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讲座预告 | 该何时播放广告?基于消费者行为的最优策略研究

发布时间:2021-01-18    来源:

讲座时间: 2021年1月18日(周一)14:00-17:30

腾讯会议:730550287

主讲人:谭寅亮


主讲人介绍

谭寅亮教授是美国杜兰大学弗里曼商学院管理科学方向助理教授,戈德林国际教育中心行政主任。博士毕业于美国佛罗里达大学,学习运营管理及信息系统。其研究兴趣主要集中在科技管理与创新,电子产品定价,以及人工智能领域。他在国际顶级期刊Management Science, MIS Quarterly, Information Systems Research, Production and Operations Management多次发表论文。现在担任Production and Operations Management的资深编辑以及Decision Science的副编辑。于2019年被评为世界最佳40名40岁以下的商学院教授,也是杜兰大学历史上第一个获此殊荣的教授。

讲座内容

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. 
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