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Research: Projects

with Puneet Manchanda and Eric Schwartz

A majority of US households view on-demand content on streaming video services and ad spending on these online platforms is growing rapidly. However, extant research on streaming media has not explored the balance between the interest of the viewer (content consumption) with the incentives of the platform (ad exposure). We characterize this interplay using two new metrics based on viewing data on a streaming media platform. The first metric, Bingeability, measures non-linear content consumption while the second metric, Ad Tolerance, measures the willingness of a viewer to view ads and to continue viewing after ad exposure. Using causal machine learning methods that combine a tree-based model with instrumental variables, we capture the impact of ad delivery on Bingeability and Ad Tolerance for individual viewers for each viewing session. The results indicate that the average “sweet spot” that balances the interest of the viewer and the platform consists of short ad breaks that are equally spaced at long intervals during a viewing session. We discuss the implications of our results for managers of streaming platforms.

Keywords: Advertising, Targeting, Streaming Media, Machine Learning, Causal Inference


with Puneet Manchanda 

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 elements in explaining video engagement. We study YouTube influencers and analyze their unstructured video data across text, audio and images using a novel “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) followed by acoustics (audio). Our 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 elements. We eliminate several spurious and confounded relationships, and identify a smaller subset of theory-based relationships. We uncover novel findings that establish distinct effects for measures of shallow and deep engagement which are 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.

Keywords: Influencer Marketing, Video Advertising, Social Media, Interpretable Deep Learning, Transfer Learning

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