Identifying the customer profiles for 3C-product retailers: a data mining approach

Abstract

Because of a speedy pace of technology innovation, the competition of the information and communication industries in Taiwan has grown vigorously. At the same time, the demand of consumers has become more sophisticated than ever. In order to outperform competitors and fulfill consumers' sophisticated requests, retailers of 3C (computer, communication, and consumer electronics) products, on one hand, have to improve greater efficiency and effectiveness of their up-stream supply chains. On the other hand, the retailers have to establish stronger relationships with their customers. This paper intends to identify customer profiles for 3C product retailers and then develop customized marketing strategies for meeting the demands of different customers. In this paper, we adopted a data mining approach. First, the three indicators (recency, frequency and monetary) of the RFM model were used to cluster customers into homogeneous purchasing-behavior-pattern groups through K-means analysis. Then, the characteristics of the individual customer groups were further analyzed in terms of a CART (classification and regression tree) method. According to the research findings, the two indicators (frequency and monetary) were used for profiling four customer groups. Finally, this paper formulated four marketing strategies customized to target the individual customer groups, respectively.