An effective hybrid learning system for telecommunication churn prediction

Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Moreover, high predictive accuracy and good interpretability of the results are two key measures of a classification model. More studies have shown that single model-based classification methods may not be good enough to achieve a satisfactory result. To obtain more accurate predictive results, we present a novel hybrid model-based learning system, which integrates the supervised and unsupervised techniques for predicting customer behaviour. The system combines a modified k-means clustering algorithm and a classic rule inductive technique (FOIL). Three sets of experiments were carried out on telecom datasets. One set of the experiments is for verifying that the weighted k-means clustering can lead to a better data partitioning results; the second set of experiments is for evaluating the classification results, and comparing it to other well-known modelling techniques; the last set of experiment compares the proposed hybrid-model system with several other recently proposed hybrid classification approaches. We also performed a comparative study on a set of benchmarks obtained from the UCI repository. All the results show that the hybrid model-based learning system is very promising and outperform the existing models. With recent evolution in the Information and Communication Technology (ICT) sector, numerous new and attractive services have been introduced, and they put huge pressure on traditional services. Customer churn has emerged as one of the major issues in Customer Relationship Management (CRM) in telecommunica-tion services around the world, for both wireless providers and long-distance carriers. For instance, in the U.S., telecom providers of long-distance and international services have been bearing the churn rates from 45% to 70% percent for some years (Mattison, 2001). Under the fierce competitive environment, it becomes very important for the telecom operators to retain their existing customers as acquiring new customers is much more expensive. Consequently , predicting which customers are likely to stop their subscription and switch to competitors (churn) is critical. Predicting the potential churners and successfully retain them, especially the valuable ones, can substantially increase the profitability of a company. In the telecommunications industry, operators usually capture the transactional data, which reflects the service usage, and some static data such as subscriber's personal information and contract details. Data mining (DM) methods have emerged as a good alternative to study the …