EXPRESS: Context Information can Increase Revenue in Online Display Advertising Auctions: Evidence from a Policy Change

2021 ◽  
pp. 002224372110702
Author(s):  
Sıla Ada ◽  
Nadia Abou Nabout ◽  
Elea McDonnell Feit

Ad exchanges where real-time auctions for display ad impressions take place historically emphasized user targeting, and advertisers sometimes did not know which sites their ads would appear on, i.e., the ad context. More recently, some ad exchanges have been encouraging publishers to provide context information to ad buyers, allowing them to adjust their bids for ads at specific sites. This paper explores the empirical effect of a change in context information provided by a private European ad exchange. Analyzing this as a quasi-experiment using difference-in-differences, the authors find that average revenue per impression rose when the exchange provided subdomain information to ad buyers. Thus, ad context information is important to ad buyers, and they will act on it. Revenue per impression rises for nearly all sites, which is what is predicted by auction theory when rational buyers with heterogeneous preferences are given more information. The exception to this are sites with thin markets prior to the policy change; consistent with theory, these sites do not show a rise in prices. This paper adds evidence that ad exchanges with reputable publishers, particularly smaller volume, highquality sites, should provide ad buyers with context information, which can be done at almost no cost.

2020 ◽  
pp. 002224372096854
Author(s):  
Srinivas Tunuguntla ◽  
Paul R. Hoban

This article introduces a near-optimal bidding algorithm for use in real-time display advertising auctions. These auctions constitute a dominant distribution channel for internet display advertising and a potential funding model for addressable media. The proposed efficient, implementable learning algorithm is proven to rapidly converge to the optimal strategy while achieving zero regret and constituting a competitive equilibrium. This is the first algorithmic solution to the online knapsack problem to offer such theoretical guarantees without assuming a priori knowledge of object values or costs. Furthermore, it meets advertiser requirements by accommodating any valuation metric while satisfying budget constraints. Across a series of 100 simulated and 10 real-world campaigns, the algorithm delivers 98% of the value achievable with perfect foresight and outperforms the best available alternative by 11%. Finally, we show how the algorithm can be augmented to simultaneously estimate impression values and learn the bidding policy. Across a series of simulations, we show that the total regret delivered under this dual objective is less than that from any competing algorithm required only to learn the bidding policy.


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