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In the bricks-and-mortar environment, stores employ sales people that have learned to distinguish between shoppers based on their in-store behavior. Some shoppers appear to be very focused in looking for a specific product. In those cases, sales people may step in and help the shopper find what they are looking for. In other cases, the shopper is merely “window shopping.” The experienced sales person can identify these shoppers and either ignore them and let them continue window shopping, or intercede and try and stimulate a purchase in the appropriate manner. However, in the virtual shopping environment, there is no sales person to perform that role. Therefore, this article theoretically develops and empirically tests a typology of store visits in which visits vary according to the shoppers’ underlying objectives. By using page-to-page clickstream data from a given online store, visits are categorized as a buying, browsing, searching, or knowledge-building visit based on observed in-store navigational patterns, including the general content of the pages viewed. Each type of visit varies in terms of purchasing likelihood. The shoppers, in each case, are also driven by different motivations and therefore would respond differentially to various marketing messages. The ability to categorize visits in such a manner allows the e-commerce marketer to identify likely buyers and design more effective, customized promotional message.
Wendy W. Moe (Wed,) studied this question.