Optimal Auction Design with User Coupons in Advertising Systems

Optimal Auction Design with User Coupons in Advertising Systems

Xiaodong Liu, Zhikang Fan, Yiming Ding, Yuan Guo, Lihua Zhang, Changcheng Li, Dongying Kong, Han Li, Weiran Shen

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 2904-2912. https://doi.org/10.24963/ijcai.2024/322

Online advertising is a major revenue source for most Internet companies. The advertising opportunities are usually sold to advertisers through auctions that take into account the bids of the advertisers and the click-through rates (CTRs) and the conversion rates (CVRs) of the users. Standard auction design theory perceives both the CTRs and the CVRs as constants. We consider a new auction mechanism that offers coupons to users when displaying the ads. Such coupons allow the user to buy the advertisers' products or services at a lower price, which increases both the CTRs and the CVRs of the ads. In this paper, we formulate the problem mathematically and perform a systematic analysis. We characterize the set of individually rational and incentive compatible mechanisms in our setting. Based on the characterization, we identify the optimal strategy of offering coupons that maximizes the platform's expected revenue. We also conduct extensive experiments on both synthetic data and industrial data. Our experiment results show that our mechanism significantly improves both the revenue and welfare of the platform, thereby creating a win-win situation for all parties including the platform, the advertisers, and the user.
Keywords:
Game Theory and Economic Paradigms: GTEP: Auctions and market-based systems
Game Theory and Economic Paradigms: GTEP: Mechanism design
Game Theory and Economic Paradigms: GTEP: Noncooperative games