Extensions - Expanding the possible

The three primary use cases are:

1. To define audiences from your standard data sources that are so complex that they can’t be done via the platform. This may be the case if your data scientist has worked on intricate audience definitions for you, giving you an easy route to deploy their work.
2. Using extensions to provide cross-project data matching “Data Clean Room” matches. In this case, Thirty-One Circles IDs are compared between projects (with written permission from both project admins). Our Data Clean room matching allows for “comparison only” giving a shared user pool. No data is ever transferred from one project to the other.
3. Building audiences based on external data such as offline sales or other data sources you have access to. The main requirement is that the data in question must be stored in Google Big Query.