The future of measurement is here, and it’s built on privacy. Marketers need effective measurement to understand and optimize performance, and ad tech companies need it to fine-tune solutions and show value to their clients.
But with user privacy taking center stage, traditional measurement methods are evolving. Companies across the ads ecosystem are exploring new signals like first-party data and innovative technologies like the Privacy Sandbox Attribution Reporting, Private Aggregation, and Shared Storage APIs.
This blog post shares how the Privacy Sandbox for web empowers ad tech companies to measure advertising effectiveness while safeguarding user privacy. We'll discuss how these technologies work and provide practical steps to unlock their full potential. Get inspired by real-world examples from ad tech companies using these tools to maximize performance, and discover resources to help you keep pace with the latest advancements.
Measuring effectively with fewer cross-site identifiers
Let’s start with some basics on privacy-preserving measurement. The good news is that privacy and effective ad measurement can co-exist. To gain accurate insights and make informed decisions, marketers and ad tech providers need reliable data that reflects overall trends and is not skewed by outlier data. This approach respects user privacy because it doesn't rely on individual-level information.
To make this possible, Privacy Sandbox uses techniques like:
We understand there is no “one size fits all” solution when it comes to measurement, and we’re invested in providing customizable tools that support your unique business needs, without compromising on privacy. Learn more about tailoring these tools in the “If you’re ready to level up” section below.
Understanding and optimizing ad performance
Marketers use attribution reporting to understand ad performance and optimize campaigns. They need to know which ads drive valuable actions, understand which buying strategies are performing best, and measure return on investment. DSPs use this same reporting data to build models that predict which ads are likely to perform well and bid on the best ad placements for their clients.
By leveraging the Attribution Reporting API's flexible reporting options, ad tech companies can fine-tune their bidding models and provide robust reporting and optimization capabilities to marketers—all while safeguarding individual-level information.
If you’re just getting started:
- Check out the Attribution Reporting API’s event-level reports. This reporting mode provides visibility into individual ad events that drive conversions.
- Next, explore the Attribution Reporting API’s summary reports for aggregated performance reporting on conversion counts and values. This reporting mode, which generates reports with Aggregation Service, offers additional flexibility beyond event-level reports to measure more conversion types and information about the conversion.
If you’re ready to level up:
- Tailor reports to the unique reporting needs of each advertiser with flexible event-level configurations. This includes the ability to measure more conversion types and customize the timing and number of reports you receive.
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Explore approaches to increase the signal-to-noise ratio of summary reports
by:
- Understanding how noise works in summary reports and learning about approaches to working with noise.
- Using Noise Lab to explore and assess different noise management strategies.
- Integrate with debug reports to enable testing and troubleshooting for your reports.
- Implement cross-platform attribution to improve reporting accuracy across websites and apps.
- Consider blending data from both event-level and summary reporting modes for a more comprehensive view with increased signal-to-noise ratio and metric visibility. Learn how Google Ads is approaching this, alongside other advanced configuration techniques, in their two-part series ( Part One, Part Two). Review their encouraging initial results here.
Hear more from ad tech companies using these tools today:
