Abstract

   Nowadays, organizations are facing several challenges resulting from competition and market trends. Customer churn is a real issue for organizations in various industries, especially in the telecommunications sector with a churn rate of approximately 30%, placing this industry in the top of the list. Because higher expenses are involved when trying to attract a new customer than trying to retain an existing one, this is an important problem that needs an accurate resolution. This paper presents an advanced methodology for predicting customers churn in mobile telecommunication industry by applying data mining techniques on a data set consisting of call detail records. The data mining algorithms considered and compared in this paper are Multi- Layer Perceptron and Radial Basis Function neural networks.