# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 5 Discussion  
The article presents a novel approach to model the influence of automatically derived low-level video features on short-term ad engagement. Most existing approaches for modeling consumer ad engagement are based on video metadata and use high-level content features (e.g., genre, director, cast, tags, text ratings, and similar metadata). Such metadata are often scarce and may not capture the impact of visual video features on consumers. The implicit metadata available through the computational analysis of low-level video features might better capture some of the ad's visual effects on consumers. Moreover, in most cases, the existing approaches lack referential ground truth for consumer-perceived engagement.  
To this end, UES-SF scores on the four dimensions of consumer engagement were collected to provide the ground truth. Low-level video features were then derived and used to model the UES-SF scores. The results show that several features can be used to estimate consumer engagement, but each explains only a small portion of the variability for the respective UES-SF dimensions.  
For most low-level video features studied, we can identify their relationships with the four dimensions of UES-SF. In the existing literature, we have found no linear nor more complex models of user engagement scores based on low-level video descriptors. However, we found some evidence of U-shaped behavior of the models related to human behaviour [35,36]. Indeed, linear models have been shown to explain a statistically significant portion of the variability only in rare cases of our study. Given the tentative U-shaped model options, we opted for quadratic models and identified a satisfactory number of significant model relations.  
The upper limit of the explained variability related to the video contribution by any of the models is low for itself, ranging from 25% to 50%, see Table 11. This behaviour, also indicated by relatively small \(R^{2}\) values, was expected as the factors of personality and context influencing the user engagement were not part of this study. Consumer ad engagement is a complex construct that cannot be captured solely by visual features of an ad, but is influenced by many factors, such as context and consumer perceptions and attitudes, such as personality, brand likability, familiarity, and recall; see Equation (1).  
There are several limitations to the presented study. First, as a general limitation, the study addresses only short-term engagement and does not address possible changes or patterns of ad engagement over time. Second, the study participants represent a specific group of consumers within a narrow age range and in a specific location (the United States). This group may have specific consumer behavior and usage patterns, as well as social and/or cultural characteristics that cannot be generalized to other consumer groups or  
Figure 4: Scatter plot and corresponding linear regression plot for the optimal video feature against the overall User Engagement Score (UES). Note that the \(x\)-axis (abscissa) relates to categorical variable of Video ID; the assigned numerical values are equal to corresponding mean engagement scores.  
locations. In addition, only personal computer devices were used in this study, while usage patterns and consumer behavior in general may be different on mobile devices such as tablets and smartphones. Another potential limitation is the choice of UES-SF to collect ground truth data. UES-SF may be too general to provide more detailed insights into consumer engagement, such as brand awareness and recall, which are important factors in advertising. This potential problem needs to be explored in the future, as the current state of the art does not provide comparable psychometric instruments for measuring consumer engagement in in-video ads.