The Rundown: The hope, hype and reality of data clean rooms

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Given ongoing data privacy concerns, it may feel like everyone in advertising believes in using a data clean room these days. But, in actuality, they’re not that popular.

In fact, more than half (53%) of the 266 marketing professionals surveyed by data clean room firm Habu said they have never used one.

Simply put, data clean rooms are a long way off mass adoption despite how ubiquitous they seem to be in the marketing lexicon now — a point that should surprise no one. Most marketers wouldn’t know what to do with a data clean room if they had one.

“From a clean room perspective, two things we know to be true are that privacy and decentralization of the data that fuels our industry are not going away any time soon,” said Tim Norris-Wiles, managing director of international at Habu. “That being said, most of digital advertising lacks clarity right now, as opposed to any one individual technology or subcategory. There’s a lot up in the air, making it challenging for brands to know where to invest their time, money and strategy for fear of the goalposts moving.”

So marketers are really no better off now than they were before clean rooms came around?

Yeah, the whole situation feels a bit like an oxymoron. Data clean rooms were meant to bring some clarity to what was becoming a very opaque way of targeting and measuring ads in place of blunt third-party addressability, and yet providing clarity is the last thing these technologies have done. The market is awash with solutions preaching different takes on the same goal: scaling the use of identity in a privacy-compliant way. Sorting the wheat from the chaff here is a business planning and technical challenge. That would be hard enough without the fact that each advertiser, publisher and software provider defines identity and audiences slightly differently. And that’s just for the marketers that have determined how to build addressable audiences beyond the rudimentary. There are many others, however, who are not even remotely ready to be given the keys to this sort of technology. 

“The reality is most marketers, if we’re being honest, have never actually had to define these audiences on their own, much less track performance etc. across a media lifecycle,” said Kevin Bauer, data and identity strategy lead at Prohaska Consulting. “Historically they just did whatever the walled garden (i.e Facebook etc.) told them to do, without really taking the time to understand it deeply.”

In many ways, the real issue is how marketers own those data clean room relationships as opposed to being the silent partner

Up until now, marketers haven’t had to really think about data clean rooms. They’ve had to use them, of course. Anyone who has spent money on Facebook, Amazon or Google over the years will have done so. But in those instances, those companies have wielded all the control over what happened in those environments. Marketers had to just go with the flow. These days, they can’t be so passive. They have to own at least half of those relationships. Granted, that’s not necessarily with the platforms. It’s more with the scores of third-party solutions that have emerged over the years. Nevertheless, this is a sea-change in how to plan, forecast, evaluate and operationalize decision making, said Bauer. Look at the changes Walgreens-owned retail pharmacy chain Boots had to go through to use a data clean room for proof. Its marketing team was brought a lot closer to its data team as a result.

“The clarity of a clean room solution is directly proportional to the clarity and maturity of building addressable audiences beyond ‘all humans’ or ‘People who eat cheese’,” continued Bauer. “Until brands gain maturity in determining their own data and identity taxonomy, it will be harder to understand how to leverage third-party software solutions in concert with other business partners to make use of these identities.”

This sounds like a slog. What steps does it take to get to a point where you realistically argue the case for a data clean room?

For starters, a marketer needs a clear identity taxonomy for their organization that helps them sort and make sense of various device, account, or household level data sets into something that can be leveraged as a data product across systems and processes. While some companies will have had to have done this as a matter of course — think those in banking, telcos and energy — there are others that won’t know where to start; their media investment and operational decisions are made on a channel and or at partner level as the common denominator, not on individuals or audiences. Not to mention the small matter of the eye-watering costs of these technologies. To say nothing of the need to audit data clean room services across their existing tech stack before making any big investments in a new solution.

There are a lot of solutions now in the market for advertisers, all with nuances and individual characteristics from functionality to integrations into walled gardens. Which is to say finding the right solution is a long process. Even the first step of listing and prioritizing the benefits a solution can enable for marketers takes time as the space is developing so fast and every month there is seemingly new capability being released or major issues fixed, said Dan Larden, head of U.K. at digital media consultancy TPA. “On top of that, as it’s first-party data, navigating multiple stakeholders in legal, technology and IT that need to lean into any process adds more complexity,” he continued.

Are data clean rooms worth all this hassle? 

It depends on who you ask. For some marketers, data clean rooms are invaluable — or at least they think they will be soon. To them, there are few better ways to futureproof an advertising strategy. That’s because the use cases go beyond just showing people an ad in ways that don’t attract the ire of privacy regulators. Then there are those marketers who aren’t so quick on the take up. And who can blame them? It’s so hard to know whether they have a challenge that only a clean room can solve. And even if they do, finding the right data clean room solution isn’t easy. No surprise that so many marketers feel the juice isn’t worth the squeeze.

“It’s a slow process because aside from a genuine use case for a data clean room, advertisers need to remember that they have to enter into an agreement with another data owner, which is often a long, protracted process,” said Charlie Hawker, global data director at Wavemaker. “Some of these big brands have a long procurement process. A data clean room isn’t something that gets typically signed via an agency. That’s normally done via the client.”

Will data clean rooms ever take off? 

Chances are they will. It may take a while, though. There are many marketers who are still figuring out how to wrangle their data, for a start. This will change and when it does more marketers are likely to take these solutions more seriously. As Vihan Sharma, md of Europe at ad tech vendor LiveRamp explained: “This perception of data clean rooms being cumbersome means there has been reluctance by some to get involved, but this is an outdated misconception.”

The real issue is that — at least for now — the technologies deliver small but not necessarily scalable benefits. Indeed, use cases are either scant or quite niche. That’s despite the technologies being capable of so much more, from delivering analytics and insights to marketing attribution and improving machine learning. Data clean rooms are a lot more than putting ads on a site. That should stand them in good stead as more marketers come to terms with the potential (or lack thereof) of their own data.

“That said, data clean rooms are only one part of the data infrastructure needed to support retail media,” said Sharma. ”As a result, it’s important that they are tightly integrated into business user-accessible capabilities including, insight and planning tools, data management, as well as activation and measurement, rather than pure-play data clean rooms that are more focused on data science and analytical use cases.”

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