Data Mining Techniques on the Evaluation of Wireless Churn

Abstract

Abstract. This work focuses on one of the most critical issues to plague the wireless telecommunications industry today: the loss of a valuable subscriber to a competitor, also defined as churn. Analytical methods and models intrinsic to decision technology and machine learning are here evaluated, in an effort to provide the necessary intelligence to identify and understand troublesome customers in order to act upon them before they churn. Making use of a large real-world database, a thorough analysis is performed. First, due attention is given to data representation, with input selection methods being employed in the search of the most relevant attributes. Then, the predictive and explanatory power of four families of models is compared: neural networks, decision trees, genetic algorithms and neuro-fuzzy systems. To conclude, light is shed upon the possible savings and profits resulting from the application of the developed methodology in the retention strategies of wireless carriers. 1.