A Neural Network Application to Identify High-Value Customers for a Large Retail Store in Japan
Edward Ip (University of Southern California, USA), Joseph Johnson (University of Miami, USA), Katsutoshi Yada (Kansai University, Japan), Yukinobu Hamuro (Osaka Sangyo University, Japan), Naoki Katoh (Kyoto University, Japan) and Stephane Cheung (University of Southern California, USA)
Copyright: © 2002
The data mining activities studied in this chapter concern the early identification of potential high-value customers. Member stores can use this information to establish a close relationship with this select group of customers, thus reducing the chances of losing them. Traditionally, Japanese drugstore chains, unlike their American counterparts, have enjoyed close ties with their customers. For example, cash register clerks at Pharma stores may have substantial interactions with customers using online market research questionnaire forms. By closely monitoring the purchasing behavior of relatively new visitors to the store and applying data mining tools to pertinent data, the company can provide decision support to clerks and to the marketing department to aid in relationship building. For instance, sales campaign information, customized coupons, and free samples can be directly mailed to members of the targeted group or given to them at the checkout counter.