As third-party data sources are being phased out and privacy legislations take effect, marketers need to use identity solutions to stitch multiple sources of data together for a complete picture of their audience.
Here are some of the latest identity solutions available to marketers and what to keep in mind while implementing them.
1. Cohort-based solutions
Overview: Cohort-based solutions use anonymized groups of consumers—grouped based on interests, affinities, online behaviors, personas, etc.—to create targeted ads and messaging. These solutions give marketers the ability to better target consumers without relying on third-party cookies while giving users the ability to control the types of data being shared with advertisers.
The player(s): Google has been experimenting with cohort-based solutions for a few years. The company first tested its Federated Learning of Cohorts (FLoC) in 2021 as an alternative to third-party cookies. Then in early 2022, Google proposed its Topics API to replace it.
What to consider: It seems unlikely that Topics will replace the granular insights that cookies provide. There are also concerns about whether the number of topics will be enough to truly provide relevant targeting. In addition, if users opt out of Topics, advertisers won’t gain any additional insights into the consumer journey.
Topics is relatively new, so kinks need to be worked out. FLoC raised privacy concerns, but Topics has been a move in the right direction, according to industry experts.
2. Universal IDs
Overview: Universal IDs are a privacy-compliant way for advertisers to keep track of consumer behavior across the internet. They use a unique and persistent data signal (like an email address or a phone number) to create an anonymous identifier, which can be encrypted and shared with advertisers, publishers, and ad tech platforms.
The player(s): There’s a variety of universal ID solutions on the market, but a few solutions have come to the forefront, including: The Trade Desk’s Unified ID 2.0, ID5, Secure Web Addressability Network, LiveRamp’s RampID, and Lotame’s Panorama ID.
What to consider: There’s virtually no data lost in the data-syncing process, unlike with cookies. The Trade Desk claims its universal IDs have a near 100% match rate, so the pairing between user interests and ads is highly accurate.
And since partners across the supply chain are working with the same set of data, universal IDs create a more efficient process.
Because consumers must opt in to universal IDs, it will be hard for the solution to reach the ubiquity that cookies had. It also requires publishers, ad tech platforms, and advertisers to agree on one solution, which could be difficult given the number of solutions that exist.
3. Seller-defined audiences
Overview: Introduced by the Interactive Advertising Bureau (IAB) in early 2022, seller-defined audiences (SDAs) is a framework that works with existing media-buying processes and standards to enable publishers to use their first-party data to create audience segments based on a predefined taxonomy.
The player(s): Most publishers have just begun testing SDAs. The IAB is the biggest player in SDAs, but others are entering the field. Many publishers have adopted the IAB’s Taxonomy (which defines more than 1,600 user attributes), but some experts recommend publishers create their own customized taxonomies.
What to consider: Like universal IDs, SDAs provide a standardized method for publishers, advertisers, and ad tech platforms to use to target audiences. It even allows advertisers to use the same audience across multiple publishers, creating a more seamless experience.
Niche publishers may not have enough data to create SDAs, and niche advertisers may struggle if taxonomies don’t cover the audiences they are interested in. Because it’s so new, there’s also been less trial and error among publishers and advertisers, creating a lack of proven results or iteration.
4. Data clean rooms
Overview: Data clean rooms are digital environments where advertisers and their partners can share first-party consumer data securely and in compliance with privacy standards. There are a few types of data clean rooms: those operated by walled gardens, those from independent vendors, and those custom-made by brands and content owners.
The player(s): Pretty much everyone. Walled gardens like Google and Amazon offer data clean rooms, as well as ad tech platforms like LiveRamp, Snowflake, AppsFlyer, and Habu. Even Pinterest and The Walt Disney Co. are getting in on the action.
What to consider: Data clean rooms are a privacy-friendly solution that can help marketers analyze audiences, target ads, and measure performance. They deliver a holistic view of performance across various distribution channels. But many data clean rooms only work for a specific platform, leaving marketers to manually combine and analyze results from different sources.
Because data clean rooms are also very new, there’s a lack of universal standards for adoption or implementation. In February, the IAB took the first step in data clean room standardization by releasing guidance and recommended practices, as well as a framework to support and define interoperable clean room interactions.
Learn more about data sources by watching the free webinar, “The Data Tipping Point: A Panel Discussion.”
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