Optimizing Performance-Based Internet Advertisement Campaigns

AbstractThis study provides an approach to manage an ongoing Internet ad campaign that substantially improves the number of clicks and the revenue earned from clicks. The problem we study is faced by an Internet advertising firm (Chitika) that operates in the Boston area. Chitika contracts with publishers to place relevant advertisements (ads) over a specified period on publisher websites. Ad revenue accrues to the firm and the publisher only if a visitor clicks on an ad (i.e., we are considering the cost-per-click model in this study). This might imply that all visitors to the publisher’s website be shown ads. However, this is not the case if the publisher imposes a click-through-rate constraint on the advertising firm. This performance constraint captures the publisher’s desire to limit ad clutter on the website and hold the advertising firm responsible for the publisher’s opportunity cost of showing an ad that did not result in a click. We develop a predictive model of a visitor clicking on a given ad. Using this prediction of the probability of a click, we develop a decision model that uses a threshold to decide whether or not to show an ad to the visitor. The decision model’s objective is to maximize the advertising firm’s revenue subject to a click-through-rate constraint. A key contribution of this paper is to characterize the structure of the optimal solution. We study and contrast two competing solutions: (1) a static solution, and (2) a rolling-horizon solution that resolves the problem at certain points in the planning horizon. The static solution is shown to be optimal when accurate information on the input parameters to the problem is known. However, when the parameters to the model can only be estimated with some error, the rolling-horizon solution can perform better than the static solution. When using the rolling-horizon solution, it becomes important to choose the appropriate resolving frequency. The implemented models operate in real time in Chitika’s advertising network. Implementation challenges and the business impact of our solution are discussed. To present a head-to-head comparison of our implemented approach with the past practice at Chitika, we implemented our solution in parallel to the past practice.