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CHI
2009
ACM

Predicting shoppers' interest from social interactions using sociometric sensors

12 years 2 months ago
Predicting shoppers' interest from social interactions using sociometric sensors
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%. Keywords Shopping, Interest, Behavior modeling, Group dynamics, Interaction patterns, Sociometric Sensors ACM Classification Keywords H5.3. Group and Organization Interfaces
James Begole, Maurice Chu, Oliver Brdiczka, Taemie
Added 24 Nov 2009
Updated 24 Nov 2009
Type Conference
Year 2009
Where CHI
Authors James Begole, Maurice Chu, Oliver Brdiczka, Taemie Jung Kim
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