Over the past few years, more and more Internet advertisers have started using automated bidding for optimizing their advertising campaigns. Such advertisers have an optimization goal (e.g. to maximize conversions), and some constraints (e.g. a budget or an upper bound on average cost per conversion), and the automated bidding system optimizes their auction bids on their behalf. Often, these advertisers participate on multiple advertising channels and try to optimize across these channels. A central question that remains unexplored is how automated bidding affects optimal auction design in the multi-channel setting. In this paper, we study the problem of setting auction reserve prices in the multi-channel setting. In particular, we shed light on the revenue implications of whether each channel optimizes its reserve price locally, or whether the channels optimize them globally to maximize total revenue. Motivated by practice, we consider two models: one in which the channels have full freedom to set reserve prices, and another in which the channels have to respect floor prices set by the publisher. We show that in the first model, welfare and revenue loss from local optimization is bounded by a function of the advertisers' inputs, but is independent of the number of channels and bidders. In stark contrast, we show that the revenue from local optimization could be arbitrarily smaller than those from global optimization in the second model.
翻译:过去几年来,越来越多的互联网广告商开始使用自动投标来优化广告宣传,这些广告商开始使用自动化投标来优化广告宣传。这些广告商有一个优化目标(例如最大限度地实现转换),以及一些制约因素(例如预算或按每次转换平均成本设定上限),自动招标系统代表他们优化了拍卖投标。这些广告商通常参加多个广告渠道,并试图在这些渠道之间优化。一个尚未探讨的中心问题是,自动化投标如何影响多渠道环境下的最佳拍卖设计。在本文中,我们研究了多渠道设置拍卖储备价格的问题。特别是,我们阐明了每个频道是否优化其当地储备价格,或者频道是否优化其全球总收入。我们受实践激励,我们考虑两种模式:一种是渠道完全自由设定保留价格,另一个是渠道必须尊重出版商设定的底价。我们在第一个模型中发现,本地优化带来的福利和收入损失受多渠道设定的固定价格制约,而另一种是广告商投入的功能约束,但与当地最佳化的渠道和最佳化程度相比,我们是没有多少次的。