The disappearance of third-party cookies and identifiers is reshaping digital advertising as we have come to know it. Add regulatory requirements related to personal data processing into the mix with evolving user attitudes toward advertising targeting and we have a challenge on our hands. Thus, publishers and advertisers must rapidly transform to continue to understand audiences, personalize content and ads, maintain the monetization, and preserve and measure the profitability of their investments. Luckily, there are already solutions to help meet these challenges.

The first possible solution relies on taking full advantage of first-party data. However, in the context of collaborative use cases, concerns related to security, privacy, and confidentiality beyond the technical limitations linked to their sharing remain. So, how can an advertiser enrich, share, or activate its data on external sites without risk? How can a publisher make its data available to an advertiser for audience extension outside its proprietary environments? Data clean rooms provide one possible solution.

What are data clean rooms?

Data clean rooms are platforms that allow advertisers and publishers to compare their proprietary data without one party having direct access to the other’s data. More concretely, the different stakeholders have access to a secure, virtual environment in which their data is matched with others’ data. Even if none of the participants has direct access to the data, the results of these “matches” allow them to enrich their understanding of their audiences, refine their segmentation and activate this data — safely and on a large scale. Without cookies and complex integration into the existing technology stack, this is done automatically.

It is important to note that each player remains in control of their data and can change their settings. Participants specify who can access the virtual room, which data is accessible and activatable, and over what period.

Targeting and activation capabilities of walled gardens on the open web

Data clean rooms are not new. They have already contributed to the success of online platforms operating in walled gardens. Web giants like social media networks who have amassed massive repositories of first-party data with the aim of giving advertisers a way to reach users behind their wall. 

As our industry enters a privacy-centric era, driven by more data regulation and advances in technology, we’re quickly seeing interest in data clean room capabilities spread across the open web. 

For content and service publishers, this technology represents an opportunity to offer data similar to walled gardens, increase the value of their inventories, and win back market share. In addition, advertisers can benefit from the same targeting (precision and volume) and activation (retargeting and prospecting) capabilities offered by the major platforms in premium and brand-safe environments.

Better data activation

A collaborative data strategy – one that is based on first-party data – allows advertisers to manage their advertising investments in a secure, privacy-conscious way and ultimately achieve  better results. To optimize their budgets and increase the performance of campaigns, advertisers need to understand their audiences in detail while respecting the confidentiality of personal data. To do this, the use of their proprietary data is an essential first step. Second, advertisers and publishers must seek to  enrich this data with similar and/or complementary audiences. For example, to communicate with car enthusiasts, a car manufacturer is interested in combining its own data with a publisher whose car section attracts a large audience or specializes in topics related to bank loans.

The most common uses of data clean rooms are to retarget users and increase the reach of a campaign. In the first case, the advertiser’s data is compared with a publisher’s to retarget everyday users. Machine learning and predictive analytics technologies built into the data clean room model the two parties’ data using a “lookalike” or “statistical twin” method. This method creates new prospecting opportunities as the advertiser can find new audiences with similar behavior to existing targets.

The use of data clean rooms is a win-win approach for all stakeholders. However, data clean rooms require a sufficient volume of data and an independent and reliable technological partner. 

The bottom line is this: the digital advertising industry is being reshaped and rebuilt. It’s a new world where no one approach will suffice. Only a portfolio approach that is made up of multiple solutions – clean room data matching, first-party data, persistent IDs, contextual targeting, etc. – will ensure success without third-party cookies.