Website for the tutorial: Online Advertising Incrementality Testing: Practical Lessons, Paid Search and Emerging Challenges. Presented at ECIR2022 https://ecir2022.org/tutorials/
View the Project on GitHub joel-barajas/ecir2022-incrementality-testing
Presented at the ECIR 2022 in person!!
Online advertising has historically been approached as an ad-to-user matching problem within sophisticated optimization algorithms. As the research and ad-tech industries have progressed, advertisers have increasingly emphasized the causal effect estimation of their ads (incrementality) using controlled experiments (A/B testing).
With low lift effects and sparse conversion, the development of incrementality testing platforms at scale suggests tremendous engineering challenges in measurement precision. Similarly, the correct interpretation of results addressing a business goal requires significant data science and experimentation research expertise.
We propose a practical tutorial in the incrementality testing landscape, including:
We provide first-hand lessons based on the development of such a platform in a major combined DSP and ad network, and after running several tests for up to two months each over recent years.
We elaborate more on the user privacy implications in online experimentation and incrementality testing. We aim to motivate the research community to focus on solutions under these emerging constraints.
Some of the papers cited in the slides are avaiable at the folder papers
The tutorial conference submission, which includes the detailed outline of the tutorial, is available at the conference proceedings here. We keep a local copy in this page repository here.
Feedback welcome!!