Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining

Abstract

Customer churn in the mobile telephony industry is a continuous problem owing to stiff competition, new technologies, low switching costs, deregulation by governments, among other factors. To address this issue, players in this industry must develop precise and reliable predictive models to identify the possible churners beforehand and then enlist them to intervention programs in a bid to retain as many customers as possible. This paper proposes a new set of features with the aim of improving the recognition rates of possible churners. The features are derived from call details and customer profiles and categorized as contract-related, call pattern description, and call pattern changes description features. The features are evaluated using two probabilistic data mining algorithms Naïve Bayes and Bayesian Network, and their results compared to those obtained from using C4.5 decision tree, a widely used algorithm in many classification and prediction tasks. Experimental results show improved prediction rates for all the models used. Keywords: Customer churn, data mining, classification / prediction, decision tree, Naïve Bayes and Bayesian Network.