Applying Survival Analysis to Telecom Churn Data
American Journal of Theoretical and Applied Statistics
Volume 8, Issue 6, November 2019, Pages: 261-275
Received: Feb. 12, 2018; Accepted: Mar. 5, 2018; Published: Dec. 2, 2019
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Melik Masarifoglu, Department of Statistics, Yildiz Technical University, İstanbul, Turkey
Ali Hakan Buyuklu, Department of Statistics, Yildiz Technical University, İstanbul, Turkey
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In competitive telecommunication environment, it is imperative to maintain an effective customer retention strategy even while mobile service operators attracting new customers. Not only acquiring new customers costly process, but successful customer retention helps build brand loyalty and good business reputation. Motivated by real mobile service operator data set, we designed and proposed a solution to employ survival analysis technique that estimates customers’ survivals and hazards. We aim to examine the impact of: campaign, tariff, tenure, age, auto-payment on survival times and hazards. After hazard ratios and survival experiences determined for each predictor, results enable mobile service operator to target the right customers to incentivize so that they can stay with their current operator. Proactive actions triggered by the results of the survival model is key to customer retention.
Customer Retention, Telecom Churn Prediction, Survival Analysis
To cite this article
Melik Masarifoglu, Ali Hakan Buyuklu, Applying Survival Analysis to Telecom Churn Data, American Journal of Theoretical and Applied Statistics. Vol. 8, No. 6, 2019, pp. 261-275. doi: 10.11648/j.ajtas.20190806.18
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Owczarczuk, M., “Churn models for prepaid customers in the cellular telecommunication industry using large datamarts,” Expert Systems with Applications 37, 4710-4712, 2010.
Jahanzep, S., and Jabeen S., “Churn managment in the Telecom industry of Pakistan: Acomparative study of Ufone and Telenor,” The Journal of Database Marketing Customer Strategy Management 14 (2), 120-129, 2007.
Hung, S. Y., Yen, D. C., and Wang H. Y., “Applying data mining to telecom churn management,” Expert Systems with Applications 31, 515-524, 2006.
Keramati, A., and Ardabili S. M. S., “Churn analysis for an Iranian mobile operator,” Telecommunications Policy 35, 344-356, 2011.
Safko, L., “The Fusion Marketing Bible: Fuse Traditional Media, Social Media, & Digital Media to Maximize Marketing,” McGraw-Hill Education - Europe 35, 344-356, 2011.
Keramati, A., Marandi-Jafari, R., Aliannejadi, M., Ahmadian, M., Moza_ari, M. and Abbasi U., “Improving churn prediction in telecommunication industry using data mining techniques,” Applied Soft Computing 24, 994-1012, 2011.
Ling, R. and Yen D. C., “Customer relationship management: An analysis framework and implementation strategies,” Journal of Computer Information Systems 41, 82-97, 2001.
Ngai, E. W. T, Xiu, L. and Chau, D. C. K, “Application of data mining techniques in customer relationship management: A literature review and classification,” Expert Systems with Applications 36 (1), 2592-2602, 2009.
DeMaris, A, “Regression with Social data: Modeling Continious and Limited Response Variables,” Wiley Series in Probabilty and Statistics, 381-385, 2005.
Langova K., “Survival Analysis for Clinical Studies,” Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 152 (2), 303-307, 2008.
Lawless, J. F., Statistical Models and Methods for Lifetime Data, John Wiley 36, 122-132, 2003.
London, D., “Survival Models and Their Estimation,” Actex Publications 2, 50-75, 1998.
Jerenz, A., “Revenue Management and Survival Analysis in the Automobile Industry,” Gabler Edition Wissenschaft, 70-80, 2008.
Kaplan, L. E., “Nonparametric estimation from incomplete observations,” Journal of American Statistical Association 53, 457-81, 1958.
Cox, D. R., “Regression Model and Life-Tables,” Journal of the Royal Statistical Society 34, 187-220, 1972.
Lee, M., “Business Bankruptcy Prediction Based on Survival Analysis Approach,” International Journal of Computer Science Information Technology 6 (2), 2014.
Pereira, J., “Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal,” International Business Research 7 (6), 2014.
Kumar, K., and Gepp A., “Predicting Financial Distress: A Comparison of Survival Analysis and Decision Tree Techniques,” Procedia Computer Science 54 (2), 396-404, 2015.
Iwasaki, I., “Global _financial crisis, corporate governance, and firm survival: The Russian experience,” Journal of Comparative Economics 42 (1), 178-211, 2014.
Bu, Z., Liu, L., Cao, J. and Wu Z., “Survival analysis method for stock market prediction, Behavioral, Economic and Socio-cultural Computing,” 10, 1109, 2015.
Leung, M. K., Rigby, D. and Young, T., “Entry of foreign banks in the People's Republic of China: a survival analysis,” Journal of Applied Economics 10 21-31, 2010.
Evrensel, A. Y., “Banking crisis and _financial structure: A survival-time analysis,” International Review of Economics & Finance 17 (4), 589-602, 2008.
Tsujitani, M. and Baesens, B., “Survival Analysis for Personal Loan Data Using Generalized Additive Models,” Behaviormetrika 39 (1), 1109, 9-23, 2012.
Baesens, B., Gestel, TV., Stepanova, M., Poel, D. V and Vanthienen J., “Neural network survival analysis for personal loan data,” Journal of the Operational Research Society 56 (9), 1089-1098, 2005.
Singh, G., “Predicting Customer Churn in the Telecommunications Industry-An Application of Survival Analysis Modeling Using SA,” SAS Conference Papers-Sugi 27 114 (27), 2002.
Rud, O. P., “Data Mining Cookbook,” John Wiley & Sons 56 (9), 12-25, 2001.
