Vol. 8(27), pp. 1289-1295, 18 July, 2013 DOI 10.5897/SRE2013.5559 ISSN 1992-2248 © 2013 Academic Journals http://www.academicjournals.org/SRE Scientific Research and Essays Full Length Research Paper Customer churn prediction using a hybrid genetic programming approach Ruba Obiedat 1 , Mouhammd Alkasassbeh 2 , Hossam Faris 1 * and Osama Harfoushi 1 1 Department of Business Information Technology, The University of Jordan, P. O. Box 11942 Amman, Jordan. 2 Department of Information Technology, Mutah University, P. O. Box 61710, Karak, Jordan. Accepted 3 July, 2013 A churn consumer can be defined as a customer who transfers from one service provider to another service provider. Recently, business operators have investigated many techniques that identify the customer churn since churn rates leads to serious business loss. In this paper, a hybrid technique has been used which combines K-means clustering with Genetic Programming to predict churners in telecommunication companies. First, K-means clustering is used to filter the training dataset from outliers and non representative customer behaviors then Genetic Programming is applied in order to build classification trees that are able to classify customers into churners and non churners. The proposed approach is evaluated and compared with other common classification approaches. Experimental results show that K-means clustering with Genetic Programming has promising capabilities in predicting churners’ rates. Key words: Churn consumer, churn customer, K-means clustering, Genetic Programming INTRODUCTION Recently, mobile telecommunications became the superior communication medium and sharing data between the callers all over the world. Sanou (2013), in the International Telecommunication Union reveals that there were around 6.8 billion mobile subscriptions, which means 96% of the world population. The mobile subscriptions here mean the SIM cards and most of the mobile users have more than one SIM card. In many countries, usually, there is more than one mobile provider who gives different services to attract new customers and keep the exciting ones; every now and then there are latest new mobile phones offers and promotions to keep the company alive and prosperous. At the same time, public regulations and the standardization of mobile communication allow customers to easily move from one provider to another, which is called a churn consumer or churn customer; as a result, churn prediction has raised a crucial mobile Business Intelligence (BI) application that aims at identifying the customer who is about to leave to a competitor or stay with the same provider. This information is very important for the managers to set-up their plans for the future, technically or commercially. To endure in the stimulating atmosphere of an international market and be competitive, organizations essentially identify and predict customer predilections and behaviors to make the most of customer retention before their rivals do so. Customer churn management includes three steps: determination and identification of churn customers, investigating the reasons of churn and application of certain policies; as well as taking measures to deteriorate the rate of churn (Rodpysh, 2012). Some of the major data tasks in data mining are prediction and classification, which can be applied to extract knowledge by using the data available regarding customers’ behaviors. Nowadays, this technique is used for customers churn management and customers relation management (Rodpysh, 2012). A study carried out by *Corresponding author. E-mail: 7ossam@gmail.com.