The Journal of Information Systems (한국정보시스템학회지:정보시스템연구)

Korea Association of Information Systems (한국정보시스템학회)
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사례기반추론을 이용한 다이렉트 마케팅의 고객반응예측모형의 통합

  • Published : 2009.09.01
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Abstract

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

Keywords

References

  1. 김상용, 송지연, 이기순, "CRM 고객데이터 분석을 통한 이탈고객 연구," 한국마케팅저널, 제7권, 제1호, 2005, pp. 21-42.
  2. 노태협, 유명환, 한인구, "러프집합이론과 사례 기반추론을 결합한 기업신용평가 모형," 정보시스템연구, 제14권, 제1호, 2005, pp. 41-65.
  3. 민재형, 이영찬, "Support Vector Machine을 이용한 부도예측모형의 개발: 격자탐색을 이용한 커널 함수의 최적 모수 값 선정과 기존 부도예측모형과의 성과 비교," 한국경영과학회지, 제30권, 제1호, 2005, pp. 55-74.
  4. 박기남, "사례기반추론을 이용한 정보시스템 가치평가 모형개발에 관한 연구," 정보시스템연구, 제15권, 제2호, 2006, pp. 95-123.
  5. 박정민, 김경재, 한인구, "Support Vector Machine을 이용한 기업부도예측," 경영정보학연구, 제15권, 제2호, 2005, pp. 51-63.
  6. 송수섭, 이의훈, "인공신경망을 이용한 소비자 선택 예측에 관한 연구," 한국경영과학회지, 제26권, 제4호, 2001, pp. 55-70.
  7. 신성훈, 정수연, 박철, "이메일 마케팅 성과에 관한 연구: 신용카드 고객을 대상으로한 캠페인을 중심으로," Information Systems Review, 제11권, 제1호, 2009, pp. 49-67.
  8. 신택수, 홍태호, "인공신경망과 로짓모형을 통합한 부실확률맵기반 신용등급화에 관한 연구," 회계저널, 제13권, 제3호, 2004, pp. 1-26.
  9. 안현철, 김경재, 한인구, "효과적인 고객관계관리를 위한 사례기반추론 동시 최적화 모형," 한국지능정보시스템학회논문지, 제11권 제2호, 2005a, pp. 175-195.
  10. 안현철, 김경재, 한인구, "Support Vector Machine을 이용한 고객구매예측모형," 한국지능정보시스템학회논문지, 제11권, 제3호, 2005b, pp. 69-81.
  11. 이건창, 정남호, 신경식, "신용카드 시장에서 데이터 마이닝을 이용한 이탈고객분석," 한국지능정보시스템학회논문지, 제8권, 제2호, 2002, pp. 13-35.
  12. 이극노, 이홍철, "이동통신고객 분류를 위한 의사결정나무(C4.5)와 신경망 결합 알고리즘 연구," 한국지능정보시스템학회지, 제9권, 제1호, 2003, pp. 139-155.
  13. 이훈영, 양주환, 류치환, "고객의 이탈가능성과 LTV를 이용한 고객등급화 모형개발에 관한 연구," 한국지능정보시스템학회논문지, 제12권, 제4호, 2006, pp. 109-126.
  14. 홍태호, 김진완, "데이터 마이닝 비대칭 오류비용을 이용한 지능형 침입탐지시스템 개발," 정보시스템연구, 제15권, 제4호, 2006, pp. 211-224.
  15. Adomavicious, G., and Tuzhilin, A., "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, June, 2005, pp. 734-749. https://doi.org/10.1109/TKDE.2005.99
  16. Agrawal, D., and Schorling, C., "Market share forecasting: An Empirical Comparison of Artificial Neural Networks and Multinomial Logit model," Journal of Retailing, Vol. 72, No. 4, 1996, pp. 383-407. https://doi.org/10.1016/S0022-4359(96)90020-2
  17. Ahn, B.A., Cho, S.S., and Kim, C.Y., "The integrated methodology of rough set theory and artificial neural network for business failure prediction," Expert Systems with Applications, Vol. 19, 2000, pp. 65-74.
