A case study of applying data mining techniques in an outfitter's customer value analysis

Authors:
Shian-Chang Huang;En-Chi Chang;Hsin-Hung Wu
Affiliations:
Department of Business Administration, National Changhua University of Education, No. 2, Shida Road, Changhua City, Changhua 500, Taiwan;Manchester Business School, Booth Street West, Manchester, M15 6PB, UK;Department of Business Administration, National Changhua University of Education, No. 2, Shida Road, Changhua City, Changhua 500, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This study applies K-means method, fuzzy c-means clustering method and bagged clustering algorithm to the analysis of customer value for an outfitter in Taipei, Taiwan. These three techniques bear similar philosophy for data classification. Thus, it would be of interest to know which clustering technique performs best in a real world case of evaluating customer value. Using cluster quality assessment, this study concludes that bagged clustering algorithm outperforms the other two methods. To conclude the analyses, this study also suggests marketing strategies for each cluster based on the results generated by bagged clustering technique.