Johannes Hermle, Giorgio Martini
arXiv 1 Jul 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2207.00206 · PDF · DOI · OpenAlex · Extracted main text
Ad platforms require reliable measurement of advertising returns: what increase in performance (such as clicks or conversions) can an advertiser expect in return for additional budget on the platform? Even from the perspective of the platform, accurately measuring advertising returns is hard. Selection and omitted variable biases make estimates from observational methods unreliable, and straightforward experimentation is often costly or infeasible. We introduce Asymmetric Budget Split, a novel methodology for valid measurement of ad returns from the perspective of the platform. Asymmetric budget split creates small asymmetries in ad budget allocation across comparable partitions of the platform's userbase. By observing performance of the same ad at different budget levels while holding all other factors constant, the platform can obtain a valid measure of ad returns. The methodology is unobtrusive and cost-effective in that it does not require holdout groups or sacrifices in ad or marketplace performance. We discuss a successful deployment of asymmetric budget split to LinkedIn's Jobs Marketplace, an ad marketplace where it is used to measure returns from promotion budgets in terms of incremental job applicants. We outline operational considerations for practitioners and discuss further use cases such as budget-aware performance forecasting.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Min Liu, Jialiang Mao, and Kang Kang (2020) Trustworthy online marketplace experimentation with budget-split design | 1.000 | 7 | 3 | 100% |
| 2 | Randall A Lewis and Justin M Rao (2015) The unfavorable economics of measuring the returns to advertising | 0.737 | 3 | 2 | 100% |
| 3 | Brett R Gordon, Kinshuk Jerath, Zsolt Katona, Sridhar Narayanan, Jiw… (2021) Inefficiencies in digital advertising markets | 0.511 | 2 | 1 | 100% |
| 4 | Deepak Agarwal, Souvik Ghosh, Kai Wei, and Siyu You (2014) Budget pacing for targeted online advertisements at linkedin. In Proceedings of the 20th ACM SIGKDD international conference on… | 0.405 | 1 | 1 | 100% |
| 5 | Guillaume W Basse, Hossein Azari Soufiani, and Diane Lambert (2016) Randomization and the pernicious effects of limited budgets on auction experiments. In Artificial Intelligence and Statistics. P… | 0.405 | 1 | 1 | 100% |
| 6 | Iavor Bojinov, David Simchi-Levi, and Jinglong Zhao (2020) Design and Analysis of Switchback Experiments | 0.405 | 1 | 1 | 100% |
| 7 | Alex Deng and Xiaolin Shi (2016) Data-driven metric development for online controlled experiments: Seven lessons learned. In Proceedings of the 22nd ACM SIGKDD I… | 0.405 | 1 | 1 | 100% |
| 8 | David S Evans (2009) The online advertising industry: Economics, evolution, and privacy | 0.405 | 1 | 1 | 100% |
| 9 | Ayman Farahat and Michael C Bailey (2012) How effective is targeted advertising?. In Proceedings of the 21st international conference on World Wide Web. 111–120 | 0.405 | 1 | 1 | 100% |
| 10 | Avi Goldfarb and Catherine Tucker (2011) Online display advertising: Targeting and obtrusiveness | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 29 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Multi-cell experiments for marginal treatment effect estimation of digital ads | 0.405 | 1 | 1 |