Korean Management Science Review (경영과학)

The Korean Operations Research and Management Science Society (한국경영과학회)
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A Defection Prevention Procedure using SOM for On-line Game Providers

SOM을 이용한 온라인 게임 제공업체의 고객이탈방지 방법론

  • 김재경 (경희대학교 경영대학) ;
  • 채경희 (경희대학교 경영대학 일반대학원 e비즈니스) ;
  • 송희석 (한남대학교 경상대학 경영정보학과)
  • Published : 2004.11.01
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Abstract

The retention of customer is an increasingly pressing issue in today's competitive environment. The proposes of this paper is a personalized defection detection and the procedure of prevention based on economic analysis of customer defection possibility, and behaviour state transition cost. This procedure is based on the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behaviour before their eventual withdrawal. In this procedure, the SOM(Self-Organizing Map) is used to determine the possible states of customer behaviour from past behaviour data, and to prevent the defection of potential defectors, the proposed procedure recommends the desirable behaviour state for the next period based on the analysis of transition cost. and likelihood of defection. The case study has been conducted for a Korean on-line game provider to evaluate of this procedure.

Keywords

References

  1. Beiman, L., J. Friedman, R. Olshen and C. Stone, Classification and Regression Trees, Wadsworth International Group, C.A, 1984
  2. Berson, A, S. Smith and K Thearling, Building data mining applications for CRM. McGraw-Hill, 2000
  3. Draenos, S., Gamers Log On, Upside Magazine, January, 2001
  4. Fayyad, U. G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996
  5. Quinlan, J, C4.5: Programs for Machine Learning, Morgan Kaufmann, C.A., 1993
  6. Alhoniemi, E., J Hollmen, O. Simula and J Vesanto, 'Process monitoring and modeling using the Self-Organizing Map,' Integrated CAE, Vol.6, No.1(1999), pp.3-14
  7. Almualiim, H. and T. Dietterich, 'Learning Boolean concepts in the presence of many irrelevant features,' Artificial Intelligence, Vol.69(1994), pp.279-306
  8. Chakraborty, G., and B. Chakraborty, 'A novel normalization technique for unsupervised learning in ANN,' IEEE transactions on neural networks, Vol.11, No.1(2000), pp.253-257 https://doi.org/10.1109/72.822529
  9. Chung, Y, W. Pottenger and B. Schatz, 'Automatic Subject Indexing Using an Associative Neural Network,' In Proceedings of the ACM Conference on Digital Libraries, DL-99(1998), pp.59-68
  10. Cooley, R, B. Mobasher and J. Srivastava, 'Data preparation for mining world wide web browsing patterns,' Knowledge andinformation systems, Vol, No.l(1999) pp.5-32
  11. Datta, P., B. Masand, D. Mani and B. Li, 'Automated cellular modeling and prediction on a large scale,' Artificial Intelligence Review, Vol.14(2000), pp.485-502
  12. Hall, M, and L. Smith, 'Practical feature subset selection for machine learning,' Proceedings of the 21st Australian Computer Science Conference, 1998, pp.181-191
  13. Kass, G. 'An Exploratory Technique for Investigating Large Quantities of Categorical Data,' Applied Statistics, Vol.29(1980), pp.119-127
  14. Kasslin, M, J Kangas, and O. Sirnula, 'Precess state rmnitoring using Self-organizing maps,' Artificial Neural Networks, Vol.2(1992), pp.1531-1534
  15. Kim, Y, R Song and S. Kim, 'A dynarnic procedure for defection detection and prevention based on SOM and a Markov chain,' International Conference of Korea Intelligent Information Systems Societ, 2003, pp.141-148
  16. Kira, K, and L.A Rendell, 'The feature selection problem: Traditional methods and a new algorithm' Proceedings AAAI-92(1992), pp.129-134
  17. Kohavi, R and G. John, 'Wrappers for feature subset selection,' Artificial Intelligence, Vol.97(1997), pp.273-324
  18. Kohonen, T., 'The Self-Organizing Map,' Proceedings of the IEEE, Vol.78, No.9(1990), pp.1464-1480 https://doi.org/10.1109/5.58325
  19. Kohonen, T. 'Self-Organizing and Associative Memory,' Berlin, Springer-Verlag, 1995
  20. Kohonen, T., E. OJa, O. Sirnula, A Visa and J Kangas, 'Engineering applications of the Self-Organizing Map,' Proceedings of the IEEE, Vol.84, No.10(1996), pp.1358-1384 https://doi.org/10.1109/5.537105
  21. Loh, W. and Y Shih, 'Split selection methods for classification trees,' Statistica Sinica, Vol.7, No.8(1997), pp.815-840
  22. Ng,K and H Liu, 'Customer retention via data mining,' Artificial Intelligence Review, Vol.14, No.6(2000), pp.569-590 https://doi.org/10.1023/A:1006676015154
  23. Raghavan, N., RM Bell, and M Schonlau, 'Defection Detection: Using Online Activity Profiles to Predict ISP Custorrer Vulnerability: In Proceedings of the sixth International Conference of Knowledge Discovery and Data Mining, 2000, pp.506-515
  24. Song, H, J Kim, Y. Cho and S. Kim, 'A personalized defection detection and prevention procedure based on the self-organizing map and association rule mining : Applied to online gamesit,' Artificial Intelligence Review, Vol.21, No.2(2004), pp.161-18 https://doi.org/10.1023/B:AIRE.0000021067.66616.b0
  25. Trubik, E., and M. Smith, ''Developing a model of customer defection in the Australian banking industry,' Managerial Auditing Journal, Vol.15, No.5(2000), pp.199-208 https://doi.org/10.1108/02686900010339300
  26. Tryba, V. and K. Goser, 'Self-organizing feature maps for process control in chemistry,' Proceedings of international conference on artificial neural networks (ICANN 91), 1991, pp.847-852
  27. Vesanto, J, 'SOM-based data visualization methods,' Intelligent data analysis, Vol.3(1999), pp.111-126
  28. Yeo, A, K Smith, R Willis and M Brooks, 'Modelling the effect of premium changes on motor insurance customer retention rates using neural networks,' Lecture notes in computer science, Springer-Verlag, 2001, pp.390-399