Nowadays customer churn has become the main concern of companies which are active in different industries. Among all industries which suffer from this issue, telecommunications industry can be considered in the top of the list with approximate annual churn rate of 30%. Dealing with this problem, there exist. different approaches via developing predictive models for customer churn but due to the nature of pre-paid mobile telephony market which is not contract-based, customer churn is not easily traceable and definable, thus constructing a predictive model would be of high complexity. Handling this issue, in this study, we developed a dual-step model building approach, which consists of clustering phase and classification phase. With this regard firstly, the customer base was divided into four clusters, based on their RFM related features, with the aim of extracting a logical definition of chum, and secondly, based on the churn definitions that were extracted in the first step, different algorithms were utilized with the intention of constructing predictive models for churn in our developed clusters. Evaluating and comparing the performance of the employed algorithms based on "gain measure", we concluded that employing a multi-algorithm approach in the model constructing step, instead of single-algorithm one, can bring the maximum gain among the tested algorithms.
