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Study on the Application of Decision Trees for Personalization based on e-CRM  

양정희 (인덕대학 산업시스템경영과)
한서정 (호서대학교 디지털비즈니스학부)
Publication Information
Journal of the Korea Safety Management & Science / v.5, no.3, 2003 , pp. 107-119 More about this Journal
Abstract
Expectation and interest about e-CRM are rising for more efficient customer management in on-line including electronic commerce. The decision-making tree can be used usefully as the data mining technology for e-CRM. In this paper, the representative decision making techniques, CART, C4.5, CHAID analyzed the differences in personalization point of view with actuality customer data through an experiment. With these analysis data, it is proposed a new decision-making tree system that has big advantage in personalization techniques. Through new system, it can get following advantage. First, it can form superior model more qualitatively in personalization by adding individual's weight value. Second it can supply information personalized more to customer. Third, it can have high position about customer's loyalty than other site of similar types of business. Fourth, it can reduce expense that cost marketing and decision-making. Fifth, it becomes possible that know that customer through smooth communication with customer who use personalized service wants and make from goods or service's quality to more worth thing.
Keywords
e-CRM; Decision -making Tree; personalization;
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  • Reference
1 Kim, H. and Loh, W.-Y. Classification trees with unbiased multiway splits, Technical Report 1012, Department of Statistics, University of Wisconsin-Madison, 1999
2 Quinlan, J.R., C4.5: Programs for Machine Learning, Morgan Kaufmann, San, Mateo, CA, 1993
3 A. Berson, S. Smith and K. Thearling, Building data mining applications for CRM, McGraw-Hill, 1999
4 J. Ross Quinlan, 'Simplifying Decision Trees', Int. J. Man-Machine Studies, 27, pages 221-234, 1987
5 L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, Wadsworth, Belmont, 1984
6 Loh, W.-Y. and Shih, Y.-S. Split selection methods for classification trees, Statistica Sinica, 7, 815-840, 1997
7 Song, M., Yoon, Y., 'A comparative study on variable selection methods in data mining software packages', 한일통계학회, 2000
8 Salford Systems, CART, Salford Systems, San Diago, CA, 1997