Zhou, T., He, H., Pan, S., Karlsson, N., Shetty, B., Kitts, B., Gligorijevic, Dj., Pan, J., Gultekin, S., Mao, T., Long, J., Flores, A., (2021) “Efficient deep distribution network for bid shading in First-Price Auctions“, Proc. 27th ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD 2021), August 14. – 18. 2021, Virtual Event, Singapore
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference between these auctions, demand-side platforms (DSPs) have had to update their bidding strategies to avoid bidding unnecessarily high and hence overpaying. Bid shading is the name given to techniques for reducing the price intended for second-price auctions, in order to balance cost and winning probability in a first-price auction setup. In this study, we introduce a novel deep neural network distribution-based approach in tackling both open (non-censored) and closed (censored) online first-price ad auctions. Offline and online A/B testing results show that our algorithm outperforms previous state-of-art algorithms in terms of both surplus and effective cost per action (eCPX) metrics. Furthermore, the algorithm is optimized in run-time and has been deployed into VerizonMedia DSP as productin algorithm, serving hundreds of billions of bid requests per day. Online A/B test shows that advertiser’s ROI are improved by +2.4%, +2.4%, and +8.6% for impression based(CPM), click based (CPC), and conversion based (CPA) campaigns respectively.