ADtreesLogit model for customer churn prediction. (English) Zbl 1179.90037

Summary: We propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet\(\circledR \), a model which won the Gold Prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament). In fact, ADTreesLogit has better predictive accuracy than TreeNet\(\circledR \) on two important observation points.


90B06 Transportation, logistics and supply chain management
91B42 Consumer behavior, demand theory
90B90 Case-oriented studies in operations research


Full Text: DOI


[1] Anderson Consulting (2000). Battling churn to increase shareholder value: wireless challenge for the future. Anderson Consulting research report.
[2] Ang, L., & Buttle, F. (2006). Customer retention management processes: a quantitative study. European Journal of Marketing, 40(1/2), 83–89. doi: 10.1108/03090560610637329 .
[3] Au, W., Chan, K. C. C., & Yao, X. (2003). A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation, 7(6), 532–545. doi: 10.1109/TEVC.2003.819264 . · Zbl 05451873
[4] Beheshti, H., Hultman, M., Jung, M., Opoku, R., & Salehi-Sangari, E. (2007). Electronic supply chain management application by Swedish SMEs. Enterprise Information Systems, 1(2), 255–268. doi: 10.1080/17517570701273221 .
[5] Berry, M. J. A., & Linoff, G. S. (2000). Mastering data mining: the art and science of customer relationship management. New York: Wiley.
[6] Bostan, V., & Li, L. (2003). A decision model for reducing active power losses during electric power dispatching. Computers & Operations Research, 30, 833–849. doi: 10.1016/S0305-0548(02)00039-4 . · Zbl 1026.90516
[7] Chen, Y., & Li, L. (2006). Deriving information from CRM for knowledge management-A note on a commercial bank. Systems Research and Behavioral Science, 23(2), 141–146. doi: 10.1002/sres.756 .
[8] Chiang, D., Wang, Y., Lee, S., & Lin, C. (2003). Goal-oriented sequential pattern for network banking churn analysis. Expert Systems with Applications, 25(3), 293–302. doi: 10.1016/S0957-4174(03)00073-3 .
[9] Coussement, K., & Poel, D. V. D. (2008, in press). Churning prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications, 34(1), 313–327. (Available online http://ideas.repec.org/s/rug/rugwps.html , 13 October 2006).
[10] Dowling, G. R., & Uncles, M. (1997). Do customer loyalty programs really work? Sloan Management Review, 38(4), 71–82.
[11] Duan, L., Xu, L., Guo, F., Lee, J., & Yan, B. (2007). A local-density based spatial clustering algorithm with noise. Information Systems, 32(7), 978–986. doi: 10.1016/j.is.2006.10.006 . · Zbl 05184335
[12] Farquhar, J. D. (2004). Customer retention in retail financial services: an employee perspective. International Journal of Bank Marketing, 22(2), 86–99. doi: 10.1108/02652320410521700 .
[13] Feng, S., Li, L., & Lin, C. (2003). Using MLP approach to design a production scheduling system. Computers & Operations Research, 30, 821–832. doi: 10.1016/S0305-0548(02)00044-8 . · Zbl 1026.90046
[14] Flaxer, D., Cao, R. Z., Tian, C., Ding, W., & Lee, J. (2007). Variable pricing business solutions in a decomposed business environment. Enterprise Information Systems, 1(2), 177–195. doi: 10.1080/17517570701254965 .
[15] Freund, Y., & Mason, L. (1999). The alternating decision tree learning algorithm. In Proceeding of the sixteenth international conference on machine learning, Bled, Slovenia, pp. 124–133.
[16] Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. doi: 10.1006/jcss.1997.1504 . · Zbl 0880.68103
[17] Guo, J. (2007). Business-to-business electronic market place selection. Enterprise Information Systems, 1(4), 383–419.
[18] Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management, state-of-the-art and future trends. Computers & Operations Research, 34(10), 2902–2917. doi: 10.1016/j.cor.2005.11.007 . · Zbl 1185.90119
[19] Heskett, J. L., Sasser, W. E., & Schlesinger, L. A. (1997). The service profit chain. New York: Free Press.
[20] Holmes, G., Pfahringer, B., Kirkby, R., Frank, E., & Hall, M. (2002). Multiclass alternating decision trees. In I. T. Elomaa, H. Mannila, H. Toivonen (Eds.), Lecture Notes in Computer Science : Vol. 2430. Proceedings of 13th European conference on machine learning (pp. 161–172). Berlin: Springer. · Zbl 1014.68754
[21] Hosmer, B. E. (1998). The loyalty effect: The hidden force behind growth, profits, and lasting value. Journal of Management Consulting, 10(2), 82–93.
