PROJECT TITLE :
Predicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads
Billions of on-line video ads are viewed every month. We tend to gift a large-scale analysis of facial responses to video content measured over the Web and their relationship to selling effectiveness. We tend to collected over 12,00zero facial responses from 1,223 folks to one hundred seventy ads from a vary of markets and products categories. The facial responses were automatically coded frame-by-frame. Collection and coding of those 3.seven million frames wouldn't have been possible with ancient research strategies. We show that detected expressions are sparse however that aggregate responses reveal rich emotion trajectories. By modeling the link between the facial responses and ad effectiveness, we show that ad liking can be predicted accurately (ROC AUC = 0.eighty five) from webcam facial responses. Furthermore, the prediction of a modification in purchase intent is doable (ROC AUC = 0.78). Ad liking is shown by eliciting expressions, notably positive expressions. Driving purchase intent is additional complicated than simply creating viewers smile: peak positive responses that are immediately preceded by a complete appearance are additional probably to be effective. The results presented here demonstrate a reliable and generalizable system for predicting ad effectiveness automatically from facial responses while not a want to elicit self-report responses from the viewers. Additionally we have a tendency to can gain insight into the structure of effective ads.
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