Reserve price optimization with header bidding and Ad Exchange

The extremely high turnover of online advertising makes it one of the most important sources of income for many online ad publishers. Advertising through world wide web is mainly performed by Real Time Bidding in which the advertisers and the publishers participate to online auctions for trading the ad slots. Publishers usually set the reserve prices for their ad slots and any winning buyer in the auctions performed by ad exchanges has to pay at least the value of reserve price. Header bidding is a way of real time bidding and it becomes very popular, but how to use it together with advertising Exchanges (AdX) to achieve good revenue for online publishers is not well studied. In this paper, we propose a method that makes use of the historical auction data from header bidding and AdX to learn and optimize the reserve price for AdX. We propose a method based on supervised learning and survival analysis to increase the reserve price. The method assumes no information about current auctions and the bids of header bidding and AdX response are predicted and used to determine the highest possible reserve price. The experiments with real-world auction data show the promising results of our method in increasing the expected revenue of online publishers