Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2748

Full Length Research Paper

Customer churn prediction using a hybrid genetic programming approach

Ruba Obiedat1, Mouhammd Alkasassbeh2, Hossam Faris1* and Osama Harfoushi1
1Department of Business Information Technology, The University of Jordan, P. O. Box 11942 Amman, Jordan. 2Department of Information Technology, Mutah University, P. O. Box 61710, Karak, Jordan.
Email: [email protected]

  •  Accepted: 03 July 2013
  •  Published: 18 July 2013

Abstract

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.