A decision support method to increase the revenue of ad publishers in waterfall strategy

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 tend to maximize the revenue obtained through online advertising. Recently, we proposed a method to select the most profitable ad network for each ad request. Our method consists of two parts: a prediction model and a reinforcement learning modeling. In this paper we evaluate the prediction model and test two methods of ad network ordering. First we use prediction model and second the two-step method. We show that the ad network ordering obtained from the second step of our method increases the revenue and the prediction model provides initial values which could not be used as a decision method.