How data clean rooms might help keep the internet open

Are data cleanrooms the solution to what IAB CEO David Cohen called the “slow train wreck” of accountability? Voices at the IAB will tell you that they have a big role to play.

“The problem with addressability is that once cookies are gone, and with the loss of identifiers, approximately 80% of the addressable market will become unknown audiences, and therefore there is a need for privacy-centric consent and a better consent-value trade-off ,” said Jeffrey Bustos, VP, measurement, addressability and data at the IAB.

“Everybody talks about first-party data, and it’s very valuable,” he explained, “but most non-opt-in publishers have about 3 to 10% of their readership’s first-party data.” First-party data, from the perspective of advertisers who want to reach relevant audiences and publishers who want to offer valuable inventory, just isn’t enough.

Why we care. Who was talking about data cleanrooms two years ago? The surge in interest is recent and significant, according to the IAB. At the very least, DCRs have the potential to keep brands in touch with their audiences on the open internet; to maintain viability for publishers’ inventory; and to provide sophisticated measurement capabilities.

How data can help clean rooms. DCRs are a type of privacy-enhancing technology that enables data owners (including brands and publishers) to share first-party customer data in a privacy-friendly way. Clean rooms are secure spaces where first-party data from a number of sources can be resolved to the same customer’s profile while that profile remains anonymous.

In other words, a DCR is a kind of Switzerland—a space where a truce is called on competition while enriching first-party data without compromising privacy.

“The value of a data cleanroom is that a publisher is able to collaborate with a brand on both their data sources and the brand is able to understand audience behavior,” Bestos said. For example, a brand that sells glasses may not know anything about their customers other than basic transactional data – and that they wear glasses. Matching profiles with a publisher’s behavioral data provides enrichment.

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“If you’re able to understand behavioral context, you can understand what your customers are reading, what they’re interested in, what their hobbies are,” Bustos said. Armed with those insights, a brand has a better idea of ​​what kind of content they want to advertise.

The publisher does need to have a certain level of first-party data for the match to take place, even if it doesn’t have a universal requirement for logins like The New York Times. A publisher may only be able to match a small percentage of the eyeglass seller’s customers, but if they like to read the sports and art sections, it at least gives some direction as to what audience the seller should be targeting.

Dig deeper: Why we care about data cleanrooms

What counts as a good fit? In its “State of Data 2023” report, which focuses almost exclusively on data cleanrooms, concerns are expressed that DCR effectiveness may be threatened by poor match rates. Average match rates hover around 50% (less for some types of DCR).

Bustos is keen to put this into context. “When you match data from a cookie perspective, match rates are usually around 70 percent,” he said, so 50% isn’t terrible, although there’s room for improvement.

One obstacle is a persistent lack of interoperability between identity solutions – although it does exist; LiveRamp’s RampID is interoperable, for example, with The Trade Desk’s UID2.

Nevertheless, Bustos said, “it’s incredibly difficult for publishers. They have a bunch of identity pixels that fire for all these different things. You don’t know which identity provider to use. Definitely a long way to go to make sure there is interoperability.”

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Maintaining an open internet. If DCRs can contribute to solving the addressability problem, they will also contribute to the challenge of keeping the Internet open. Walled gardens like Facebook do have a rich wealth of first-party and behavioral data; brands have access to those audiences, but with very limited visibility into them.

“The reason CTV is a very valuable proposition for advertisers is that you can identify the user 1:1 which is really powerful,” Bustos said. “Your standard news or editorial publisher doesn’t have that. I mean, the New York Times moved there and it was incredibly successful for them.” To compete with the walled gardens and streaming services, publishers need to offer some accountability – and without relying on cookies.

But DCRs are a heavy lift. Data maturity is an important qualification to get the most out of a DCR. The IAB report shows that of the brands evaluating or using DCRs, more than 70% have other data-related technologies such as CDPs and DMPs.

“If you want to have a data cleanroom,” Bustos explained, “there are a lot of other technology solutions that you need to have in place beforehand. You need to make sure you have strong data assets.” He also recommends starting by asking what you want to achieve, not what technology would be nice to have. “The first question is, what do you want to achieve? You may not need a DCR. ‘I want do it’, then see what tools will get you there.”

Also understand that implementation is going to require talent. “It’s a demanding project in terms of the setup,” Bustos said, “and there’s been significant growth in consulting companies and agencies that have helped set up these data cleanrooms. You do need a lot of people, so it’s more efficient to hire outside help for the setup, and then just have an in-house maintenance team.”

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Underutilization of measurement capabilities. One key finding in the IAB’s research is that DCR users exploit the audience matching capabilities far more than they realize the potential for measurement and attribution. “You need very strong data scientists and engineers to build advanced models,” Bustos said.

“A lot of brands that are looking at this are saying, ‘I want to be able to do a predictive analysis of my high lifetime value customers who are going to buy in the next 90 days.’ Or ‘I want to be able to measure which channels are driving the most incremental lift.’ They want to do very complex analyses; but they don’t really have a reason why. What’s the point? Understand your outcome and develop a sequential data strategy.”

Trying to understand the incremental lift of your marketing can take a long time, he warned. “But you can easily do a reach and frequency and overlap analysis.” It will identify wasted investment in channels and suggest as a by-product where incremental lift occurs. “There is a need for companies to know what they want, identify what the outcome is, and then there are steps that will get you there. It will also help to prove ROI.”

Dig deeper: Failure to get the most out of data clean rooms is costing marketers money

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