Targeting Pre-Roll Ads using Video Analytics
Abstract:
Pre-roll video ads continue to rise at an unparalleled pace, creating new opportunities and challenges. They are more immersive than conventional banner ads and must be viewed at least partially before the content video is played. On the other hand, the prevailing skippable format of pre-roll video ads that allows viewers to skip ads after five seconds generates opportunity costs for advertisers and online platforms when the ad is skipped. Against this backdrop, we propose a novel video analytics method for improving pre-roll video ad performance by extracting multi-modal (audio, video, text) properties from both video ads and content videos using deep learning and signal processing techniques, and then analyzing their effect on video ad completion. The findings indicate that the ad-content congruence in various modalities is essential in explaining viewers' ad completion. Specifically, visual congruence (i.e., celebrity overlap in ad and content) and textual congruence (i.e., topic similarity of ad and content) play important roles as viewers may shape ex-ante expectations of the congruence based on visual cues (i.e., thumbnail images) and previous experience (i.e., watched content clips from the same program) before watching the content video. We also discover, through predictive analyses, that video ad completion can be reliably predicted by features derived from the proposed method. Surprisingly, there is no discernible loss of predictive power when analyzing only the first five seconds of ads and content videos rather than their entire length, resulting in significant cost savings when processing large video datasets.
Bio:
Donghyuk Shin is an assistant professor in the Department of Information Systems at the W. P. Carey School of Business, Arizona State University. Prior to joining ASU, he was a machine learning scientist at Amazon Web Services and received his Ph.D. in Computer Science from the University of Texas at Austin. His main research interest lies at the intersection of machine learning and information systems focusing on leveraging ML to solve business problems and applications. He has worked on digital and mobile platforms, social networks, recommender systems, display advertising, and online markets. His work has been published in MIS Quarterly and top machine learning conferences including NeurIPS, ACM RecSys and CIKM.