Skip to main content
SpringerLink
Log in

ADTreesLogit model for customer churn prediction

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In this paper, 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®, 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® on two important observation points.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Anderson Consulting (2000). Battling churn to increase shareholder value: wireless challenge for the future. Anderson Consulting research report.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Berry, M. J. A., & Linoff, G. S. (2000). Mastering data mining: the art and science of customer relationship management. New York: Wiley.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

  • Dowling, G. R., & Uncles, M. (1997). Do customer loyalty programs really work? Sloan Management Review, 38(4), 71–82.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Guo, J. (2007). Business-to-business electronic market place selection. Enterprise Information Systems, 1(4), 383–419.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Heskett, J. L., Sasser, W. E., & Schlesinger, L. A. (1997). The service profit chain. New York: Free Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • Hosmer, B. E. (1998). The loyalty effect: The hidden force behind growth, profits, and lasting value. Journal of Management Consulting, 10(2), 82–93.

    Google Scholar 

  • Huffmire, D. W. (2001). Improving customer satisfaction, loyalty, and profit: an integrated measurement and management system. Choice (Chicago), 38(5), 946–947.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Larivière, 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Quinlan, J. R. (1987). Simplifying decision trees. International Journal of Machine Learning Studies, 27(3), 221–234.

    Article  Google Scholar 

  • Ridley, D. B. (2005). Price differentiation and transparency in the global pharmaceutical marketplace. PharmacoEconomics, 23(7), 651–658. doi:10.2165/00019053-200523070-00002.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Shu, H., & Qi, J. (2004). Customer life cycle management in telecommunication industry. Beijing: Beijing University of Post and Telecommunication Press.

    Google Scholar 

  • Varian, H. (1989). Price discrimination and competition. In M. Armstrong & R. Porter (Eds.), Handbook of industrial organization (Vol. 1, pp. 597–654). Amsterdam: Elsevier.

    Google Scholar 

  • Warfield, J. N. (2007). System science serves enterprise integration: a tutorial. Enterprise Information Systems, 1(2), 235–254. doi:10.1080/17517570701241079.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Xu, L. (2007). Editorial. Enterprise Information Systems, 1(1), 1–2. doi:10.1080/17517570712331393320.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jiayin Qi & Yongpin Zhou

  2. School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China

    Li Zhang & Yanping Liu

  3. Department of Information Technology and Decision Science, Old Dominion University, Norfolk, VA, 23529, USA

    Ling Li

  4. College of International Business and Management, Shanghai University, Shanghai, 200072, China

    Yao Shen

  5. School of Management, University of Science and Technology of China, Hefei, 230026, China

    Liang Liang

  6. School of Business, University of Alberta, Edmonton, Alberta, Canada, T6G 2R6

    Huaizu Li

Authors
  1. Jiayin Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ling Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yongpin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yao Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Liang Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Huaizu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Qi, J., Zhang, L., Liu, Y. et al. ADTreesLogit model for customer churn prediction. Ann Oper Res 168, 247–265 (2009). https://doi.org/10.1007/s10479-008-0400-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-008-0400-8

Keywords

Search

Navigation