Mining Customer Shopping Behavior

Abstract

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.