Abstract
Customer churn in considered to be a core issue in telecommunication customer relationship management (CRM). Accurate prediction of churn time or customer tenure is important for developing appropriate retention strategies. In this paper, we discuss a method based on ordinal regression to predict churn time or tenure of mobile telecommunication customers. Customer tenure is treated as an ordinal outcome variable and ordinal regression is used for tenure modeling. We compare ordinal regression with the state-of-the-art methods for tenure prediction - survival analysis. We notice from our results that ordinal regression could be an alternative technique for survival analysis for churn time prediction of mobile customers. To the best knowledge of authors, the use of ordinal regression as a potential technique for modeling customer tenure has been attempted for the first time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Neslin, S.A., Gupta, S., Kamakura, W., Lu, J., Mason, C.H.: Defection detection: Improving predictive accuracy of customer churn models. Working paper series, Teradata center for customer relationship management at Duke university (2004)
Allison, P.D.: Survival analysis using the SAS system: A practical guide, SAS Institute Inc, Cary, NC (1995)
Lu, J.: Predicting customer churn in telecommunications industry - An application of survival analysis modeling using SAS. SAS User Group international online proc., Paper No. 114-27 (2002)
SAS Institute Inc.: SAS/STAT®Users Guide, Version 6. SAS Institute Inc., 1,2 (1989)
Bolton, R.N.: A dynamic model for the duration of the customer’s relationship with a continuous service provider: The role of satisfaction. Marketing Science 17, 45–65 (1998)
Chu, W., Keerthi, S.S.: Support vector ordinal regression. Neural Computation 19(3), 145–152 (2007)
Frank, E., Hall, M.: A simple approach to ordinal classification. In: Proc. of the European Conf. on Machine Learning, pp. 145–156 (2001)
Witten, I.H., Frank, E.: Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufman, San Francisco (2005)
Cox, D.R.: Regression models and life tables. J. of the Royal Stat. Soc., Series B 34, 187–220 (1972)
Gopal, R.K., Bhattacharyya, C., Meher, S.K.: Customer churn time prediction in mobile telecommnunication industry using ordinal regression. ARG-TR-Y7-001, Technical report, Satyam Computer Services Limited (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gopal, R.K., Meher, S.K. (2008). Customer Churn Time Prediction in Mobile Telecommunication Industry Using Ordinal Regression. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_88
Download citation
DOI: https://doi.org/10.1007/978-3-540-68125-0_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
eBook Packages: Computer ScienceComputer Science (R0)