The Topics API is designed to work seamlessly with other ad tech signals, providing a flexible and adaptable solution for various advertising needs. You can use Topics alongside other private, durable signals such as first party and contextual data as features in machine learning models, in order to infer audience segments for users.
How will Topics be bundled into solutions ad techs are providing?
This is an area that is still under development. Ad tech companies are actively exploring ways to integrate Topics into their offerings, and we expect to see a variety of innovative solutions emerge in the near future.
In the meantime, for more information, refer to the developer documentation and the Google Blog post detailing the results from Google Ads’ interest-based advertising testing.
How can Topics and Protected Audience be used together?
Topics and the Protected Audience API can be powerful tools when used together. Protected Audience allows for the creation of custom audiences based on specific criteria, while Topics provides insights into broader user interests.
- Enriching bidding data: The signals provided by Topics can be used to enrich the data available during the bidding and ad selection process in Protected Audience auctions. This can lead to more relevant ads and better campaign performance.
- Increasing inventory value: By combining Topics with Protected Audience, publishers can increase the value of their ad inventory by offering advertisers more targeted and effective advertising options.
How should publishers, advertisers, and ad techs think about using Topics?
- Publishers: Consider how Topics can complement your existing advertising strategies and enhance the value of your inventory. Work with your ad tech partners to understand how they are integrating Topics into their solutions.
- Advertisers: Explore how Topics can help you reach relevant audiences and improve the performance of your campaigns. Consider combining Topics with other signals, such as Protected Audience, first-party, and contextual data in machine learning models to infer audience segments for users and create more effective advertising strategies.
- Ad tech providers: Develop innovative solutions that leverage Topics to meet the evolving needs of publishers and advertisers. Focus on creating privacy-preserving solutions that enhance user trust and contribute to a sustainable online advertising ecosystem.