A method for generating plans for retail store improvements using text mining and conjoint analysis

Authors:
Takumi Kaneko;Yuichiro Nakamura;Michiko Anse;Tsutomu Tabe;Yumiko Taguchi
Affiliations:
Graduate School of Science and Engineering, Aoyama Gakuin University, Sagamihara, Kanagawa, Japan;Graduate School of Science and Engineering, Aoyama Gakuin University, Sagamihara, Kanagawa, Japan;Graduate School of Science and Engineering, Aoyama Gakuin University, Sagamihara, Kanagawa, Japan;Graduate School of Science and Engineering, Aoyama Gakuin University, Sagamihara, Kanagawa, Japan;Department of Business Administration and Communication, Shohoku College, Atsugi, Kanagawa, Japan
Venue:
Proceedings of the 2007 conference on Human interface: Part II
Year:
2007

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Abstract

Sales at retail stores in Japan have been declining for various reasons. One important factor has been a steady diversification in the lifestyles and needs of customers. If a retail store is to sustain itself and continue developing, it must search for the latent demands of customers and adapt itself to accommodate those demands. A support system for store improvement capable of viewing specific improvement plans will prove useful as a tool for improving stores on a sustainable basis. In this research we develop a method for generating effective store-improvement plans to accommodate actual customer demands. The method employs both Text Mining and Conjoint Analysis techniques. We also demonstrate a sequence and a test run of a prototype.