A Methodology of Conjoint Segmentation for Internet Shopping Malls Using Customer's Surfing Data

인터넷 쇼핑몰 방문자의 행위 분석을 이용한 컨조인트 시장세분화 방법론에 대한 연구

A lot 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 is essential for their continuous survival. However, only a few marketing researchers and practicioners focused on this issue, compared with academic and industry efforts devoted to traditional market segmentation. In this paper, we suggest a methodology of conjoint segmentation for electronic shopping malls. Traditional market segmentation methodologies based on customer's profile sometimes fail to utilize 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 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 4-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.