A Near-Optimal Bidding Strategy for Real-Time Display Advertising Auctions

Abstract

This paper 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 e_cient, implementable learning algorithm is proven to rapidly converge to the optimal strategy while achieving zero-regret and constituting a competitive equilib- rium. This is the _rst algorithmic solution to the online knapsack problem to o_er such theoretical guarantees without assuming a-priori knowledge of object values or costs. Further, it meets advertiser requirements by accommodating any valuation metric while satisfying budget constraints. Across a series of 100 simulated and ten 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.

First-Price Auctions in Online Display Advertising

“…This is in contrast with situations in which the publisher sells its inventory directly to advertisers without calling third-party exchanges (e.g., when Google sells YouTube impressions) or uses only a single exchange to sell the impressions. Indeed, as observed in Tunuguntla and Hoban (2021), secondprice auctions remain the standard when the publisher sells the impressions exclusively through one exchange, and, as such, the exchange is the final arbiter of impression placement (e.g., scenarios when OpenX runs the final auction [OpenX 2019]).…”

Advertising Media and Target Audience Optimization via High-dimensional Bandits

“…One branch of this literature studies the specifc problem of bidding in online auctions, cf. Chapelle (2015), Balseiro et al (2015), Baardman et al (2019), Balseiro and Gur (2019), Waisman et al (2019), Tunuguntla and Hoban (2021). Another branch is concerned with the related problem of scheduling different ads, cf.…”

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

“…1 Together, RTB and programmatic direct advertising are the dominant way that online display ads are sold, with projected spending set to reach $81.58 billion representing 86.5% of total display ad spending in the United States in 2021 (eMarketer 2021b). Besides their economic importance, display advertising markets have been of interest to researchers (Berman 2018; Budak et al 2014; Choi and Mela 2019; Goldfarb and Tucker 2011a, b; Johnson, Shriver, and Du 2020; Lambrecht and Tucker 2013; Miller and Skiera 2017; Rafieian and Yoganarasimhan 2021; Reiley, Lewis, and Schreiner 2012; Tunuguntla and Hoban 2021), in part because they provide detailed tracking of ad transactions and user, ad buyer, and publisher behavior.…”

“… 1 Institutional details of RTB are described further in Choi et al (2020) and Tunuguntla and Hoban (2021).…”