Improved churn prediction in telecommunication industry by analyzing a large network

Customer retention in telecommunication companies is one of the most important issues in customer relationship management, and customer churn prediction is a major instrument in customer retention. Churn prediction aims at identifying potential churning customers. Traditional approaches for determining potential churning customers are based only on customer personal information without considering the relationship among customers. However, the subscribers of telecommunication companies are connected with other customers, and network properties among people may affect the churn. For this reason, we proposed a new procedure of the churn prediction by examining the communication patterns among subscribers and considering a propagation process in a network based on call detail records which transfers churning information from churners to non-churners. A fast and effective propagation process is possible through community detection and through setting the initial energy of churners (the amount of information transferred) differently in churn date or centrality. The proposed procedure was evaluated based on the performance of the prediction model trained with a social network feature and traditional personal features.

[1]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[2]  A. Khenchaf,et al.  An SVM based churn detector in prepaid mobile telephony , 2004, Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004..

[3]  David C. Yen,et al.  Applying data mining to telecom churn management , 2006, Expert Syst. Appl..

[4]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[6]  R. Guimerà,et al.  Modularity from fluctuations in random graphs and complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[8]  P. Bonacich.  Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

[9]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

[10]  Sergio Gómez,et al.  Size reduction of complex networks preserving modularity , 2007, ArXiv.

[11]  Rajeev Motwani,et al.  Pricing Strategies for Viral Marketing on Social Networks , 2009, WINE.

[12]  Sougata Mukherjea,et al.  Social ties and their relevance to churn in mobile telecom networks , 2008, EDBT '08.

[13]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Yossi Richter,et al.  Predicting Customer Churn in Mobile Networks through Analysis of Social Groups , 2010, SDM.

[15]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[16]  Ulrik Brandes,et al.  On Modularity - NP-Completeness and Beyond , 2006 .

[17]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[18]  YongSeog Kim,et al.  Toward a successful CRM: variable selection, sampling, and ensemble , 2006, Decis. Support Syst..

[19]  Eric Johnson,et al.  Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry , 2000, IEEE Trans. Neural Networks Learn. Syst..

[20]  Kiyana Zolfaghar,et al.  A syntactical approach for interpersonal trust prediction in social web applications: Combining contextual and structural data , 2012, Knowl. Based Syst..

[21]  Georg Lausen,et al.  Spreading activation models for trust propagation , 2004, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004.

[22]  Bart Baesens,et al.  Domain knowledge integration in data mining using decision tables: case studies in churn prediction , 2009, J. Oper. Res. Soc..

[23]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[24]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Dong Hong-bin,et al.  Intelligent Viral Marketing Algorithm over Online Social Network , 2011, 2011 Second International Conference on Networking and Distributed Computing.