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Publication Detail
From online browsing to offline purchases: Analyzing contextual information in the retail business
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Chan S, Capra L
  • Publication date:
    01/12/2012
  • Journal:
    CEUR Workshop Proceedings
  • Volume:
    889
  • Status:
    Published
  • Print ISSN:
    1613-0073
Abstract
Accurate recommender systems can enhance consumers' shop- ping experiences. In retail and many other business environments, extra contextual factors are usually available for building even more accurate recommender systems. The inuence of some factors is controversial in the industry. For instance, consumers' recent online exposure to products can decrease the chance of in-store purchase as consumers may choose to purchase products online. On the other hand, online exposure can be seen as an evidence of consumers' preference on products, which implies a higher chance of in-store purchase. The understanding of true inuence is important for product recommendation in-store in this case. The question is how to evaluate the relevance and the inuence of potential factors for prediction. Existing literature focuses on applying machine learning techniques to identify relevant contextual factors. While these methods are proven to be effective in some experiments, an alternative approach that can provide easy-to-interpret analysis on relevance and inuence is preferred in many situations. The paper introduces a computationally inexpensive approach to conduct preliminary relevance and inuence analysis for contextual information in retail business. Statistical tech- niques from medical research field are applied to analyze re- lationship between consumers' online exposure to retailer's e-commerce website, i.e., a contextual factor, and their of- ine in-store purchase decisions, i.e., the outcome to be predicted, based on a retail dataset provided by a large UK retail business with both online and offline presence. Unlike machine learning approaches, this analysis can be done even before a recommender system is built by using the proposed approach. This research further shows that the inuence of this contextual factor depends on extraneous attributes, such as consumers' ages and gender. This papers serves as a preliminary step to analyze relevant contextual factors for building context-aware recommender systems.
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