  18. Ahn, H., and Kim, K.-j., "Using genetic algorithms to optimize nearest neighbors for data mining," Annals of Operations Research, Vol. 163, 2008, pp. 5-18. https://doi.org/10.1007/s10479-008-0325-2
  19. Baesens, B., Viaene, S., Van den Poel, D., Vanthienen, J., and Dedene, G., "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Vol. 138, No. 1, 2002, pp. 191-211. https://doi.org/10.1016/S0377-2217(01)00129-1
  20. Bradley, P., "Case-based reasoning: Business applications," Communications of the ACM, Vol. 37, No. 3, 1994, pp. 40-43.
  21. Carvalho, D.R., and Feitas, A.A., "Hybrid Decision Tree/ Genetic Algorithm Method for Data Mining," Information Science, Vol. 163, 2004, pp. 13-35. https://doi.org/10.1016/j.ins.2003.03.013
  22. Chiang, W.K., Zhang, E., and Zhou, L., "Predicting and explaining patronage behavior toward web and traditional stores using neural networks: A comparative analysis with logistic regression," Decision Support Systems, Vol. 41, 2006, pp. 514-531. https://doi.org/10.1016/j.dss.2004.08.016
  23. Chiu, C., "A case-based customer classification approach for direct marketing," Expert Systems with Applications, Vol. 22, 2002, pp. 163-168. https://doi.org/10.1016/S0957-4174(01)00052-5
  24. Coenen, F., Swinnen, G., Vanhoof, K., and Wets, G., "The improvement of response modeling: combining rule-induction and case-based reasoning," Expert Systems with Applications, Vol. 18, 2000, pp. 307-313. https://doi.org/10.1016/S0957-4174(00)00012-9
  25. Conversano, C., Siciliano, R., and Mola, F., "Generalized Additive Multi-mixture Model for Data Mining," Computational Statistics & Data Analysis, Vol. 38, No. 4, 2002, pp. 487-500. https://doi.org/10.1016/S0167-9473(01)00074-3
  26. Cranor, L.F., and LaMacchia, B.A., "Spam!," Communications of the ACM, Vol. 41, No. 8, 1998, pp. 74-83.
  27. Dasgupta, C.G., Dispensa, G.S., and Ghose, S., "Comparing the predictive performance of a neural network model with some traditional market response models," International Journal of Forecasting, Vol. 10, 1994, pp. 235-244. https://doi.org/10.1016/0169-2070(94)90004-3
  28. Debar, H., Becker, M., and Siboni, D., "A Neural Network Component for an Intrusion Detection Systems," IEEE Computer Society Symposium Research in Security and Privacy, 1992, pp. 240-250.
  29. Desarbo, W.S., and Ramaswamy, V., "Customer response based iterative segmentation procedures for response modeling in direct marketing," Journal of Direct Marketing, Vol. 8, No. 3, 1994, pp. 7-20.
  30. Endler, D., "Intrusion Detection Applying Machine Learning to Solars Audit Data," Proceedings of Computer Security Applications Conference, 1998, pp. 268-279.
  31. Fish, K.E., Barnes, J.H., and Aiken, M.W., "Artificial neural networks - A new methodology for industrial market segmentation," Industrial Marketing Management, Vol. 24, 1995, pp. 431-438. https://doi.org/10.1016/0019-8501(95)00033-7
  32. Geng, M., "A Comparison of Logistic Regression to Random Forests for Exploring Differences in Risk Factos Associated with Stage at Diagnosis between Black and White Colon Cancer Patients," the degree of M.S., Univ. of Pittsburgh, 2006.
  33. Goldberg, D., Nichols, D., Oki, B., and Terry, D., "Using Collaborative Filtering to Weave an Information Tapestry," Communications of the ACM, Vol. 35, No. 12, 1992, pp. 61-70.
  34. Ha, K., Cho, S., and Maclachlan, D., "Response models based on bagging neural networks," Journal of Interactive Marketing, Vol. 19, No. 1, 2005, pp. 17-30. https://doi.org/10.1002/dir.20028
  35. Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., and Scholkopf, B., " Support Vector Machines", IEEE Intelligent Systems, Vol. 13, No. 4, 1988, pp. 18-28.
