Auction Design for ROI-Constrained Buyers

We combine theory and empirics to (i) show that some buyers in online advertising markets are financially constrained and (ii) demonstrate how to design auctions that take into account such financial constraints. We use data from a field experiment where reserve prices were randomized on Google's advertising exchange. We find that, contrary to the predictions of classical auction theory, a significant set of buyers lowers their bids when reserve prices go up. We show that this behavior can be explained if we assume buyers have constraints on their minimum return on investment (ROI). We proceed to design auctions for ROI-constrained buyers. We show that optimal auctions for symmetric ROI-constrained buyers are either second-price auctions with reduced reserve prices or subsidized second-price auctions. For asymmetric buyers, the optimal auction involves a modification of virtual values. Going back to the data, we show that using ROI-aware optimal auctions can lead to large revenue gains as well as large welfare gains for buyers.