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A Genetic Programming Based Framework for Churn Prediction in Telecommunication Industry

  • Hossam Faris
  • Bashar Al-Shboul
  • Nazeeh Ghatasheh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8733)

Abstract

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.

Keywords

Churn prediction Genetic Programming Self Organizing Maps Telecommunication 

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References

  1. 1.
    Blattberg, R.C., Do, K.B., Scott, N.A.: Database Marketing: Analyzing and Managing Customers. In: International Series in Quantitative Marketing, vol. 18, pp. 607–633. Springer, New York (2008)Google Scholar
  2. 2.
    Owczarczuk, M.: Churn models for prepaid customers in the cellular telecommunication industry using large data marts. Expert Systems with Applications 37, 4710–4712 (2010)CrossRefGoogle Scholar
  3. 3.
    Kim, N., Lee, J., Jung, K.H., Kim, Y.S.: A new ensemble model for efficient churn prediction in mobile telecommunication. In: 2013 46th Hawaii International Conference on System Sciences, pp. 1023–1029 (2012)Google Scholar
  4. 4.
    Miguéis, V.L., Van den Poel, D., Camanho, A.S., Cunha, J.F.: Modeling partial customer churn: On the value of first product-category purchase sequences. Expert Syst. Appl. 39, 11250–11256 (2012)CrossRefGoogle Scholar
  5. 5.
    Miguéis, V.L., Van den Poel, D., Camanho, A.S., Cunha, J.F.: Predicting partial customer churn using markov for discrimination for modeling first purchase sequences. Advances in Data Analysis and Classification 6, 337–353 (2012)CrossRefzbMATHGoogle Scholar
  6. 6.
    Saha, S.K., Ghoshal, S.P., Kar, R., Mandal, D.: Cat swarm optimization algorithm for optimal linear phase fir filter design. ISA Transactions 52, 781–794 (2013)Google Scholar
  7. 7.
    Yazdani, D., Nasiri, B., Azizi, R., Sepas-Moghaddam, A., Meybodi, M.R.: Optimization in dynamic environments utilizing a novel method based on particle swarm optimization. Int’l Journal of Artificial Intelligence 11, 170–192 (2013)Google Scholar
  8. 8.
    Sheta, A., Faris, H., Alkasassbeh, M.: A genetic programming model for s&p 500 stock market prediction. International Journal of Control and Automation 6, 303–314 (2013)CrossRefGoogle Scholar
  9. 9.
    Huang, B., Kechadi, M.T., Buckley, B.: Customer churn prediction in telecommunications. Expert Syst. Appl. 39, 1414–1425 (2012)CrossRefGoogle Scholar
  10. 10.
    Li, G., Deng, X.: Customer churn prediction of china telecom based on cluster analysis and decision tree algorithm. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds.) AICI 2012. CCIS, vol. 315, pp. 319–327. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Adwan, O., Faris, H., Jaradat, K., Harfoushi, O., Ghatasheh, N.: Predicting customer churn in telecom industry using multilayer preceptron neural networks: Modeling and analysis. Life Science Journal 11 (2014)Google Scholar
  12. 12.
    Obiedat, R., Alkasassbeh, M., Faris, H., Harfoushi, O.: Customer churn prediction using a hybrid genetic programming approach. Scientific Research and Essays 8, 1289–1295 (2013)Google Scholar
  13. 13.
    Yu, X., Guo, S., Guo, J., Huang, X.: An extended support vector machine forecasting framework for customer churn in e-commerce. Expert Systems with Applications 38, 1425–1430 (2011)CrossRefGoogle Scholar
  14. 14.
    Kohonen, T.: Clustering, taxonomy, and topological maps of patterns. In: Proceedings of the Sixth International Conference on Pattern Recognition, Silver Spring, MD, pp. 114–128. IEEE Computer Society Press (1982)Google Scholar
  15. 15.
    Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78, 1464–1480 (1990)CrossRefGoogle Scholar
  16. 16.
    Bação, F., Lobo, V., Painho, M.: Self-organizing maps as substitutes for K-means clustering. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 476–483. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Gray, R.: Vector quantization. IEEE ASSP Magazine 1, 4–29 (1984)CrossRefGoogle Scholar
  18. 18.
    Kohonen, T.: Data management by self-organizing maps. In: Zurada, J.M., Yen, G.G., Wang, J. (eds.) Computational Intelligence: Research Frontiers. LNCS, vol. 5050, pp. 309–332. Springer, Heidelberg (2008)Google Scholar
  19. 19.
    Koza, J.: Evolving a computer program to generate random numbers using the genetic programming paradigm. In: Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, La Jolla (1991)Google Scholar
  20. 20.
    Koza, J.R.: Genetic Programming: On the programming of computers by means of natural selection, vol. 1. MIT press (1992)Google Scholar
  21. 21.
    Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40, 121–144 (2010)CrossRefGoogle Scholar
  22. 22.
    Kotanchek, M., Smits, G., Kordon, A.: Industrial strength genetic programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practice, pp. 239–256. Kluwer (2003)Google Scholar
  23. 23.
    Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic programming- Modern Concepts and Practical Applications. CRC Press (2009)Google Scholar
  24. 24.
    Miller, W., Sutton, R., Werbos, P.: Neural Networks for Control. MIT Press, Cambridge (1995)Google Scholar
  25. 25.
    Burez, J., Van den Poel, D.: Handling class imbalance in customer churn prediction. Expert Syst. Appl. 36, 4626–4636 (2009)CrossRefGoogle Scholar
  26. 26.
    Tsai, C.F., Lu, Y.H.: Customer churn prediction by hybrid neural networks. Expert Systems with Applications 36, 12547–12553 (2009)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hossam Faris
    • 1
  • Bashar Al-Shboul
    • 1
  • Nazeeh Ghatasheh
    • 1
  1. 1.The University of JordanAmmanJordan

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