Can we predict the impact of a piece of advertising on the viewer’s ‘liking’ and ‘purchase intent’ simply by observing how viewers react? This paper published by researchers from the MIT Media Lab, in association with Affectiva and MARS, suggest we can. Focused on the fast moving consumer goods (FMCG) categories, this study analyzes over 12,000 responses to 170 ads collected in 4 European countries.  To do this they employ Affectiva’s online platform to collect data from over a thousand individuals and focus on a few keys self-reported responses from individual viewers:

  1. How much did you LIKE the AD that you just watched?
  2. Next time you are buying [type of product] how likely are you TO PURCHASE products from each of these brands?


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 frameby- 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.