  36. Kim, D., Lee, H.-j.,, and Cho, S., "Response modeling with support vector regression," Expert Systems with Applications, Vol. 34, 2008, pp. 1102-1108. https://doi.org/10.1016/j.eswa.2006.12.019
  37. Kim, H., and Shin K.-s., "A Hybrid Approach Using Case-based Reasoning and Fuzzy Logic for Corporate Bond Rating," Korea Intelligent Information Systems Society, Vol. 10, No. 2, 2004, pp. 91-109.
  38. Kolodner, J.L., Case-based Reasoning, Morgan Kaufmann: Los Altos, CA, 1993
  39. Konstan, J.A., Miller, B.N., Maltz, D, Herlocker, J.L., Gordon, L.R., and Riedl, J., "GroupLense: Applying Collaborative Filtering to Usenet News," Communications of the ACM, Vol. 40, No. 3, 1997, pp. 77-87. https://doi.org/10.1145/245108.245126
  40. Kumar, P.R., and Ravi, V., "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational research, Vol. 180, 2007, pp. 1-28. https://doi.org/10.1016/j.ejor.2006.08.043
  41. Lee, K., Booth, D., and Alam, P., "A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms," Expert Systems with Applications, Vol. 29, 2005, pp. 1-16. https://doi.org/10.1016/j.eswa.2005.01.004
  42. Lee. H.-j., and Cho, S., "Focusing on non-respondents: Response modeling with novelty detectors," Expert Systems with Applications, Vol. 33, 2007, pp. 522-530. https://doi.org/10.1016/j.eswa.2006.05.016
  43. Li, R., and Wang, Z., "Mining classification rules using rough sets and neural networks," European Journal of Operational Research, Vol. 157, 2004, pp. 439-448. https://doi.org/10.1016/S0377-2217(03)00422-3
  44. Lin, F.Y., and McClean, S., "A Data Mining Approach to the Prediction of Corporate Failure," Knowledge-Based Systems, Vol. 14, No. 3/4, 2001, pp. 189-195. https://doi.org/10.1016/S0950-7051(01)00096-X
  45. Malthouse, E.C., "Assessing the performance of direct marketing scoring models," Journal of Interactive Marketing, Vol. 15, No. 1, 2001, pp. 49-62. https://doi.org/10.1002/1520-6653(200124)15:1<49::AID-DIR1003>3.0.CO;2-F
  46. Malthouse, E.C., "Performance-based variable selection for scoring models," Journal of Interactive Marketing, Vol. 16, No. 4, 2002, pp. 37-50. https://doi.org/10.1002/dir.10043
  47. Ohlson, J., "Financial ratios and probabilistic prediction of bankruptcy," Journal of Accounting Research, Vol. 18, 1980, pp. 109-131. https://doi.org/10.2307/2490395
  48. Paliwal, M., and Kumar, U.A., "Neural networks and statistical techniques: A review of applications," Expert Systems with Applications, Vol. 36, 2009, pp. 2-17. https://doi.org/10.1016/j.eswa.2007.10.005
  49. Prinzie, A., and Van den Poel, D., "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Expert Systems with Applications, Vol. 29, No. 3, 2005, pp. 630-640. https://doi.org/10.1016/j.eswa.2005.04.022
  50. Reinarts, W., Frafft, M., and Hoyer, W.D., "The Customer Relationship Management Process: Its Measurement and Impact on Performance," Journal of Marketing Research, 41, 2004, 293-305. https://doi.org/10.1509/jmkr.41.3.293.35991
  51. Resnick, P., and Varian, H.R., "Recommender Systems," Communications of the ACM, Vol. 40, No. 3, 1997, pp. 56-58.
  52. Rogers, M., "Customer Strategy: Observations from the Trenches," Journal of Marketing, 69, 2005, 262-263. https://doi.org/10.1509/jmkg.2005.69.4.262
  53. Rumelhart, D.E., and McClelland, J.I., Parallel Distributing Processing: Exploration in the Microstructure of Cognition, Vol. 1, Cambridge, MA: MIT Press, 1986.
