Predicting shoppers' interest from social interactions using sociometric sensors

Taemie J. Kim, Maurice Chu, Oliver Brdiczka, James Begole
2009 Proceedings of the 27th international conference extended abstracts on Human factors in computing systems - CHI EA '09  
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
more » ... est 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%.
doi:10.1145/1520340.1520692 dblp:conf/chi/KimCBB09 fatcat:jubdxljd7rhhfimztd6zgc2tku