Internet Advertising Firms

Abstract

In this chapter, we analyze a problem faced by an Internet advertising firm (Chitika) that operates in the Boston area. Chitika contracts with publishers to place relevant 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 CPC model). 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 ad-firm. This performance constraint captures the publisher’s desire to limit ad clutter on the website and holds the ad-firm responsible for the publisher’s opportunity cost of showing an ad that does not result in a click. We present a predictive model of a visitor clicking on a given ad. Using this prediction of the probability of a click, we present 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. We present and contrast two competing solutions: (1) a static solution, and (2) a rolling-horizon solution that re-solves 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 re-solving frequency.