Customer churn prevention

Our client, an insurance company with a large customer base, sees substantial potential in actively preventing customer churn. Their existing customers are important revenue generators with ongoing premium payments, valuable up- and cross-selling opportunities and by generating new leads through recommendations. Consequently, the company pursued the goal of developing a new model for determining the willingness of customers to change in order to draw conclusions about customer satisfaction.

Implementation

To model the cancellation forecast, ZENAI analysed historical customer data across all communication channels and information from the entire customer life cycle. The consolidated data is the training basis of the Machine Learning model for churn prediction. The churn motives are recorded with systematic feedback. Targeted measures are then developed on the basis of this data in order to actively serve other customers in the same segments and successfully prevent them from churning.

Result

Today, our client maintains an optimised portfolio of policies and proactively manages its customers, which significantly increases customer loyalty and ensures a stable customer base.