Mining Customer Knowledge for a Recommendation System in Convenience Stores

Shu-Hsien Liao, Chih-Hao Wen, Pei-Yuan Hsian, Chien-Wen Li and Che-Wei Hsu
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Shu-Hsien Liao: Department of Management Sciences, Business and Management College, Tamkang University, Danshuei District, New Taipei City, Taiwan, R.O.C.
Chih-Hao Wen: Department of Logistics Management, National Defense University, Taipei, Taiwan, R.O.C.
Pei-Yuan Hsian: Department of Management Sciences, Business and Management College, Tamkang University, Danshuei District, New Taipei City, Taiwan, R.O.C.
Chien-Wen Li: Department of Management Sciences, Business and Management College, Tamkang University, Danshuei District, New Taipei City, Taiwan, R.O.C.
Che-Wei Hsu: Department of Management Sciences, Business and Management College, Tamkang University, Danshuei District, New Taipei City, Taiwan, R.O.C.

International Journal of Data Warehousing and Mining (IJDWM), 2014, vol. 10, issue 2, 55-86

Abstract: Taiwan's rapid economic growth with increasing personal income leads increasing numbers of young unmarried people to eat out, and shopping at convenience stores for food is indispensable to the lives of these people. Thus, it is an essential issue for convenience store owners to know how to accurately market appropriate products and to choose effective endorsers for brands or products in order to attract target consumers. Data mining is a business intelligence analysis approach with great potential to help businesses focus on the most important business information contained in a database. Therefore, this study uses the Apriori algorithm as an association rules approach, and clustering analysis for data mining. The authors divide consumers into three groups by their consumer profiles and then find each group's product preference mixes, product endorsers, and product/brand line extensions for new product development. These are developed as a recommendation system for 7-11 convenience stores in Taiwan.

Date: 2014
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