Incentive Misalignments in Programmatic Advertising: Evidence from a Randomized Field Experiment
通过大规模随机田野实验，发现广告平台为追求自身收益而瞄准高购买概率消费者，但广告对低购买概率消费者更有效，导致企业利润和整体福利损失，并提出了基于个体估计的定向优化方案。
In programmatic advertising, firms outsource the bidding for ad impressions to ad platforms. Although firms are interested in targeting consumers that respond positively to advertising, ad platforms are usually rewarded for targeting consumers with high overall purchase probability. We develop a theoretical model that shows if consumers with high baseline purchase probability respond more positively to advertising, then firms and ad platforms agree on which consumers to target. If, conversely, consumers with low baseline purchase probability are the ones for which ads work best, then ad platforms target consumers that firms do not want to target—the incentives are misaligned. We conduct a large-scale randomized field experiment, targeting 208,538 individual consumers, in a display retargeting campaign. Our unique data set allows us to both causally identify advertising effectiveness and estimate the degree of incentive misalignments between the firm and ad platform. In accordance with the contracted incentives, the ad platform targets consumers that are more likely to purchase. Importantly, we find no evidence that ads are more effective for consumers with higher baseline purchase probability, rendering the ad platform’s bidding suboptimal for the firm. A welfare analysis suggests that the ad platform’s bidding optimization leads to a loss in profit for the firm and an overall decline in welfare. To remedy the incentive misalignment, we propose a solution in which the firm restricts the ad platform to target only consumers that are profitable based on individual consumer-level estimates for baseline purchase probability and ad effectiveness. This paper was accepted by Anandhi Bharadwaj, information systems. Funding: This work was funded by Vereniging Trustfonds Erasmus Universiteit Rotterdam [Grant 97090/17.16/741/oe], Fundacão para a Ciência e a Tecnologia [UID/ECO/00124/2019, UIDB/00124/2020, UIDP/00124/2020, and Social Sciences DataLab - PINFRA/22209/2016], POR Lisboa, and POR Norte [Social Sciences DataLab, PINFRA/22209/2016]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4438 .