Publishers, exchanges and advertisers are preparing for third-party cookie depreciation in several different ways. There’s still no one answer to solve all the problems that ad tech is facing, particularly for targeting and measurement, but the pressure is on, and many are implementing solutions to start testing or merging with other companies to monetize on proprietary data. The good news is that this creates innovation within the ecosystem that hopefully maximizes trust with users' data while maintaining the integrity of personalized ads. Unfortunately, this also makes the customer journey increasingly fragmented.
As the implications become more fragmented, we'll likely see a lot of challenges: less addressability, less reliable measurement (especially multi-touch attribution), acquisitions or partnerships of companies (squeezing out smaller players in the industry) and most importantly, it remains to be seen whether advertisers actually achieve trust with consumers. Many users are seeing more transparency about their data and are able to control who it’s shared with, but if buyers can’t acquire consumer trust, then all of the time and money being poured into privacy will likely be a costly endeavor with little to show for it.
Ultimately, it could be riskier to become siloed and create taller walled gardens, so the best outcome is if tech partners can continue to build partnerships and share capabilities that other platforms can plug into, such as data clean rooms. This would maximize scalability, measurement, accuracy and performance if companies can successfully collaborate with data sets vs. sharing data. This also removes some additional risk that comes with a centralized approach: the potential for data leakage and misuse. There are still pitfalls to this approach, such as whether or not the security of the data is truly clean (ensuring all data is consented to and compliant) and the fact that not all clean rooms are built the same.
A data clean room is a secure way for platforms and advertisers to bring data together without sharing any personal information to target and attribute user behavior. Data clean rooms are governed by encrypted security keys to feed anonymized data between the partners without having to store or share data, allowing for custom-built audience segmentation and accurate reporting and attribution. Examples of data clean rooms include Ads Data Hub, Amazon Marketing Cloud, Snowflake and LiveRamp Safe Haven.
In the meantime, brands and advertisers need to start gathering secure first-party data, testing scale and measurability on first and second-party data and begin creating a blueprint for activating these audiences. You can also check out how Google is leaning into existing customer relationships and the information they agree to share by partnering with InfoSum to leverage PAIR (Publisher Advertiser Identity Reconciliation) in Display & Video 360 (DV360).
Google's PAIR is an encrypted first-party solution to match users from both the advertiser and publisher without sharing PII data.
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