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    【學術通知】美國杜蘭大學弗里曼商學院助理教授譚寅亮:When to Play Your Advertisement? Optimal Insertion Policy of Behavioral Advertisement

    • 發布日期:2020-12-08
    • 點擊數:

      

    喻園管理論壇2020年第31期(總第648期)

    演講主題: When to Play Your Advertisement? Optimal Insertion Policy of Behavioral Advertisement

    主 講 人: 譚寅亮,美國杜蘭大學弗里曼商學院助理教授

    主 持 人: 關   旭,生產運作與物流管理系教授

    活動時間: 2020年12月11日(周五)10:00-11:30

    網絡直播平臺: 騰訊會議,會議ID:611 448 410

                                            會議密碼:328666

    主講人簡介:

    譚寅亮是美國杜蘭大學弗里曼商學院管理科學方向助理教授,戈德林國際教育中心行政主任。譚寅亮博士畢業于美國佛羅里達大學,學習運營管理及信息系統。他擁有豐富的商業分析方面的教學經驗,獲得過弗里曼商學院最佳教師獎。其研究興趣主要集中在科技管理與創新,電子產品定價,以及人工智能領域。他在國際頂級期刊Management Science, MIS Quarterly, Information Systems Research, Production and Operations Management多次發表論文?,F在擔任Production and Operations Management的資深編輯以及Decision Science的副編輯。他于2019年被評為世界最佳40名40歲以下的商學院教授,也是杜蘭大學歷史上第一個獲此殊榮的教授。

    活動簡介:

    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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