Running Secure Audience Overlap Analysis with AWS Clean Rooms: A Guide for Advertisers, Publishers, and Advertising Technology Providers

The Power of Audience Overlap Analysis with AWS Clean Rooms

In the world of digital advertising, collaboration is key. Advertisers, publishers, and advertising technology providers are constantly seeking more efficient ways to work together and generate valuable insights from their collective datasets. One common analysis that drives this collaboration is audience overlap analysis, essential for media planning and partnership evaluation.

What is Audience Overlap Analysis?

Audience overlap analysis is the process of determining the percentage of users in one audience who are also present in another dataset. This analysis helps advertisers compare their first-party data with a publisher’s dataset to evaluate the potential reach and effectiveness of partnering with that publisher. By understanding audience overlap, advertisers can determine whether a partnership will provide unique reach or if there is significant overlap with their existing audience.

Challenges and Current Approaches

Traditionally, sharing data for audience overlap analysis has been done through methods like pixels and SFTP transfers. However, these methods can present significant risks, including data breaches, unauthorized access, and privacy violations. Mishandling of sensitive customer data can result in legal consequences, reputational damage, and loss of consumer trust.

Introducing AWS Clean Rooms

AWS Clean Rooms offers a secure and efficient solution for collaborating on and analyzing datasets without sharing sensitive information. With AWS Clean Rooms, advertisers and publishers can create a data clean room, collaborate on insights, and run audience overlap analyses while mitigating the risks associated with traditional data sharing methods.

How to Run Audience Overlap Analysis with AWS Clean Rooms

Here’s a brief overview of the steps involved in setting up an audience overlap analysis collaboration using AWS Clean Rooms:

1. Create a Collaboration

One party, such as the publisher, initiates the collaboration in the AWS Clean Rooms console and invites the other party, such as the advertiser, using their AWS account ID.

2. Create a Configured Table and Set Analysis Rules

The publisher sets up a configured table from the AWS Glue table, specifying columns, aggregation functions, join controls, and aggregation constraints for the analysis.

3. Associate the Table to the Collaboration

Both parties associate their configured tables to the collaboration, enabling AWS Clean Rooms to run queries securely without sharing underlying data.

4. Run Queries in the Query Editor

The advertiser can now write and execute queries in the query editor, specifying the join key (e.g., hashed email) and desired analysis metrics.

Benefits of Using AWS Clean Rooms for Audience Overlap Analysis

By utilizing AWS Clean Rooms, advertisers and publishers can collaborate on audience overlap analyses securely, efficiently, and compliantly. The platform’s capabilities enable partners to gain valuable insights while protecting sensitive data and maintaining trust with consumers.

Conclusion

Running audience overlap analyses is crucial for optimizing media planning and evaluating potential partnerships in the digital advertising landscape. With AWS Clean Rooms, advertisers, publishers, and other stakeholders can leverage the power of secure data collaboration to drive better business outcomes and strengthen industry partnerships.

For more information on AWS Clean Rooms and how it can benefit your organization, explore the resources provided by Amazon Web Services and start harnessing the power of collaborative data insights today.

About the Authors

Eric Saccullo is a Senior Business Development Manager for AWS Clean Rooms at Amazon Web Services. He is passionate about helping customers enhance their collaboration efforts and drive business success through data insights.

Shamir Tanna is a Senior Technical Product Manager at Amazon Web Services, dedicated to developing innovative solutions for data collaboration and analysis.

Ryan Malecky is a Senior Solutions Architect at Amazon Web Services, specializing in helping customers unlock the full potential of their data through technologies like AWS Clean Rooms.