Pan, S., Kitts, B., Zhou, T., He, H., Shetty, B., Flores, A., Gligorijevic, Dj., Pan, J., Mao, T., Gultekin, S., Zhang, J. (2020) ” Bid Shading by Auction Clearing Price Prediction and Fast Surplus Maximization Search“, AdKDD workshop 2020 at the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), San Diego, California, August 23, 2020
This paper describes a new win-rate based bid shading algorithm (WR) that does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform (SSP). The method uses a modified logistic regression to predict the profit from each possible shaded bid price. The function form allows fast maximization at run-time, a key requirement for Real-Time Bidding (RTB) systems. We report production results from this method along with several other algorithms. We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost. Further, the particular approach described in this paper captures 7% more profit for advertisers, than do benchmark methods of just bidding the most probable winning price. We also report 4.3% higher surplus than an industry Sell-Side Platform shading service. Furthermore, we observed 3% – 7% lower eCPM, eCPC and eCPA when the algorithm was integrated with budget controllers. We attribute the gains above as being mainly due to the explicit maximization of the surplus function, and note that other algorithms can take advantage of this same approach.