# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 1 Introduction  
The rapid and sustained development of new content consumption models based on personalized media access and streaming is having a significant impact on the established advertising landscape [1]. Audience measurement providers are struggling to keep pace with these advances and to provide advertisers with improved advertising effectiveness metrics. The latter requires robust metrics to demonstrate the impact of advertising campaigns on potential consumers.  
Exposure to ads can be quantified in the context of consumer engagement (UE). To this end, brands use innovative tactics to create memorable impressions in order to increase consumer awareness and engagement with ads. Higher engagement leads to more ad exposure, improves brand memorability, and positively affects consumer purchase intentions [2; 3; 4]. However, exposure to ads and its effects are complex and depend on several factors. Subjective and objective factors that affect ad exposure include the circumstances in which a particular advertisement is consumed (context, denoted by \(c\)), subjective conditions related to a consumer's personality and/or temporal mood (person, denoted by \(p\)), and the characteristics of the advertising video materials (video, denoted by \(v\)). A measured engagement using of UES-SF instrument is denoted by \(UE\), and the measurement noise of this measurement is denoted by \(\varepsilon\):  
\[f(c,p,v)=UE+\varepsilon \tag{1}\]A thorough model of ad engagement necessarily considers all of the three above mentioned factor groups. The present research focuses on parts related to the impact of video material characteristics on user engagement. We present a novel approach to modeling the relation between consumer ad engagement and visual video features derived from ads. The advantage of the proposed approach is that it is unobtrusive, scalable, and can be automated. To our knowledge, most of the existing approaches estimate consumer engagement based on user and content metadata (such as the number of views, video resolution, and genre), ignoring the potential effects that visual video features might have on consumer engagement. In addition, many existing approaches lack a referential ground truth for user-perceived engagement. We hypothesize that the low-level video features of an ad might capture some of its visual effects on consumers that cannot be modeled from the metadata itself.  
The article has two objectives: a) to identify relevant low-level video features that can be automatically derived from video ad, and b) to test the feasibility of the model built on these features by evaluating their predictive power for assessing consumer engagement. The reference ground truth for the consumer engagement was obtained using the User Engagement Scale-Short Form (UES-SF), a standardized psychometric instrument which has been shown to capture several aspects of engagement (focused attention, aesthetic appeal, perceived usability, and reward) and provide reliable results [5]. The ground truth was collected in an online crowdsourcing study (N = 150 participants) that measured participants' engagement in video ads using UES-SF. The target audience of the present study was young adults, the largest segment of digital natives who engage in online multimedia, especially ad-supported video streaming. The focus was on short-term exposure to in-video ads, which are common in ad-supported video streaming [6].  
The remainder of this paper is organized as follows: Section 2 briefly introduces related work and discusses the concepts of ad exposure, consumer engagement, and relevant low-level video features. Section 3 presents the materials and methods used in the observational study, gathering the ground truth of perceived consumer engagement, and constructing a model based on video features. Section 4 presents the results of the study and the evaluation of low-level video features for assessing consumer ad engagement scores. In Section 5, we discuss our findings, shortcomings of the presented research, and future work.