A Decision Support Method to Increase the Revenue of Ad Publishers in Waterfall Strategy

Abstract

Online advertising is one of the most important sources of income for many online publishers. The process is as easy as placing slots in the website and selling those slots in real time bidding auctions. Since websites load in few milliseconds, the bidding and selling process should not take too much time. Sellers or publishers of advertisements aim to maximize the revenue obtained through online advertising. In this paper, we propose a method to select the most profitable ad network for each ad request that is built upon our previous work [1]. The proposed method consists of two parts: a prediction model and a reinforcement learning modeling. We test two strategies of selecting ad network orderings. The first strategy uses the developed prediction model to greedily choose the network with the highest expected revenue. The second strategy is a two-step approach, where a reinforcement learning method is used to improve the revenue estimation of the prediction model. Using real AD auction data, we show that the ad network ordering obtained from the second strategy returns much higher revenue than the first strategy.