Explainer: What you need to know about data clean rooms

Who is using DCRs right now

As noted, the earliest examples of clean rooms in action in the marketing and advertising ecosystem are those by the walled garden providers. Google’s Ads Data Hub is often quoted as the first example in this context, followed by Meta Business Suite and Amazon Marketing Cloud.

Snowflake’s US customers include NBC Universal, Roku and Disney for its advertising sales solution across Disney media, properties and outlets. In the latter’s case, segmented data from its owned data sets is split into 1000 different user segments. Advertisers come in and use those for targeting specific audiences, as well as for performance analytics. Disney has also done its own smart modelling.

In Australia, Snowflake also cites several brands and media outlets as customers. “We've certainly seen media companies trying to aggregate different data sets to be able to better position those audiences from an advertising point of view,” says Snowflake sales engineering A/NZ director, Clive Astbury.

“That was going on separately to any cookie conversation. We’ve also seen quite a few different second-party data partnerships occurring. Generally, when I've heard about those, they've come out of a martech player’s stack, so they will have customers using the same technology. And they've been able to connect those two customers together and have that second-party data sharing capability.”

For Jaanimagi, the hope is the evolution of clean rooms allows everyone to start to make effective yet responsible first-party data sharing practices much easier, more efficient, and less risky. “The major private ecosystem platforms have been running these types of solutions for a while now. But the real excitement is in how different partnerships are now launching and evolving across the open Internet and among a range of second-party partners.”

As well as being involved with Disney, partners and clients of InfoSum include Omnicom, Experian, Foursquare and CNN. Retail networks are another growing example of data clean rooms being put into action. LiveRamp has worked with retailers such as Boots and Carrefour to build such solutions.

“Clean rooms should be used by any business that wants to create better experiences for its customers, which is a lot of businesses. Our clean room customers are far-ranging as well,” Loptman says. “We work with some of the biggest retailers in the world to power programs that drive immense value for them.

“We work also with everyone from major Australian publishers to, most recently, a major European utilities organisation that wanted to enhance its customers’ experiences.” 

Common business use cases

As the list of organisations signing up to use DCRs expands, so do the many use cases. One of the most common in a marketing and advertising context is first-party data onboarding, allowing brands, media owners and data providers to undertake matching across their respective data sets. Identity resolution and leveraging multiple identity graph partner is a second example.  

Another use case Pinto points to segmentation and enrichment, to more easily connect, match and enrich first-party data with direct access and without reliance on third-party cookies or aggregated data. “This is about quickly identify the best performing consumer attributes across existing core customers and using that intelligence to build powerful audiences without sharing your data with a third party,” he says.  

Then there’s the popular use case of data activation for media planning. “Both brands and media owners can future-proof their advertising performance with first-party data matching and deliver relevant high performing experiences with little to no media waste,” Pinto says.  

“The fourth one worth mentioning is measurement and optimisation. Marketers can quickly understand and measure the effectiveness of their campaigns, audiences, and sales performance through direct collaboration.”

Hoptman cites retailers and CPG brands leveraging clean rooms to collaborate on customer and transaction data. “Retailers can leverage insights on what customers are buying, how much they are buying, and how often, and share these insights with CPG brands,” she says. “Brands achieve better targeting, reach the right audiences, and reduce their advertising waste. Retailers improve yields and deliver better customer experiences.”

A more interesting use case is between partners that may not have obvious audience overlap or association. “Partnering for data collaboration offers a chance to unlock new insights about brands’ mutual customers that they may not have otherwise discovered. For example, an automotive brand could partner with someone like a big box retailer to uncover audience overlap,” Hoptman says.

“This would then help each partner to reach their customers at different points of the customer journey, and they could curate new messaging to better reach these customers.”

Versent has transport clients wanting to be able to share data with each other to get an end-to-end view of how a particular trip is occurring.

