Optimizing the revenue for ad exchanges in header bidding advertising markets

With the ever-growing popularization of Real Time Bidding (RTB) advertising, the Ad Exchange (AdX) platform has long enjoyed a dominant position in the RTB ecosystem due to its unique role in bridging publishers and advertisers in the supply and demand sides, respectively. A novel technology called header bidding emerged in the recent one or two years, however, is widely believed to have the potential of challenging this dominant position. Compared with RTB markets, header bidding establishes a priority sub-market allowing bidding partners of the publisher submit their bids before the ad impression delivered to the open AdX platform, resulting in a decreased winning probability and revenue for the AdX. As such, there is a critical need for the AdX to tackle this challenge so as to better coexist with header bidding platforms. This need motivates our research. We utilize stochastic programming approach and establish a stochastic optimization model with risk constraints to optimize the pricing strategy for the AdX, considering that the highest bids from the bidding partners can be characterized by random variables. We study the equivalent forms of our proposed model in case when the randomness is characterized by uniform or normal random variables. With the computational experiment approach, we validate our proposed model, and the experimental results indicate that both the risk tolerance of the AdX and the distribution of randomness of the highest bid from the bidding partners can greatly affect the optimal strategy and the corresponding optimal revenue of the AdX. Our work highlights the importance of the risk level of the AdX and the distribution of the randomness generated by the partners to the decision making process of the AdXs in header bidding markets.