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- Title
A Near-Optimal Bidding Strategy for Real-Time Display Advertising Auctions.
- Authors
Tunuguntla, Srinivas; Hoban, Paul R.
- Abstract
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.
- Subjects
BIDDING strategies; REAL-time bidding (Internet advertising); ALGORITHMS; DISPLAY advertising; INTERNET auctions
- Publication
Journal of Marketing Research (JMR), 2021, Vol 58, Issue 1, p1
- ISSN
0022-2437
- Publication type
Article
- DOI
10.1177/0022243720968547