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

• Published in: Expert systems with applications 2011-06, Vol.38 (6), p.7186-7191
• Main Authors: Li, Der-Chiang, Dai, Wen-Li, Tseng, Wan-Ting
• Format: Article
• Language: English
• Subjects: Business; Clustering; Clustering analysis; Construction; Customer lifetime value; Customer relationship management; Data mining; Expert systems; LRFM model; Management; Marketing; Mathematical models; Strategy; Textiles
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2010.12.041

► The four-factor (LRFM) clustering all has statistical significant differences. ► We provide a company further understanding customers for making segmented marketing strategies. ► Taiwan factories with domestic sales have more long-term customers with greater company loyalty. 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.