Data Clean Rooms: A Promising Prospect
In this week’s guest blog post, we have Filippo Gramigna, CEO at Audiencerate diving into the world of data clean rooms. What are they? What are the benefits and drawbacks? Ultimately, are they worth it? Keep reading to find out!
The marketing world has been busy, if not slightly chaotic, following the abjuration of the cookie. Google’s FLoC came and went, only to be replaced by Topics, while Meta teamed up with Mozilla to develop Interoperable Private Attribution (IPA). The Trade Desk, LiveRamp, and others opted for identifiers, and still more solutions are being configured, rejected, and refined at any given time.
In the race for privacy-compliant technologies that offer omnichannel measurement and campaign oversight, one piece of tech is steadily growing in popularity: data clean rooms.
Simply put, a data clean room is a piece of intermediary software that enables two parties to pool their data safely and securely. The process allows companies to connect insights from other parties, creating a more comprehensive dataset.
Anonymisation is configured into the design: any personally identifiable information, such as email addresses, is encrypted, and access is only granted to those involved in a partnership. This brings us to the next point: there are different kinds of data clean rooms, which plays a significant role when it comes to the kind of data and insights available.
Walled gardens run media clean rooms. Think Google Ads Data Hub, Amazon Marketing Cloud, and Meta Business Suite. In this scenario, each platform has a full view of its own data but only provides hashed and aggregated insights to partnered companies. Marketers then match this information with their own first-party data, using the inconsistencies to flag inefficient ad spend or targeting, for example. While undoubtedly valuable, these insights do come at a price; brands can only evaluate campaign performance within the rules and parameters set out by each platform, with no possibility for competitive oversight and comparisons.
Another option is a partner data clean room, where two parties – a publisher and an advertiser, for example – can share their data, each gleaning insights from a more complete understanding of customer journeys. Both parties have full control over the amount and type of data shared and benefit from a secure, closed environment.
Data clean rooms are a secure, privacy-friendly way to help brands get a clearer and more detailed picture of their media performance, with insights that can help determine reach, frequency, and attribution metrics. This means brands can evaluate their ad spend, optimise strategies, and maintain value-driven campaigns.
As data clean rooms rely on partnerships, this technology may also help pave the way for a more inclusive, cooperative, and equal marketing environment. For example, brands that have less access to consumer or transactional data, such as some consumer goods brands, can collaborate with retailers to gauge campaign performance, while simultaneously ensuring retailers can offer popular products to their customers.
Meanwhile, alliances between publishers and advertisers can strengthen their overall position in the market, bolstering data independence from tech giants and moving away from outsourcing revenue and audience monetisation. While this may impact scalability to a certain extent, rich and high-quality audiences will still be available, with quality outweighing quantity. Many clean rooms, therefore, help brands segment and target audiences, even without cookies.
Loyal customers and high-quality audience data will further encourage advertisers to reach out for partnerships, increasing the potential of valuable alliances in the future. This idea of increased cooperation and interoperability extends to the tech, with data clean rooms complementing other existing technologies such as customer data platforms (CDPs), improving data sharing and optimising data strategies and asset activation.
As with any emerging technology, there remain some kinks to iron out. Some of these are purely technical, such as the issue of formatting: without universal standardisation, parties can find themselves with two incompatible data sets that might cause some hiccups when it comes to matching them.
Others are more social: the culture of cooperation is not quite here yet, with many still wary of sharing data due to privacy concerns, data breaches, and anything that might risk their reputation. As data clean rooms rely on first-party data, data-rich companies – direct-to-consumer brands and other major players – will have a considerable marketing advantage until the environment truly adapts to the technology.
Finally, a fully integrated, omnichannel understanding of a brand’s performance is not yet possible. With no option to pull data from different platforms, brands can only achieve a siloed view of their activities, which becomes both confusing and pricey as investing in multiple data clean rooms can rack up a fair cost. Some companies have already begun to tackle this particular issue, yet it remains to be seen to what extent walled gardens are willing to cede data in a privacy-first landscape.
Are data clean rooms worth it? The immediate answer is yes: they provide more insights, allowing brands to evaluate and adjust campaigns, targeting, and ad spend in a way that is safe and privacy compliant, while simultaneously complementing pre-existing tech. As we move closer to a first-party data future, combining the use of data clean rooms with CDPs can create a package of tools that will allow data orchestration and trading, also in a privacy-safe manner. Since some fine-tuning remains when it comes to standardisation and omnichannel attribution, companies should still see how and where they can maximise their first-party data, establish valuable partnerships, and experiment, explore, and collaborate.