Influencer marketing has become a very popular tool to reach customers. Despite the rapid growth in influencer videos, there has been little research on the effectiveness of their constituent features in explaining video engagement. We study YouTube influencers and analyze their unstructured video data across text, audio and images using an "interpretable deep learning" framework that accomplishes both goals of prediction and interpretation. Our prediction-based approach analyzes unstructured data and finds that "what is said" in words (text) is more influential than "how it is said" in imagery (images) or acoustics (audio). Our novel interpretation-based approach is implemented after completion of model prediction by analyzing the same source of unstructured data to measure importance attributed to the video features. We eliminate several spurious relationships in two steps, identifying a subset of relationships which are confirmed using theory. We uncover novel findings that establish distinct associations for measures of shallow and deep engagement based on the dual-system framework of human thinking. Our approach is validated using simulated data, and we discuss the learnings from our findings for influencers and brands.

Binge-watching TV shows on streaming services is becoming increasingly popular. However, there is a paucity of comprehensive metrics to effectively summarize such media watching behavior. We address this gap by presenting two new metrics—Bingeability and Ad Tolerance—to quantify key aspects of watching streaming TV interspersed with ads. These metrics are motivated by consumer psychology literature on hedonic adaptation and also reflect media consumption behavior. Using machine learning methods, including ensembles of classification trees, we identify the key predictors of these metrics, study non-linear effects, and rank the predictors in order of predictive power. The superiority and validity of these metrics is also discussed.