Reserve Price Optimization at Scale

Abstract

Online advertising is a multi-billion dollar industry largely responsible for keeping most online content free and content creators (publishers) in business. In one aspect of advertising sales, impressions are auctioned off in second price auctions on an auction-by-auction basis through what is known as real-time bidding (RTB). An important mechanism through which publishers can influence how much revenue they earn is reserve pricing in RTB auctions. The optimal reserve price problem is well studied in both applied and academic literatures. However, few solutions are suited to RTB, where billions of auctions for ad space on millions of different sites and Internet users are conducted each day among bidders with heterogenous valuations. In particular, existing solutions are not robust to violations of assumptions common in auction theory and do not scale to processing terabytes of data each hour, a high dimensional feature space, and a fast changing demand landscape. In this paper, we describe a scalable, online, real-time, incrementally updated reserve price optimizer for RTB that is currently implemented as part of the AppNexus Publisher Suite. Our solution applies an online learning approach, maximizing a custom cost function suited to reserve price optimization. We demonstrate the scalability and feasibility with the results from the reserve price optimizer deployed in a production environment. In the production deployed optimizer, the average revenue lift was 34.4% with 95% confidence intervals (33.2%, 35.6%) from more than 8 billion auctions over 46 days, a substantial increase over non-optimized and often manually set rule based reserve prices.