Predicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads
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Picard_Predicting ad.pdf
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Author(s)
Kaliouby, Rana El
Cohn, Jeffrey F.
McDuff, Daniel Jonathan
Picard, Rosalind W.
Date Issued
December 2014
Journal
IEEE Transactions on Affective Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
McDuff, Daniel, Rana El Kaliouby, Jeffrey F. Cohn, and Rosalind W. Picard. “Predicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads.” IEEE Transactions on Affective Computing 6, no. 3 (July 1, 2015): 223–235.
Version
Author's final manuscript
Abstract
Billions of online video ads are viewed every month. We present a large-scale analysis of facial responses to video content measured over the Internet and their relationship to marketing effectiveness. We collected over 12,000 facial responses from 1,223 people to 170 ads from a range of markets and product categories. The facial responses were automatically coded frame-by-frame. Collection and coding of these 3.7 million frames would not have been feasible with traditional research methods. We show that detected expressions are sparse but that aggregate responses reveal rich emotion trajectories. By modeling the relationship between the facial responses and ad effectiveness, we show that ad liking can be predicted accurately (ROC AUC = 0.85) from webcam facial responses. Furthermore, the prediction of a change in purchase intent is possible (ROC AUC = 0.78). Ad liking is shown by eliciting expressions, particularly positive expressions. Driving purchase intent is more complex than just making viewers smile: peak positive responses that are immediately preceded by a brand appearance are more likely to be effective. The results presented here demonstrate a reliable and generalizable system for predicting ad effectiveness automatically from facial responses without a need to elicit self-report responses from the viewers. In addition we can gain insight into the structure of effective ads.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/taffc.2014.2384198
