Addressable TV First Party Data Third Party Cookie

Could seller-defined audiences be an answer to the first-party data conundrum?

By Pete Danks, Vice-president of product

September 15, 2022 | 7 min read

The deprecation of the cookie means the majority of audiences in the future will be non-addressable. Pete Danks, founder of Carbon and vice-president of product at Magnite, says seller-defined audiences could be part of the solution.


As cookies crumble, could seller-defined audiences provide a solution?

Buyers may not be paying much attention to seller-defined audiences right now, with third-party cookies around until at least 2024. But they are interested in the ROI that more quality audiences can give them.

Seller-defined audiences are something to be excited about now as they can enable us to build better audiences for our direct and programmatic clients.

First introduced by the IAB Tech Lab’s Project Rearc, seller-defined audiences can provide a privacy-centric way for publishers to use their first-party data. It’s a key development for a number of reasons, including publishers’ desire for more control over their audiences, standardized systems and more transparency.

Given the rising value of first-party data, it’s easy to see why giving it away so freely has become such a key driver of innovation to restore data control and protect its value. Seller-defined audiences can provide that control and protection, as well as fuel for innovation in audience segmentation and curation to power monetization now and in the future.

To build seller-defined audiences, publishers will need to map signals into the IAB Tech Lab Audience Taxonomy or content taxonomy IDs, which provide buyers and sellers with a common ground to define and communicate those signals.

Standardization of taxonomies is only part of the goal though, as publishers often have unique content and audiences. Seller-defined audience adopters can also choose to use their own taxonomies (when registered with the Tech Lab), providing additional flexibility that can also be utilized to create better-performing audiences in direct and PMPs, for instance.

To support both standardized and publishers’ own taxonomies, we need efficient and effective categorization tools. Automated categorization tools can help as they leverage natural language processing to extract all meaningful content themes, keywords and behaviors from a page visit into a taxonomy to relate those signals to user interests, brands and keywords. This provides more accurate audience signals as well as contextual intelligence that can be used in isolation, or combined and enriched with behavioral insights and custom event signals such as time-on-page, the number of pages visited and custom taxonomy terms to uncover further unique audiences for selling via however publishers choose.

From a publisher perspective, being able to control their audience data and where it’s used is critical to protecting its value; for instance, ensuring data that is valuable for open exchange is not competitive with the direct audience offerings.

However, ensuring transparency in the segments publishers offer is also critical to allow buyers to understand what they’re buying and how they perform. If buyers can see how audiences perform in isolation and in comparison to other segments, it can help grow trust and generate demand for a publisher’s segments in seller-defined audiences or otherwise.

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That’s why the IAB Tech Lab has put several transparency standards in place including the ‘data transparency standard,’ which sets out a minimum disclosure requirement that includes things such as data source, its age and the criteria for segmentation.

By improving segmentation capabilities and the accuracy of cohorts created, publishers can develop even more flexibility in meeting the needs of their buyers and build up trust and transparency through data labels.

Machine learning-driven segmentation techniques build audiences as the signals come in, based on either preset rules or where the artificial intelligence (AI) sees high engagement in specific areas. However, expect further innovation in manual segmentation methods too, with comprehensive audience builders that enable publishers to test and iterate combinations of audience variables.

Every cloud has a silver lining and we should look at the (eventual) deprecation of third-party cookies and loss of other signals as an opportunity to enrich the signals we can rely on now and in the future.

We need to start adopting and innovating many of the principles and processes behind seller-defined audiences. For instance, accurate categorization into flexible and standardized taxonomies, enriching signals and improving segmentation can only help in our ability to showcase audience signals in compliant and future-proofed ways that can continue to drive performance for advertisers.

Publishers are focusing on collecting and enriching valuable first-party behavioral and contextual data to create controllable and compliant audience addressability.

By improving the ways we segment and curate audiences, we can fuel improvements in both the direct and open sides of our ad monetization.

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