Barrios, E. and Lansanganb J. R. G, “Forecasting Customer Lifetime Value: A Statistical Approach,” Philippine Management Review 19, 23-34, 2012.
Ranganathan, P. and Pramesh C. S., “Censoring in survival analysis: Potential for bias,” Perspectives in Clinical Research 3 (1), 40, 2012.
Shihcorresponding, W. J., “Problems in dealing with missing data and informative censoring in clinical trials,” Curr Control Trials Cardiovasc Med 35 (1), 21-31, 2011.
Hosmer, JR. DW and Lemeshow S., “Applied Survival Analysis: Regression Modeling of Time to Event Data,” New York: John Wiley & Sons, 1999.
Kleinbaum, D. G. and Lemeshow Klein M., “Survival Analysis: A self-learning text”, Springer, 2005.
Kalbeisch, J. D. and Prentice R. L., “The Statistical Analysis Failure Time Data,” A John Wiley & Sons Publication, 2002.
Lee, E. T. and Wang J. W., “Statistical Methods for Survival Data Analysis,” A John Wiley & Sons Publication, 2003.
Hogg, R. V., McKean, W. J. and Craig A. T., “Introduction to Mathematical Statistics,” Pearson Education, 2005.
Muldowney, P., Ostaszewski, K. and Wojdowski W., “The Darth Vader rule,” Tatra Mountains Mathematical Publications 52 (1), 2012.
Kutner, H. M., Nachtsheim, J. C., Neter, J. and Li W., “Applied Linear Statistical Models,” Macgraw-Hill 52 (1), 2005.
Kuhn, M. and Johnson K., “Applied Predictive Modeling,” Springer, 2013.
Abadi, A., Yavari, P, Dehghani-Arani, M., Alavi-Majd, H., Ghasemi, E., Amanpour, F. and Bajdik C., “Cox Models Survival Analysis Based on Breast Cancer Treatments,” Iranian Journal of Cancer Prevention 7 (3), 124-129, 2014.
Lane, W. R., Looney S. W. and Wansley J., “An application of the cox proportional hazards model to bank failure,” Journal of Banking & Finance 10 (4), 511-531, 1986.
Therneau, T. M. and Grambsch P. M., Modeling Survival Data: Extending the Cox Model Presented at the 2006 Midwest SAS Users Group (MWSUG), Dearborn, Michigan, October, 22-24, 2000.
SAS Institute Inc (2008). SAS/STAT Software, Version 9.2. Cary. URL
Bellera, A. C., MacGrogan, G., Debled, M., Lara, C. T., Brouste, V. and Mathoulin-Pelissier S., “Variables with time-varying effects and the Cox model: Some statistical concepts illustrated with a prognostic factor study in breast cancer, BMC Medical Research Methodology 10 (20), 2010.
Fisher, L. D. and Lin D. Y., “Time-dependent covariates in the Cox proportional-hazards regression model,” Annual Review of Public Health, 20 (145), 57, 1999.
Powell, T. M. MS and Bagnell M. E, “Your Survival Guide to Using Time Dependent Covariates,” SAS Global Forum, 168, 2012.
Therneau TM (2014). survival: A Package for Survival Analysis in S. R package version 2.37-7, URL
Thomas, L., and Reyes, E. M. Tutorial: Survival Estimation for Cox Regression Model with Time-Varying Coefficients Using SAS and R, Journal of the American Statistical Association ( 61 (1), 2014.
Borucka, J., “Methods for Handling Tied Events: In the Cox Proportional Hazard Model,” studia oeconomica posnaniensia 2 (2), 263, 2014.
Breslov, N., “Covariance Analysis of Censored Data,” Biometrics 30, 89: 99, 1974.
Hertz-Picciotto, I. & Rockhill, B., “Validity and Efficiency of Approximation Methods for Tied Survival Times in Cox Regression,” Biometrics 53, 1151-1156, 1997.
R Development Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URLc, URL, 2011.
SAS Institute Inc., SAS/GRAPH 9.2 Reference, Second Edition. Cary, NC: SAS Institute Inc., SAS Institute Inc., 2010.
Allison, P. D., “Fixed Effects Regression Methods for Longitudinal Data Using SAS,” SAS Institute Inc., 127-137, 2005.
Efron, B., “The efficiency of Cox's Likelihood Function of Censored Data,” Journal of the American Statistical Association 72, 557-565, 1977.
Gonzalez, D., Pina, M. and Torres, P., “Estimation of Parameters in Cox's Proportional Hazard Model: Comparisons between Evolutionary Algorithms and the Newton-Raphson Approach Mexican International Conference on Artificial Intelligence-Springer Link, 513-523, 2008.
Li, H., Han, D., Hou, Y., Chen, H. and Chen Z., “Statistical Inference Methods for Two Crossing Survival Curves: A Comparison of Methods, PLOS One 10 (1), PMCID: PMC4304842, 2015.
Fox, J and Weisberg S. Cox, “Proportional-Hazards Regression for Survival Data in R”, An Appendix to An R Companion to Applied Regression, Second Edition, 2011.
SAS Documantation SAS/STAT (R) 9.22 User's Guide/Procedures/The PHREG Procedure, SAS, 2010.
Therneau, T., Crowson, C. and Atkinson E., Using Time Dependent Covariates and Time Dependent Coe_cients in the Cox Model, 2017.
Sharma, A. and Panigrahi P. K. A, “Neural Network based Approach for Predicting Customer Churn in Cellular Network Services,” International Journal of Computer Applications (0975−8887) 27 (11), 2011.
Lu, J, “Modeling Customer Lifetime Value Using Survival Analysis: An Application in the Telecommunications Industry SAS” User Groups International Proceedings Seattle Washington, 120 (28), 2003.
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