A Hybrid Data Mining Architecture for Customer Retention

Abstract

Competition in the wireless telecommunications industry is fierce. To maintain profitability, wireless carriers must control churn, which is the loss of subscribers who switch from one carrier to another. This paper proposes a hybrid architecture that tackles the complete customer retention problem, in the sense that it not only predicts churn probability but also proposes retention policies. The architecture works in two modes, namely, the learning and usage modes. In the learning mode, it learns potential associations inside the historical subscriber database to form a churn model. It then uses the attributes that appear in the churn model to segment all churners into distinct groups. It is also responsible for developing a specific policy model for each churner group. In the usage mode, the churner predictor uses the churn model to predict the churn probability of a given subscriber. A high churn probability will cause the system suggest specific retention policies according to the policy model. Our experiments illustrate that the churn prediction has around 85% of correctness in evaluation. Currently, we have no proper data to evaluate the constructed policy model. The policy construction process, however, signifies an interesting and important approach toward a better support in retaining possible churners.