  54. Shin, H.-S., and Cho, S., "Response modeling with support vector machine," Expert Systems with Applications, Vol. 30, 2006, pp. 746-760. https://doi.org/10.1016/j.eswa.2005.07.037
  55. Shin, K.-s., and Han, I., "Case-based reasoning supported by genetic algorithms for corporate bond rating," Expert systems with applications, Vol. 16, 1999, pp. 85-95. https://doi.org/10.1016/S0957-4174(98)00063-3
  56. Shin, K.-s., Lee, T.S., and Kim, H.-j., "An application of support vector machines in bankruptcy prediction model," Expert systems with applications, Vol. 28, No. 1, 2005, pp. 127-135. https://doi.org/10.1016/j.eswa.2004.08.009
  57. Shmueli, G., Patel, N.R., and Bruce, P.C., Data Mining for Business Intelligence, Wiley, 2007, pp. 88-89.
  58. Suh, E.H., Noh, K.C., and Suh, C.K., "Customer list segmentation using the combined response model," Expert Systems with Applications, Vol. 17, 1999, pp. 89-97. https://doi.org/10.1016/S0957-4174(99)00026-3
  59. Tam, K.Y., "Neural network models and the prediction of bank bankruptcy," Omega, Vol. 19, No. 5, 1991, pp. 429-445. https://doi.org/10.1016/0305-0483(91)90060-7
  60. Tay, F.E.H., and Cao, L.J., "Application of support vector machines in financial time series forecasting," Omega, Vol. 29, No. 4, 2001, pp. 309-317. https://doi.org/10.1016/S0305-0483(01)00026-3
  61. Thieme, R.J., Song, M., and Calantone, R.J., "Artificial neural network decision support systems for new product development project selection," Journal of Marketing Research, Vol. 37, No. 4, 2000, pp. 499-507. https://doi.org/10.1509/jmkr.37.4.499.18790
  62. Van den Poel, D., "Predicting mail-order repeat buying: Which variables matter?" Tijdschrift voor Economic en Management, Vol. 48, No. 3, 2003, pp. 371-403.
  63. Vapnik, V. The Nature of Statistical Learning Theory, Springer, 1995.
  64. Venkatesan, R., Kumar, V., and Bohling, T., "Optimal Customer Relationship Management Using Bayesian Decision Theory: An Application for Customer Selection," American Marketing Association, XLIV, 2007, pp. 579-594.
  65. Versace, M., Bhatt, R., Hinds, O., and Shiffer, M., "Predicting the Exchange Traded Fund DIA with a Combination of Genetic Algorithm and Neural Networks," Expert systems with applications, Vol. 27, No. 3, 2004, pp. 417-425. https://doi.org/10.1016/j.eswa.2004.05.018
  66. Viaene, S., Baesens, B., Van Gestel, T., Suykens, J.A.K., Van del Poel, D., Vanthienen, J., De Moor, B., and Dedene, G., "Knowledge discovery in a direct marketing case using least squares support vector machine classifiers," International Journal of Intelligent Systems, Vol. 9, 2001, pp. 1023-1036.
  67. Wu, C.H., Tzeng, G.H., Goo, Y.J., and Fang, W.C., "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert systems with Applications, Vol. 32, No 2, 2007, pp. 397-408. https://doi.org/10.1016/j.eswa.2005.12.008
  68. Yu, E., and Cho, S., "Constructing response model using ensemble based on feature subset selection," Expert Systems with Applications, Vol. 30, 2006, pp. 352-360. https://doi.org/10.1016/j.eswa.2005.07.026
  69. Zahavi, J., and Levin, N., "Applying Neural Computing to Target Marketing," Journal of Direct Marketing, Vol. 11, No. 1, Winter, 1997, pp. 5-22. https://doi.org/10.1002/(SICI)1522-7138(199724)11:1<5::AID-DIR2>3.0.CO;2-S
  70. Zang, G., Hu, M.Y., Patuwo, B.E., and Indro, D.C., "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Vol. 116, 1999, pp. 16-32. https://doi.org/10.1016/S0377-2217(98)00051-4
  71. Chang, C.-C., and Lin, C.-J.,: LIBSVM 2.83 - A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/-cjlin/libsvm/
  72. The Direct Marketing Educational Foundation (DMEF): The Direct Marketing Association. http://www.directworks.org