A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business

Authors:
Der-Chiang Li;Wen-Li Dai;Wan-Ting Tseng
Affiliations:
Department of Industrial and Information Management, National Cheng Kung University, 1st University Road, Tainan 70101, Taiwan;Department of Information Management, Tainan University of Technology, Taiwan;Department of Industrial and Information Management, National Cheng Kung University, 1st University Road, Tainan 70101, Taiwan and Nam Liong textile Enterprise Co., Ltd., Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In order to obtain comprehensive information about customers, this study aims to use a systematized analytic method to examine customers. This study uses LRFM customer relationship model, which consists of four dimensions: relation length (L), recent transaction time (R), buying frequency (F), and monetary (M), to carry out customer clusters. We proceed with this clustering analysis to classify customers in order to set discriminative marketing strategies. In addition, this study further employed a cross analysis over three predetermined dimensions: area, sales, and new/old characteristics to enhance the clustering analysis. The results obtained from the real textile business show that the customer groups formed using the four-factor (LRFM) clustering all has statistical significant differences, and with meaningful explanations in terms of marketing strategy. Thus, this study considers useful for discriminative customer relationship management.