Developing Consumer-Friendly Pervasive Retail Systems
IEEE Pervasive Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
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Pervasive and Mobile Computing
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Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Marketing research has longed for better ways to measure consumer behavior. In this paper, we explore using sociometric data to study social behaviors of group shoppers. We hypothesize that the interaction patterns among shoppers will convey their interest level, predicting probability of purchase. To verify our hypotheses, we observed co-habiting couples shopping for furniture. We have verified that there are sensible differences in customer behavior depending on their interest level. When couples are interested in an item they observe the item for a longer duration of time and have a more balanced speaking style. A real-time prediction model was constructed using a decision tree with a prediction accuracy reaching 79.8% and a sensitivity of 63%.