A conjoint model for Internet shopping malls using customer's purchasing data

Lots of Internet shopping malls strive for obtaining a competitive advantage over others in an increasingly tighter electronic marketplace. To this end, understanding customer preference toward products (or services) and administering appropriate marketing strategy are essential for their continuous survival. However, only a few marketing researchers and practitioners focused on this issue in this changing business environments, compared with academic and industry efforts devoted to the traditional market segmentation. In this paper, we suggest a methodology of benefit segmentation for electronic shopping malls using conjoint analysis. Traditional market segmentation methodologies based on customer's profile sometimes fail to utilize the abundant information given while navigating around cyber shopping malls. In this methodology, we do not impose information overload to the customer for preference elicitation, but capture automatically generated surfing or buying data and analyze them to get useful market segmentation information. The methodology consists of four stages: (1) analyzing legacy homepages; (2) data preparation; (3) estimating and interpreting the result; and (4) developing marketing mix. Our methodology was to give useful guidelines for market segmentation to companies working in the electronic marketplace.