Usage-Based Decision Tree for Telco Churn Prediction

Telecommunication has a common problem of suffering from high churn rate, e.g. an existing customer may leave the service provider or shift to another. Churn prediction is the task of predicting the likelihood that a certain group of customers churn, and customer segmentation has been widely applied for a target marketing strategy. Previous work has adopted various clustering and classification methods, among which support vector machines and neural networks have achieved better performance, but the possible reason why customers churn is rarely explored in details. This article aims to address the gap of understanding customer churn using customers’ usage of services. To do this, an integrated framework containing two phases was built: we carried out predictive analysis using churn prediction and association analysis (phase one), based on which we provided recommendations in the decision analysis (phase two). The experimental results showed that the proposed Usage-based Decision Tree (UsageDT) achieved better performance in understanding the churners/non-churners in terms of interpretability, higher precision, and much higher true positive rate. The usage-based association analysis was integrated seamlessly with churn prediction, and deepened our understanding of the churn phenomena. Evaluation carried out with two professionals in telecommunication industry also revealed the usefulness of our recommendations.