether a customer will leave the company. A machine learning model analyzes historical data — such as membership duration, contact behavior, tariff type, and past product usage — and identifies patterns that are typical for churning customers.

The model outputs a churn probability for each member. Sales representatives can proactively approach customers with elevated risk before they cancel. The decisive advantage: instead of reacting to cancellations, action is taken proactively.

In the ADAC Hansa project, the churn model complements the recommendation models for travel and trainings. Together, they provide a comprehensive picture of the customer situation.