Background
Ad-tech providers have historically used third-party cookies for conversion measurement, and for attributing conversions to ad interactions. Conversion measurement is critical for evaluating the performance of ad campaigns and automated bidding strategies. Now, with technology changes and privacy regulations on the rise, traditional ad-measurement systems must change in order to remain effective while protecting user privacy.
Chrome’s Attribution Reporting API (ARA), part of the Privacy Sandbox initiative, offers a new path to conversion measurement after Chrome’s planned third-party cookie deprecation in the second half of 2024 , subject to addressing any remaining competition concerns of the UK’s Competition and Markets Authority (CMA). Google's ads teams plan to use the ARA for measurement, including on Google-owned inventory such as Search and YouTube, as well on third-party inventory available via our advertising technology products. We have made significant investments in learning to use the ARA more effectively for both, to help advertisers achieve more accurate measurement.
In a previous post , we provided a high-level overview of the approach Google’s ads teams are taking to effectively blend the ARA event-level and aggregate summary reports to maximize accuracy. A key point is that your configuration determines what data you query, and how you query it. It’s crucial for ad-tech providers to effectively configure the ARA for their use cases. Google’s ads teams have found that configuring specific ARA settings can lead to notable accuracy improvements. We encourage other ad-tech providers to integrate with the ARA to retrieve the conversion data they need, and process the ARA's output to help maintain accurate measurement in a post-third-party-cookie world.
The ARA is flexible to support various use cases. Google’s ads teams use this flexibility to configure unique ARA settings for each advertiser. This way, ARA-based measurement adapts to each advertiser’s specific needs. For example, we’ve noticed that when advertisers differ in conversion volume, it’s better to have advertiser-specific configurations related to the granularity of aggregation keys and the maximum observable conversions per ad interaction.
Google’s ads teams’ approach
Here's how Google's ads teams use the ARA to ensure the raw data we receive is as useful as possible for downstream blending. We configure ARA settings as explicit mathematical optimizations by defining objective functions to represent data quality, then choosing settings to optimize those functions. Ad-tech providers can choose their own approach. Google’s ads teams plan to continue sharing insights we learn from our own optimizations with the ad-tech community.
Please see our detailed technical explainer for more information about our approach to ARA configuration.
