Abstract
Customers with prepaid lines possess higher attrition risk compared to postpaid customers, since prepaid customers do not sign long-term obligatory contracts and may churn anytime. For this reason, mobile operators have to offer engaging benefits to keep prepaid subscribers with the company. Since all such offers incur additional cost, mobile operators face an optimization problem while selecting the most suitable offers for customers at risk. In this study, an offer management framework targeting prepaid customers of a telecommunication company is developed. Proposed framework chooses the most suitable offer for each customer through a mathematical model, which utilizes customer lifetime value and churn risk. Lifetime values are estimated using logistic regression and Pareto/NBD models, and several variants of these models are used to predict churn risks using a large number of customer specific features.
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References
Kumar, V., Rajan, B.: Profitable customer management: measuring and maximizing customer lifetime value. Manag. Acc. Q. 10(3), 1 (2009). Spring 2009
Can, Z., Albey, E.: Churn prediction for mobile prepaid subscribers. In: Proceedings of the 6th International Conference on Data Science, Technology and Applications Volume 1, DATA 2017, ISBN 978-989-758-255-4, pp. 67–74 (2017)
Pfeifer, P., Haskins, M., Conroy, R.: Customer lifetime value, customer profitability, and the treatment of acquisition spending. J. Manag. Issues 17(Spring), 11–25 (2005)
Hwang, H.: A dynamic model for valuing customers: a case study. Adv. Sci. Technol. Lett. 120, 56–61 (2015)
Fader, P., Bruce, H., Ka, L.: RFM and CLV: using iso-value curves for customer base analysis. J. Mark. Res. 42, 415–430 (2005)
Wei, J., Lin, S., Wu, H.: A review of the application of RFM model. Afr. J. Bus. Manag. 4(19), 4199–4206 (2010). December Special Review
Zabkowski, T.: RFM approach for telecom insolvency modeling. Kybernetes 45(5), 815–827 (2016)
Huang, B., Kechadi, M., Buckley, B.: Customer churn prediction in telecommunications. Expert Syst. Appl. 39(1), 1414–1425 (2012)
Segarra-Moliner, J., Moliner-Tena, M.: Customer equity and CLV in Spanish telecommunication services. J. Bus. Res. 69(10), 4694–4705 (2016)
Bahnsen, A., Aouada, D., Ottersten, B.: A novel cost-sensitive framework for customer churn predictive modeling. Decis. Anal. 2, 5 (2015)
Khajvand, M., Zolfaghar, K., Ashoori, S., Alizadeh, S.: Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study. Procedia Comput. Sci. 3, 57–63 (2011)
Platzer, M.: Stochastic models of noncontractual consumer relationships. Master thesis at the Vienna University of Economics and Business Adminstration under the Supervision of Dr. Thomas Reutterer (2008)
Homburg, C., Koschate, N., Hoyer, W.D.: Do satisfied customers really pay more? A study of the relationship between customer satisfaction and willingness to pay. J. Mark. 2(69), 84–86 (2005)
Ma, S., Liu, J.: The MCMC approach for solving the Pareto/NBD model and possible extensions. In: Third International Conference on Natural Computation, ICNC 2007, vol. 2. IEEE (2007)
Abe, M.: Counting your customers one by one: a hierarchical Bayes extension to the Pareto/NBD model. Mark. Sci. 28(3), 541–553 (2009)
Platzer, M., Reutterer, T.: Ticking away the moments: timing regularity helps to better predict customer activity. Mark. Sci. 35(5), 779–799 (2016)
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Şahin, A., Can, Z., Albey, E. (2018). A Mathematical Model for Customer Lifetime Value Based Offer Management. In: Filipe, J., Bernardino, J., Quix, C. (eds) Data Management Technologies and Applications. DATA 2017. Communications in Computer and Information Science, vol 814. Springer, Cham. https://doi.org/10.1007/978-3-319-94809-6_2
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DOI: https://doi.org/10.1007/978-3-319-94809-6_2
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