[22] Huffmire, D. W. (2001). Improving customer satisfaction, loyalty, and profit: an integrated measurement and management system. Choice (Chicago), 38(5), 946–947.
[23] Hung, S., Yen, D. C., & Wang, H. (2006). Applying data mining to telecom churn management. Expert Systems with Applications, 31(3), 515–524. doi: 10.1016/j.eswa.2005.09.080 .
[24] Jia, H., & Yan, H. (2005). Empirical analysis on the forming model of customer loyalty: the case study of mobile communication service. In 2005 international conference on services systems and services management. Proceedings of ICSSSM ’05. Vol. 1, pp. 133–137.
[25] Kamakura, W. A., Wedel, M., Rosa, F., & Mazzon, J. A. (2003). Cross-selling through database marketing: a mixed data factor analyzer for data augmentation and prediction. International Journal of Research in Marketing, 20(1), 45–65. doi: 10.1016/S0167-8116(02)00121-0 .
[26] Larivi√®re, B., & Poel, D. V. D. (2004). Investigating the role of product features in preventing customer churn by using survival analysis and choice modeling: the case of financial services. Expert Systems with Applications, 27(2), 277–285. doi: 10.1016/j.eswa.2004.02.002 .
[27] Li, H., & Xu, L. (2001). Feature space theory-a mathematical foundation for data mining. Knowledge-Based Systems, 14(5–6), 253–257. doi: 10.1016/S0950-7051(01)00103-4 .
[28] Li, H., Xu, L., Wang, J., & Mo, Z. (2003). Feature space theory in data mining: transformations between extensions and intensions in knowledge representation. Expert Systems: International Journal of Knowledge Engineering and Neural Networks, 20(2), 60–71. doi: 10.1111/1468-0394.00226 .
[29] Li, L. (1999). Proposing an architectural framework of hybrid knowledge-based system for production rescheduling. Expert Systems: International Journal of Knowledge Engineering and Neural Networks, 16(4), 273–279. doi: 10.1111/1468-0394.00119 .
[30] Li, L., Warfield, J., Guo, S., Guo, W., & Qi, J. (2007). Advances in intelligent information processing. Information Systems, 32(7), 941–943. doi: 10.1016/j.is.2006.10.001 .
[31] Mihelis, G., Grigoroudis, E., Siskos, Y., Politis, Y., & Malandrakis, Y. (2001). Customer satisfaction measurement in the private bank sector. European Journal of Operational Research, 130(2), 347–360. doi: 10.1016/S0377-2217(00)00036-9 . · Zbl 1068.90569
[32] Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., & Kaushansky, H. (2000). Predicting customer dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Transactions on Neural Networks, 11(3), 690–696. doi: 10.1109/72.846740 .
[33] Quinlan, J. R. (1987). Simplifying decision trees. International Journal of Machine Learning Studies, 27(3), 221–234.
[34] Ridley, D. B. (2005). Price differentiation and transparency in the global pharmaceutical marketplace. PharmacoEconomics, 23(7), 651–658. doi: 10.2165/00019053-200523070-00002 .
[35] Shaw, M. J., Subramaniam, C., Tan, G. W., & Welge, M. (2001). Knowledge management and data mining for marketing. Decision Support Systems, 31(1), 127–137. doi: 10.1016/S0167-9236(00)00123-8 .
[36] Shu, H., & Qi, J. (2004). Customer life cycle management in telecommunication industry. Beijing: Beijing University of Post and Telecommunication Press.
[37] Varian, H. (1989). Price discrimination and competition. In M. Armstrong & R. Porter (Eds.), Handbook of industrial organization (Vol. 1, pp. 597–654). Amsterdam: Elsevier.
[38] Warfield, J. N. (2007). System science serves enterprise integration: a tutorial. Enterprise Information Systems, 1(2), 235–254. doi: 10.1080/17517570701241079 .
[39] Wei, C., & Chiu, I. (2002). Turning telecommunication call details to churn prediction: a data mining approach. Expert Systems with Applications, 3(2), 103–112. doi: 10.1016/S0957-4174(02)00030-1 . · Zbl 01937409
[40] Xu, L. (2006). Advances in intelligent information processing. Expert Systems: International Journal of Knowledge Engineering and Neural Networks, 23(5), 249–250. doi: 10.1111/j.1468-0394.2006.00405.x .
[41] Xu, L., Li, Z., Li, S., & Tang, F. (2007). A decision support system for product design in concurrent engineering. Decision Support Systems, 42(4), 2029–2042. doi: 10.1016/j.dss.2004.11.007 .
[42] Xu, L. (2007). Editorial. Enterprise Information Systems, 1(1), 1–2. doi: 10.1080/17517570712331393320 .
[43] Zang, C., & Fan, Y. (2007). Complex event processing in enterprise information system based on FRID. Enterprise Information Systems, 1(1), 3–23. doi: 10.1080/17517570601092127 .
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.