An approach with flexible choice of model for customer churn prediction and retention help

Abstract

Customer churn is one of the most critical issues faced by companies. These turn towards prediction techniques to predict the churn of their customers, because it is more expensive to acquire a new customer inside of retaining existing one. In this paper, we propose a process-based approach to detect potential customer churn and provide early warning indicator of problems that could lead to customer's loss and open up opportunities to implement effective retention strategies. The predictive churn model is determined by applying a set of data mining and machine learning algorithms, in order to keep flexible choice of the best prediction algorithm. Once the categories of churners are determined, association rule mining algorithm is applied to analyze and detect customer churn causes. The proposed approach is based on the CRISP-DM process with flexible choice of predictive model since it implements different machine learning algorithms and allows the selection of the most appropriate one for better churn prediction (the best model). The proposed approach is illustrated on a case study and the results indicate that the system is effective in detecting customer churners and addressing appropriate retention solutions.