How to cite this paper
APA: Bahmani, B., Mohammadi, G., Mohammadi, M & Tavakkoli-Moghaddam, R. (2013). Customer churn prediction using a hybrid method and censored data. Management Science Letters, 3(5), 1345-1352.
Chicago/Turabian: Bahmani, B., Mohammadi, G., Mohammadi, M & Tavakkoli-Moghaddam, R. 2013. "Customer churn prediction using a hybrid method and censored data." Management Science Letters 3, no. 5 (2013): 1345-1352.
AMA: Bahmani, B., Mohammadi, G., Mohammadi, M & Tavakkoli-Moghaddam, R. Customer churn prediction using a hybrid method and censored data. Management Science Letters. 2013;3(5):1345-1352.
References
Baesens, B., Viaene, S., Van den Poel, D., Vanthienen, J., & Dedene, G. (2002). Bayesian neural network learning for repeat purchase modeling in direct marketing. European Journal of Operational Research, 138(1), 191–211.
Berry, M. J. A., & Linoff, G. S. (2003). Data mining techniques: For marketing, sales, and customer support. John Wiley & Sons.Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34(1), 313–327.
Gerpott, T. J., Rams, W., & Schindler, A. (2001). Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market. Telecommunications Policy, 25, 249–269.
Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecomm churn management. Expert Systems with Applications, 31(3), 515–524.
Hung, C., & Tsai, C., F. (2008). Segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Applications, 34(1), 780–787.
Judd, C. M., & Kenny, D. A. (1981). Process analysis estimating mediation in treatment evaluations. Evaluation review, 5(5), 602-619.
Keramati, A., Ardabili, S., M., S. (2012). Churn analysis for an Iranian mobile operator. Telecommunications Policy, 35(4), 344–356
Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications Policy, 28(9), 751–765.
Kim, H.-S., & Kwon, N. (2003). The advantage of network size in acquiring new subscribers. Information Economics and Policy, 15(1), 17–33.
Madden, G., Savage, S.J., & Coble-Neal, G. (1999). Subscribers churn in the Australian ISP market. Information Economics and Policy, 11(2), 195–207.
Ngai, E. W. T, Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592–2602.Pendharkar, P., C. (2009). Genetic algorithm based neural network approaches for predicting customer churn in cellular wireless network services. Expert Systems with Applications, 36, 6714–6720.
Seo, D., Ranganathan, C., & Babad, Y. (2008).Two-level model of customer retention in the US mobile telecommunication service market. Telecommunications Policy, 32(3–4), 182–196.
Tsai, Ch., & Lu, Y. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36, 12547–12553.
Van den Poel, D., & Larivie‘re, B. (2004). Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research, 157, 196–217.
Berry, M. J. A., & Linoff, G. S. (2003). Data mining techniques: For marketing, sales, and customer support. John Wiley & Sons.Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34(1), 313–327.
Gerpott, T. J., Rams, W., & Schindler, A. (2001). Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market. Telecommunications Policy, 25, 249–269.
Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecomm churn management. Expert Systems with Applications, 31(3), 515–524.
Hung, C., & Tsai, C., F. (2008). Segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Applications, 34(1), 780–787.
Judd, C. M., & Kenny, D. A. (1981). Process analysis estimating mediation in treatment evaluations. Evaluation review, 5(5), 602-619.
Keramati, A., Ardabili, S., M., S. (2012). Churn analysis for an Iranian mobile operator. Telecommunications Policy, 35(4), 344–356
Kim, H. S., & Yoon, C. H. (2004). Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommunications Policy, 28(9), 751–765.
Kim, H.-S., & Kwon, N. (2003). The advantage of network size in acquiring new subscribers. Information Economics and Policy, 15(1), 17–33.
Madden, G., Savage, S.J., & Coble-Neal, G. (1999). Subscribers churn in the Australian ISP market. Information Economics and Policy, 11(2), 195–207.
Ngai, E. W. T, Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592–2602.Pendharkar, P., C. (2009). Genetic algorithm based neural network approaches for predicting customer churn in cellular wireless network services. Expert Systems with Applications, 36, 6714–6720.
Seo, D., Ranganathan, C., & Babad, Y. (2008).Two-level model of customer retention in the US mobile telecommunication service market. Telecommunications Policy, 32(3–4), 182–196.
Tsai, Ch., & Lu, Y. (2009). Customer churn prediction by hybrid neural networks. Expert Systems with Applications, 36, 12547–12553.
Van den Poel, D., & Larivie‘re, B. (2004). Customer attrition analysis for financial services using proportional hazard models. European Journal of Operational Research, 157, 196–217.