Abstract
In this study, we modeled customer purchasing behavior in a retail store using a Bayesian network-based probabilistic modeling tool to apply towards improving merchandise display and package design. The following four aspects of purchasing behavior were examined: purchasing gestures, glances made during the selection of merchandise, customer psychology concerning purchasing, and body information, including age, height, gender, eyesight, and dominant hand. To monitor purchasing behavior, three college students fitted with an eye tracker were asked to select the most appealing product among twenty-five snack food products that were displayed on five-tier shelving. Factors affecting customers’ merchandise selection in the experimental retail store were then computed using a Bayesian network, and a purchasing behavior model was constructed from the results. Using the constructed model, a method of effective merchandise display based on the characteristics of retail purchasing behavior was proposed.
Published Version
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