Using Hybrid Data Mining Techniques for Facilitating Cross-Selling of a Mobile Telecom Market to Develop Customer Classification Model

Abstract

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more ARPU (average revenue per user). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model. Our model uses the cumulated data on the existing customers including the patterns for using old products or services to find prospects for purchasing. The data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques are applied independently. In the second step, our model compromises all these probabilities by using genetic algorithm. To validate the usefulness of our model, we applied it to a real-world mobile telecom company's case in Korea.