CDSA Webinar | Multi-Objective Personalization of the Length and Skippability of Video Advertisements.

18/09/2023 - 18/09/2023

CDSA Webinar | Multi-Objective Personalization of the Length and Skippability of Video Advertisements.

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Abstract: In this paper, we study two features of digital video ads on content-streaming platforms: length and skippability. Working with, we conduct a field experiment and randomly assign users to the Skippable/Long and Non-Skippable/Short versions of the same ad. We find that compared to the Non-Skippable/Short ad, the Skippable/Long ad version in our study increases ad consumption but decreases video consumption. This substitution pattern between ad and video consumption leads to a challenge for platforms seeking to maximize both outcomes. To address this challenge, we develop algorithms for multi-objective personalization that use individual-level substitution patterns to optimize ad and video consumption. The results show that multi-objective personalized policies can significantly improve both ad and video consumption outcomes over single-objective policies. In particular, we show that compared to a single-objective policy optimized for video consumption, there exists a multi-objective policy on the Pareto frontier that increases ad consumption by 61% at the expense of only a 4% decrease in video consumption. Similarly, compared to the single-objective policy optimized for ad consumption, there is a multi-objective policy that increases video consumption by 47% while decreasing ad consumption by just 13%. We conclude by discussing the practical implications for platform decision-making in real-time.

About the Speaker:  Prof. Anuj Kapoor is an Assistant Professor of Quantitative Marketing at IIM Ahmedabad. His research interests are in the economics of digitization, artificial intelligence, privacy, and digital platforms. His research focuses on understanding how big data and artificial intelligence shape consumer welfare and digital markets. He uses a quasi- and actual experimental variation to explore how different types of human behavior in varying contexts affect algorithms. He works closely with firms to suggest to them more ways to become data-driven. He has ongoing collaborations with various tech start-ups in India in the digital media and health tech space. Anuj received his Ph.D. in Business Administration (Quantitative Marketing and Economics) from the David Eccles School of Business at the University of Utah. After his doctoral studies, he worked in the data science space in San Diego, USA. At IIMA, he teaches electives on Artificial Intelligence and Marketing and Privacy Paradox: Data, Artificial Intelligence, and Digital Platforms.