Data Clean Rooms Won’t End the Identity Battle
Since Google’s first date for the end of third-party cookies, the industry has continued to search for viable alternatives. Among the plethora of proposed solutions, data clean rooms have sprung to the top of industry-leading agendas as a potential solution for the eventual depreciation of third-party cookies. And the theory makes some sense – it’s not like their use would require reinventing the wheel.
What’s their appeal? Put simply, a data clean room is a tool that ensures sensitive data is being used in the most efficient and focused form possible and that it is sitting in its own space – independent of a company’s own systems and data pool. The end goal is to be able to work with the data but in a controlled fashion where you don’t have leakage.
Data clean rooms have been around for some time, though their current incarnation and use in the open web differs significantly from the version introduced in 2017 by Google with Ads Data Hub and later with Facebook and Amazon launching their own versions. Here, the walled gardens sought a way to tackle the conflict posed by their rich user-level data and a desire to activate first-party data without leaking large amounts of advertising-irrelevant user data outside their platforms.
While this was most useful for onboarding yet more data into the walled gardens and less so for advertisers offloading insights and data, it posed a powerful step forward and provided a new tool for how one might bridge highly sensitive data in a privacy-limited way. But as consumer trust became tethered to data transparency, privacy regulations ensued, and third-party cookies were put on the chopping block, brands once again found themselves looking for solutions for obtaining and understanding the data necessary to effectively reach their target audiences.
This has given rise to data clean rooms tailored to the open internet – a secure, privacy-centric way to leverage consumer data. But are data clean rooms really the universal solution for the complex world of identity we live in and are they ideally equipped to replace third-party cookies? The short answer is no – at least not on their own, and the long answer is that it’s complicated.
Whether encrypted or not, data clean rooms are still subject to privacy regulations. Although the data is protected with a high level of security, it is not wholly anonymous. As such, the technology must adhere to the requirements of established laws. Law such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Apple’s App Tracking Transparency framework (ATT).
In February, the IAB Tech Lab launched its Data Clean Room (DCR) Standards portfolio to help combat this issue, releasing a repository of guidelines, standards, recommended practices, and specifications. The portfolio is said to create a standard of interoperability between data clean room vendors to allow for secure data collaboration.
Despite the limitations, however, useful and current fields of application for data clean rooms exist. They can play an important role in the transmission of data for analysis and activation preparation or for subsequent retargeting of existing customers. In other words: where sound first-party data strategies exist, data clean rooms can thrive, making these powerful complementary technologies.
An example of this in the market today is retail media networks, which have gained tremendous popularity and traction due to their treasure troves of first-party data. Stories around promising or successful retail media implementations are driving a large portion of the growth in programmatic right now, and this is with good reason. In these collaborations, robust adtech can be paired with data cleanrooms to leverage their first- and third-party data to help brands reach their target audiences.
A prime example of this is the trend where major retailers that range from B&M shops to online retailers are leveraging the power of data clean rooms to make real-time, retargeted display advertising available using their treasure trove of first-party data. But what about a CPG brand whose product is purchased through a retailer? Or a smaller, less technologically advanced brand without a way to consolidate its customer data? What should they do as third-party depreciation looms?
We must remember that data clean rooms are only one part of the equation – acting as an intermediary connecting data that already exists on both sides. In environments where there are no common identifiers, nothing can be connected.
The third-party cookie – as the classic connector between advertisers and publishers – must therefore be bridged in a different way. Ideally, this is done with an end-to-end adtech solution that then uses data clean rooms as a bridge to CRM data. In other setups, the data clean room also serves as a bridge to connect supply and demand.
The key is to use the window between now and when the third-party cookie is fully phased out to build and validate a tailored setup that meets your individual needs. Reevaluate your existing options, question current assumptions, and ensure the pitched solutions are not only practical but that they have the potential to scale sufficiently for your business. There are numerous ways to leverage alternative data sets – via other independent identity solutions or methods like contextual targeting.
Test, learn, and optimize for success.
Like many other innovations within the space, data clean rooms have withstood industry transformations and continue to evolve and adapt. But while they hold great potential, they have significant room for improvement and must certify their role in a post-cookie landscape where growth outside the walled gardens is driving innovation. As such, savvy marketers will be wise to keep an eye on their trajectory while they mature to fit industry needs.
