An Intelligent Customer Retention System

  • Bong-Horng Chu
  • Kai-Chung Hsiao
  • Cheng-Seen Ho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


This paper proposes an intelligent system for handling the customer retention task, which is getting important due to keen competition among companies in many modern industries. Taking wireless telecommunication industry as a target of research, our system first learns an optimized churn predictive model from a historical services database by the decision tree-based technique to support the prediction of defection probability of customers. We then construct a retention policy model which maps clusters of churn attributes to retention policies structured in a retention ontology. The retention policy model supports automatic proposing of suitable retention policies to retain a possible churner provided that he or she is a valuable subscriber. Our experiment shows the learned churn predictive model has around 85% of correctness in tenfold cross-validation. And a preliminary test on proposing suitable package plans shows the retention policy model works equally well as a commercial website. The fact that our system can automatically propose proper retention policies for possible churners according to their specific characteristics is new and important in customer retention study.


Association Rule Customer Relationship Management Association Rule Mining Learning Mode Package Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Oceansblue: Accurate Customer Churn Prediction with Information Inventor, available at:
  2. 2.
    Mozer, M.C., Wolniewicz, R., Grimes, D.B.: Predicting Subscriber’s Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry. IEEE Transactions on Neural Networks 11(3), 690–696 (2000)CrossRefGoogle Scholar
  3. 3.
    Wei, C., Chiu, I.: Turning Telecommunications Call Details to Churn Prediction: A Data Mining Approach. Expert Systems with Applications 23(2), 103–112 (2002)CrossRefGoogle Scholar
  4. 4.
    Au, W.H., Chan, K.C.C., Yao, X.: A Novel Evolutionary Data Mining Algorithm With Applications to Churn Prediction. IEEE Transactions on Evolutionary Computation 7(6), 532–545 (2003)CrossRefGoogle Scholar
  5. 5.
    Yan, L., Wolniewicz, R.H., Dodier, R.: Predicting Customer Behavior in Telecommunications. IEEE Intelligent Systems 19(2), 50–58 (2004)CrossRefGoogle Scholar
  6. 6.
    Uschold, M., Gruninger, M.: ONTOLOGIES: Principles, Methods and Applications. The Knowledge Engineering Review 11(2), 93–136 (1996)CrossRefGoogle Scholar
  7. 7.
    Fensel, D.: Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Gerpott, T.J., Rambs, W., Schindler, A.: Customer Retention, Loyalty, and Satisfaction in the German Mobile Cellular Telecommunications Market. Telecommunications Policy 25, 249–269 (2001)CrossRefGoogle Scholar
  9. 9.
    Kin, H.S., Yoon, C.H.: Determinants of Subscriber Churn and Customer Loyalty in the Korean Mobile Telephony Market. Telecommunications Policy 28(9-10), 751–765 (2004)CrossRefGoogle Scholar
  10. 10.
    Chunghwa Telecom: Best Package Plan Calculation, available at:
  11. 11.
    Stanford Medical Informatics: The Protégé Ontology Editor and Knowledge Acquisition System, available at:
  12. 12.
    Quinlan, J.R.: Induction of Decision Tree. Machine Learning 1, 81–106 (1983)Google Scholar
  13. 13.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  14. 14.
    Dehne, F., Eavis, T., Rau-Chaplin, A.: Computing Partial Data Cubes for Parallel Data Warehousing Applications. In: Cotronis, Y., Dongarra, J. (eds.) PVM/MPI 2001. LNCS, vol. 2131, p. 319. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th International Conference on Very Large Databases, pp. 487–499 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bong-Horng Chu
    • 1
    • 3
  • Kai-Chung Hsiao
    • 2
  • Cheng-Seen Ho
    • 2
    • 4
  1. 1.Department of Electronic EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  3. 3.Telecommunication Laboratories, Chunghwa Telecom Co., Ltd.TaipeiTaiwan
  4. 4.Department of Electronic EngineeringHwa Hsia Institute of TechnologyTaipeiTaiwan

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