Mining Customer Shopping Behavior: A Method Encoding Customer Purchase Decision Attitude

Mining Customer Shopping Behavior: A Method Encoding Customer Purchase Decision Attitude

Hsiao-ping Yeh (Department of Marketing and Distribution Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan) and Tsung-Sheng Chang (Department of Information Management, Da-Yeh University, Changhua, Taiwan)
Copyright: © 2018 |Pages: 12
DOI: 10.4018/IJISSS.2018010102


Mining customer shopping data is able for business managers to understand and predict customer behavior. However, most practices are focusing on the purchasing goods, i.e. basket analysis. This article collects customer shopping data by observation to systematically discover customer shopping pattern incorporating with customer's purchasing decision time. With Apriori algorithm and the proposed customer purchasing decision pattern examining principle, customer purchase behaviors of with decision attitudes are revealed. This article gets insights at decomposing support and confidence values of an association rule. With the proposed encoding method, decision attitudes on goods in the association rule can be interpreted.
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Service industry is a business that most companies need to directly interact with customers. With the usages of information communication technology (ICT), companies enable to be close to their customers and understand their customers better than ever to promote customer satisfaction and loyalty. ICT adoptions become evitable deeds on service management in the 21st century. As Shahsavarani and Ji (2014) pointed out, ICT has enhanced a great deal of service improvement for service industry such as leveraging the surging river-like word-of-mouth flushed from social communities, or providing innovative and fun services with QR codes, mobile APPs, and augmented reality applications. These innovative applications indeed create high added-values for companies and for customers.

Big data mining nowadays has been widely adopted in businesses. The massive data collected from business transactions have made entrepreneurs realize to use the data for supporting their business decision making. The method mining customer shopping data incorporated with customer purchasing products with price and quantities is generally called market basket analysis. For example, with the usage of customers’ purchase data and the product information, Brijs, Goethals, Swinnen, Vanhoof, and Wets (2000) and Nafari and Shahrabik (2010) mined out the potential customer purchasing behavior for marketing purposes and Liao, Wen, Hsian, Li, and Hsu (2014) evaluated product’s future market value. In fact, applying data mining and suitable management method could help mass customization which is concerned with meeting the needs of an individualized customer market (Tien, 2006; Tirunillai & Tellis, 2014). The famous result is the diaper-beer rule in baskets. Benefits for retailers are better shelf management, goods supply, and market promotion. However, gaining more profits is the most important goal for retailers. Oliverira-Castro, Foxall, and Schrezenmaier (2005) investigated 80 retail shoppers in 16 weeks and found that they are most sensitive to prices of the goods. Discounts can always arouse customer’s purchase desires on one hand, yet decrease retailer’s profits on the other. Competition on prices is not the only and final answer. There must are other factors affecting customer’s decision on purchases.

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