A Genetic Programming Based Framework for Churn Prediction in Telecommunication Industry

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8733)


Customer defection is critically important since it leads to serious business loss. Therefore, investigating methods to identify defecting customers (i.e. churners) has become a priority for telecommunication operators. In this paper, a churn prediction framework is proposed aiming at enhancing the ability to forecast customer churn. The framework combine two heuristic approaches: Self Organizing Maps (SOM) and Genetic Programming (GP). At first, SOM is used to cluster the customers in the dataset, and then remove outliers representing abnormal customer behaviors. After that, GP is used to build an enhanced classification tree. The dataset used for this study contains anonymized real customer information provided by a major local telecom operator in Jordan. Our work shows that using the proposed method surpasses various state-of-the-art classification methods for this particular dataset.


Churn prediction Genetic Programming Self Organizing Maps Telecommunication 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.The University of JordanAmmanJordan

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