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Understanding Shopping Behaviors With Category- and Brand-Level Market Basket Analysis
Abstract
In a digital transformation environment, most businesses shift towards e-business and encounter businesses and customer interaction on digital channels. Information Technology renders data access and processing more efficient, and use of customer data in decision making has become a focal interest area that attracts researchers. Customer data is a relevant subject for numerous studies in Data Mining. In this chapter, Association Rule Mining has been utilized to extract purchase behavior patterns with a multilevel approach. Basket data obtained from an online retailer was analyzed to discover purchase behaviors with a focus on category and brand attributes of products. Brands and categories purchased together frequently were discovered. Brand and category-wise association rules were also presented in the results. The analysis differs from the majority of prior analyses, by referring to the category and brand attributes in basket data. It could be noted that generalized rules obtained with this approach might prove useful in recommending new items of existing brands or categories.
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