Hybrid Data Mining Models for Predicting Customer Churn

Amjad Hudaib, Reham Dannoun, Osama Harfoushi, Ruba Obiedat, Hossam Faris^*

King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan.

DOI: 10.4236/ijcns.2015.85012                

Abstract

The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this behavior is very important for real life market and competition, and it is essential to manage it. In this paper, three hybrid models are investigated to develop an accurate and efficient churn prediction model. The three models are based on two phases; the clustering phase and the prediction phase. In the first phase, customer data is filtered. The second phase predicts the customer behavior. The first model investigates the k-means algorithm for data filtering, and Multilayer Perceptron Artificial Neural Networks (MLP-ANN) for prediction. The second model uses hierarchical clustering with MLP-ANN. The third one uses self organizing maps (SOM) with MLP-ANN. The three models are developed based on real data then the accuracy and churn rate values are calculated and compared. The comparison with the other models shows that the three hybrid models outperformed single common models.

Keywords

Data Mining, k-Means, Hierarchical Cluster, Self Organizing Maps, Multilayer Perceptron Artificial Neural Networks, Churn Prediction

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Xia, G.E. and Jin, W.D. (2008) Model of Customer Churn Prediction on Support Vector Machine. Systems Engineering—Theory & Practice, 28, 71-77.
[2]	Yabas, U., Cankaya, H. and Ince, T. (2012) Customer Churn Prediction for Telecom Services. Computer Software and Applications Conference (COMPSAC), IEEE 36th Annual, Izmir, 16-20 July 2012, 358-359. http://dx.doi.org/10.1109/COMPSAC.2012.54
[3]	Yeshwanth, V., Raj, V.V. and Saravanan, M. (2011) Evolutionary Churn Prediction in Mobile Networks Using Hybrid Learning. In: Murray, R.C. and McCarthy, P.M., Eds., Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference, AAAI Press, 471-476.
[4]	Kim, M.-J., Koh, S.-J. and Park, Y.-J. (2006) A Study on Retaining Existing Customers in the Korean High-Speed Internet Service Market. Technology Management for the Global Future, 2006. PICMET, 4, 1970-1976.
[5]	Kang, C. and Pei-Ji, S. (2008) Customer Churn Prediction Based on Svmrfe. ISBIM’08. IEEE International Seminar on Business and Information Management, Wuhan, 19 December 2008, 306-309.
[6]	Cao, J.T., Zhang, H. and Zheng, Q.S. (2010) Retaining Customers by Data Mining: A Telecomunication Carrier’s Case Study in China. 2010 International Conference on E-Business and E-Government (ICEE), Guangzhou, 7-9 May 2010, 3141-3144.
[7]	Sharma, A. and Panigrahi, D.P.K. (2011) A Neural Network Based Approach for Predicting Customer Churn in Cellular Network Services. International Journal of Computer Applications, 27, 26-31. http://dx.doi.org/10.5120/3344-4605
[8]	Kim, S., Shin, K.-S. and Park, K. (2005) An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case. In: Wang, L., Chen, K. and Ong, Y., Eds., Advances in Natural Computation, Vol. 3611 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, 636-647.
[9]	Rodan, A., Faris, H., Alsakran, J. and Al-Kadi, O. (2014) A Support Vector Machine Approach for Churn Prediction in Telecom Industry. International Journal on Information, 17, 3961-3970.
[10]	Adwan, O., Faris, H., Jaradat, K., Harfoushi, O. and Ghatasheh, N. (2014) Predicting Customer Churn in Telecom Industry Using Multilayer Preceptron Neural Networks: Modeling and Analysis. Life Science Journal, 11, 75-81.
[11]	Rodan, A., Fayyoumi, A., Faris, H., Alsakran, J. and Al-Kadi, O. (2014) Negative Correlation Learning for Customer Churn Prediction: A Comparison Study. The Scientific World Journal, 2015, 1-7.
[12]	Kasiran, Z., Ibrahim, Z. and Syahir Mohd Ribuan, M. (2012) Mobile Phone Customers Churn Prediction Using Elman and Jordan Recurrent Neural Network. 7th International Conference on Computing and Convergence Technology (ICCCT), December 2012, Seoul, 3-5 December 2012, 673-678.
[13]	Burez, J. and Van den Poel, D. (2009) Handling Class Imbalance in Customer Churn Prediction. Expert Systems with Applications, 36, 4626-4636. http://dx.doi.org/10.1016/j.eswa.2008.05.027
[14]	Faris, H. (2014) Neighborhood Cleaning Rules and Particle Swarm Optimization for Predicting Customer Churn Behavior in Telecom Industry. International Journal of Advanced Science and Technology, 68, 11-22. http://dx.doi.org/10.14257/ijast.2014.68.02
[15]	Tsai, C.-F. and Lu, Y.-H. (2009) Customer Churn Prediction by Hybrid Neural Networks. Expert Systems with Applications, 36, 12547-12553. http://dx.doi.org/10.1016/j.eswa.2009.05.032
[16]	Obiedat, R., Alkasassbeh, M., Faris, H. and Harfoushi, O. (2013) Customer Churn Prediction Using a Hybrid Genetic Programming Approach. Scientific Research and Essays, 8, 1289-1295.
[17]	Faris, H., Al-Shboul, B. and Ghatasheh, N. (2014) A Genetic Programming Based Framework for Churn Prediction in Telecommunication Industry. Lecture Notes of Computer Science, 8733, 253-362. http://dx.doi.org/10.1007/978-3-319-11289-3_36
[18]	Mitchell, T.M. (1997) Machine Learning. McGraw-Hill, Inc., New York.
[19]	MacQueen, J., et al. (1967) Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Oakland, 21 June-18 July 1965, 281-297.
[20]	Johnson, S. (1967) Hierarchical Clustering Schemes. Psychometrika, 32, 241-254. http://dx.doi.org/10.1007/BF02289588
[21]	Kohonen, T. (1982) Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 43, 59-69. http://dx.doi.org/10.1007/BF00337288
[22]	Dobbyn, C. (2007) Intelligent Machines. M366: Natural and Artificial Intelligence. Open University, UK.
[23]	Zhang, Y., Liang, R., Li, Y., Zheng, Y. and Berry, M. (2011) Behavior-Based Telecommunication Churn Prediction with Neural Network Approach. 2011 IEEE International Symposium on Computer Science and Society (ISCCS), Kota Kinabalu, 16-17 July 2011, 307-310.