“All of the same principles apply in that space as well, because no one wants to give away that data, necessarily, they kind of want to share it in a managed way,” Hanus says. “That means with the appropriate controls to meet privacy and data sharing obligations they have and which they’ve essentially committed to the customer around.

“We have one customer at the moment, a boutique consultancy, looking at reputation management and to analyse those sorts of reputational risks by diving into their email and groupware systems. They need to establish that data integration point to be able to enable that to happen securely. We've combined products we work with, with a kind of set of proprietary technologies to achieve that.

“We also see this within the land titles industry: A lot of land title services are looking to figure out how to commercialise their data. No one wants to build yet another integration, people want to test it and learn iteratively. Having that data sharing capability where they can essentially mix those datasets, without giving away the asset is critical.”

Key features of a data clean room

In understanding data clean room solutions and use cases, Pinto advises companies to consider scale, speed, privacy, transparency, simplicity and control as evaluation criteria. For example, privacy is the core responsibility of data collaboration and data clean room solutions. There are multiple privacy-enhancing methodologies and technologies (PETs) that can be applied to ensure complete obfuscation and anonymisation of data.

“Control is also a crucial factor. The challenge with modern data sharing solutions is one party’s data needs to leave their control and ownership to perform even the most basic use cases such as data onboarding, activation, and measurement,” Pinto says. “Data clean rooms must eliminate the need for any data to move - even enabling all parties to maintain complete control and holistic governance with granular permissions and access controls that allow each party to dictate how their data is matched, analysed and activated by each partner.”

This is why Hoptman is advising anyone looking at DCR offerings to consider partners they’re collaborating with as a key element in decision making.

“With the advent of a slew of clean room solutions, not only should companies look for clean rooms being used by their partners today, but also interoperable solutions that will allow their data to safely and securely be leveraged with more partners in the future,” Hoptman says. “Furthermore, a preferred clean room should allow companies to get to the level of detail - transaction data, or whatever it may be - to enable them to derive win-win insights from their shared data.

Knowing if you need a DCR

So given all this, do you need to build your own DCR? The jury is out, but most experts say this is one all marketers need to watch.

“Every business that cares about their customers should and will invest in the future and in the protection of their data - and data clean rooms are part of that future,” says Pinto. “The first question they should ask themselves is whether or not they have first-party data, or want to access first-party data. If that answer is yes, then a data clean room is the right solution for them.”

For Jaanimagi, DCRs are much more than a replacement, they are a large leap forward, particularly in the context of consumer privacy, consent management and marketing efficiency and effectiveness.

“Post third-party cookies deprecation, there are three general future-proof approaches emerging for marketers, developed through the pan-industry collaborative efforts over the past 2-3 years,” he says. “For the future of both linked and unlinked first-party data, clean rooms are a significant product evolution, and we expect these privacy-by-design types of solutions to play large roles for companies that have enough scale and quality in their data, and the resources and intent to leverage them fully and completely.” 

Henao Brand believes anyone with enough of a digital footprint to activate consumers’ information is a prospective buyer of a DCR solution.

“We are going to see private data clean rooms for big conglomerates, like media, like retail, and we’ll continue to see that explosion of fragmented sort of marketplaces, which are going to make it more and more difficult for marketers for brands, in my opinion, to measure the success of, of their campaigns,” he comments. “Because if you are able to exploit some data through the Channel Nine marketplace, that doesn't mean you're going to be able to link those results to whatever you do on Infosum. So this is an interesting period for the adtech industry where the regulation, the need for monetisation, and the changes that Google is imposing on the in the industry.”

Customers are already using one or more tools for the purposes of sharing data and action, depending on the dataset, says Astbury.

“This will either become another option or the way they do it. We don't know yet and we’ll see how that pans out,” he adds. “The nice thing for Snowflake as well, we're not restricted to just say, two datasets. Because of this data sharing capability, we can have multi-party clean rooms. So three, four or five companies get together and share data. We will see where that goes and what kinds of additional use cases that brings.”