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會(huì)議號(hào):297921134

密碼:123456

01主講人介紹

譚寅亮是美國休斯頓大學(xué)鮑爾商學(xué)院決策和信息科學(xué)終身教授，鮑爾講席教授，供應(yīng)鏈管理方向系主任。此前他在美國杜蘭大學(xué)弗里曼商學(xué)院管理科學(xué)方向擔(dān)任助理教授，戈德林國際教育中心行政主任，并獲得終身教授。譚寅亮博士畢業(yè)于美國佛羅里達(dá)大學(xué)，學(xué)習(xí)運(yùn)營管理及信息系統(tǒng)。他擁有豐富的商業(yè)分析方面的教學(xué)經(jīng)驗(yàn)，獲得過弗里曼商學(xué)院最佳教師獎(jiǎng)。其研究興趣主要集中在科技管理與創(chuàng)新，電子產(chǎn)品定價(jià)，以及人工智能領(lǐng)域。他在國際頂級(jí)期刊Management Science, MIS Quarterly, Information Systems Research, Production and Operations Management多次發(fā)表論文。現(xiàn)在擔(dān)任Production and Operations Management的資深編輯以及Decision Science的副編輯。他于2019年被評(píng)為世界最佳40名40歲以下的商學(xué)院教授,也是杜蘭大學(xué)歷史上第一個(gè)獲此殊榮的教授。

02講座摘要

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.