Joint Optimization of Multiple Performance Metrics in Online Video Advertising
Sahin Geyik*, Turn Inc.; Sergey Faleev, Turn Inc.; Jianqiang Shen, Turn Inc.; Sean O'Donnell, Turn Inc.; Santanu Kolay, Turn Inc.
The ﬁeld of online advertising, in essence, deals with the problem of presenting ads to online users in the most appropriate contexts to achieve a multitude of advertiser goals. A vast amount of work in online advertising has been focused on optimizing banner display advertising campaigns where the main goal lies in direct response metrics, often as clicks or conversions. In this paper, we explore the newly popularized space of online video advertising, where brand recognition is the key focus. We propose a framework based on a feedback mechanism where we optimize multiple video speciﬁc performance indicators while making sure the delivery constraints (budget and user reach) of advertisers are satisﬁed. While our main focus is on improving metrics such as engagement (amount of view time), and viewability (whether a campaign is within eyesight of a user), we also discuss the possibilities of expanding to other metrics. We demonstrate the beneﬁt of our framework via empirical results in multiple real-world advertising campaigns. To the best of our knowledge, this is the ﬁrst paper that deals with the unique challenges arising from the nature of online video advertising.
Filed under: Optimization Techniques