Date: March 10th, 2022

Time: 10:00 am

Venue: Room 0411, Teaching Building 0#, Jiuli Campus

Tencent Meeting ID: 266-720-462

Event Details:

Lecturer: Professor Yinliang Tan, Ball School of Business, University of Houston in USA

About the Lecturer:

Yinliang Tan is a tenured professor of decision-making and information science at the Ball School of Business at the University of Houston, the Ball Chair Professor, and the head of the Department of Supply Chain Management. Before that, he was an assistant professor of management science at the Freeman School of Business at Tulane University, executive director of the Goldling Center for International Education, and was a tenured professor and chair professor. Dr. Yinliang Tan graduated from the Warrington School of Business, University of Florida, USA, and studied operations management and information systems. He has extensive teaching experience in business analytics and has won the Freeman School of Business Teacher of the Year Award. His research interests mainly focus on technology management and innovation, electronic product pricing, and artificial intelligence. He has published papers in the top international journals Management Science, MIS Quarterly, Information Systems Research, Production and Operations Management, Decision Science, etc., and has won the Best Paper Award at the International Decision Science Annual Conference. Dr. Tan is currently a senior editor of Production and Operations Management (a top international journal) and an associate editor of Decision Science. He was named the world's top 40 business school professors under 40 in 2019, the first professor in Tulane University's history to receive this honor.

About the Lecture:

Digital advertisements offer a full spectrum of behavioral customization for timing and content capabilities. The existing research in display advertising has predominantly concentrated on the content of advertising; however, our focus is on optimizing the timing of display advertising. In practice, users are constantly adjusting their engagement with content as they process new information continuously. The recent development of emotional tracking and wearable technologies allows platforms to monitor the user’s engagement in real time. We model the user’s continuous engagement process through a Brownian motion. The proposed optimal policy regarding the timing of behavioral advertising is based on a threshold policy with a trigger threshold and target level. Specifically, the platform should insert the advertisement when the user’s engagement level reaches the trigger threshold, and the length of the advertisement should let the user’s engagement level drop to the target level. Analogous to the familiar idea of “price discrimination,” the methods we propose in this study allow the platforms to maximize their revenue by “discriminatory” customization of the timing and length of the advertisement based on the behavior of individual users. Finally, we quantify the benefits of the proposed policy by comparing it with the practically prevalent policies (i.e., pre-roll, mid-roll and a mix of the two) through a simulation study. Our results reveal that for a wide range of settings, the proposed policy not only significantly increases the platform’s profitability, but also improves the completion rate at which consumers finish viewing the advertisement.