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Are data cleanrooms the solution to what IAB CEO David Cohen called the “slow-moving train wreck” of addressability? The voices of 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, about 80% of the addressable market will become an unknown audience, which is why privacy-based consent and a better consent value exchange are needed,” 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 publishers who don’t have access, they have about 3 to 10% of their readers’ 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, is simply not enough.

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

How data cleaning rooms can help. DCRs are a privacy-enhancing technology that allows data owners (including brands and publishers) to share first-party customer data in a privacy-compliant manner. Cleanrooms are secure spaces where first-party data from multiple sources can be resolved into the same customer profile, while that profile remains anonymous.

In other words, DCR is a kind of Switzerland. an area where a truce is declared in competition while first-party data is enriched without compromising privacy.

“The value of a data cleanroom is that the publisher can partner with the brand through both their data sources and the brand can understand audience behavior,” Bestos said. For example, a brand that sells eyeglasses may know nothing about its customers other than basic transactional data and that they wear eyeglasses. Matching profiles to publisher behavioral data provides enrichment.

“If you’re able to understand behavioral context, you’re able to 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 type of content they want to advertise against.

A publisher must have some level of first-party data for compliance to occur, even if it doesn’t have a universal access requirement like The New York Times. A publisher may only be relevant to a small percentage of an eyeglass retailer’s customers, but if they like to read the sports and arts sections, at least that gives some direction as to which audience the retailer should be targeting.

Dig deeper. Why do we care about data cleanrooms?

What is considered a good match? In its State of Data 2023 report, which focuses almost exclusively on data cleanrooms, it expressed concern that DCR effectiveness could be compromised by poor match rates. Average match rates hover around 50% (less for some types of DCR).

Bustos wants to put that into context. “When you match data from a cookie perspective, match rates are typically around 70 percent,” he said, so 50 percent isn’t terrible, though there’s room for improvement.

One of the obstacles is the persistent lack of interoperability between identity solutions, although it exists; LiveRamp’s RampID is interoperable with, for example, The Trade Desk’s UID2.

Still, says Bustos, “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. There’s definitely a long way to go to make sure there’s interoperability.”

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

“The reason CTV is a really valuable proposition for advertisers is because you’re able to 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 into it, and it’s been incredibly successful for them.” To compete with walled gardens and streaming services, publishers need to offer some degree of reach without relying on cookies.

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

“If you want a clean data room,” 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 to do this,” then see what tools can get you there.”

Also understand that implementation will require talent. “It’s a demanding project in terms of setup,” Bustos said, “and there’s been a significant increase in consulting firms and agencies helping to create data cleanrooms. You really need a lot of people, so it’s more efficient to hire outside help to organize then have an in-house staff.”

Underutilization of measurement capabilities. One of the key findings of the IAB research is that DCR users are using audience matching capabilities more than realizing the potential of measurement and attribution. “You need very strong data scientists and engineers to build advanced models,” Bustos said.

“A lot of brands that are looking into this are saying, “I want to do predictive analytics on 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 have the most incremental lift.” They want to do very complex analyses. but they don’t really have a reason why. What is the meaning? Understand your output and develop a sequential data strategy.”

Trying to figure out how to scale your marketing can take a long time, he warned. “But you can easily do reach and frequency and match analysis.” This will identify wasted investment in channels and as a by-product suggest where incremental lift is occurring. “It’s imperative that companies know what they want, figure out what the outcome is, and then there are steps that will get you there. It will also help prove ROI.”

Dig deeper. Not getting the most out of data cleanrooms costs marketers money

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