Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement

Abstract

The article addresses modelling of consumer engagement in video advertising based on automatically derived low-level video features. The focus is on a young consumer group (18–24 years old) that uses ad-supported online streaming more than any other group. The reference ground truth for consumer engagement was collected in an online crowdsourcing study (N = 150 participants) using the User Engagement Scale-Short Form (UES-SF). Several aspects of consumer engagement were modeled: focused attention, aesthetic appeal, perceived usability, and reward. The contribution of low-level video features was assessed using both the linear and nonlinear models. The best predictions were obtained for the UES-SF dimension Aesthetic Appeal (R2=0.35) using a nonlinear model. Overall, the results show that several video features are statistically significant in predicting consumer engagement with an ad. We have identified linear relations with Lighting Key and quadratic relations with Color Variance and Motion features (p0.02). However, their explained variance is relatively low (up to 25%).

Keywords

• multimedia signal processing
• low-level video features
• consumer engagement
• automatic estimation
• advertising exposure