Search and Score-based Waterfall Auction Optimization

TL;DR

This paper introduces a data-driven method for optimizing waterfall auctions in online advertising by learning user valuation distributions and iteratively searching for revenue-maximizing strategies, outperforming manual approaches.

Contribution

It presents a novel approach to estimate user valuation distributions and a scoring-based iterative search method for waterfall optimization, ensuring revenue improvement.

Findings

Improves revenue of real-world waterfalls

Outperforms manual expert optimization

Guarantees convergence to a local optimum

Abstract

Online advertising is a major source of income for many online companies. One common approach is to sell online advertisements via waterfall auctions, through which a publisher makes sequential price offers to ad networks. The publisher controls the order and prices of the waterfall in an attempt to maximize his revenue. In this work, we propose a methodology to learn a waterfall strategy from historical data by wisely searching in the space of possible waterfalls and selecting the one leading to the highest revenues. The contribution of this work is twofold; First, we propose a novel method to estimate the valuation distribution of each user, with respect to each ad network. Second, we utilize the valuation matrix to score our candidate waterfalls as part of a procedure that iteratively searches in local neighborhoods. Our framework guarantees that the waterfall revenue improves between iterations ultimately converging into a local optimum. Real-world demonstrations are provided to show that the proposed method improves the total revenue of real-world waterfalls, as compared to manual expert optimization. Finally, the code and the data are available here.