Mining customer knowledge for exploring online group buying behavior

Authors:
Shu-hsien Liao;Pei-hui Chu;Yin-ju Chen;Chia-Chen Chang
Affiliations:
Department of Management Sciences, Tamkang University, No. 151, Yingjuan Rd., Danshuei Dist., New Taipei City 251, Taiwan, ROC;Department of Information Management, National Taipei College of Business, No. 321, Sec. 1, Jinan Rd., Zhongzheng District, Taipei City 10051, Taiwan, ROC;Department of Management Sciences, Tamkang University, No. 151, Yingjuan Rd., Danshuei Dist., New Taipei City 251, Taiwan, ROC;Department of Management Sciences, Tamkang University, No. 151, Yingjuan Rd., Danshuei Dist., New Taipei City 251, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Online group buying is an effective marketing method. By using online group buying, customers get unbelievable discounts on premium products and services. This not only meets customer demand, but also helps sellers to find new ways to sell products sales and open up new business models, all parties benefit in these transactions. During these bleak economic times, group buying has become extremely popular. Therefore, this study proposes a data mining approach for exploring online group buying behavior in Taiwan. Thus, this study uses the Apriori algorithm as an association rules approach, and clustering analysis for data mining, which is implemented for mining customer knowledge among online group buying customers in Taiwan. The results of knowledge extraction from data mining are illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to online group buying firms for future development.