How data cleanrooms can help keep the internet open

Are data clean rooms the solution to what IAB CEO David Cohen calls the “slow-moving train wreck” of targeting? The voices at the IAB will tell you they have a big role to play.

“The issue with addressability is that once cookies are gone and identifiers are lost, about 80% of the addressable market will become an unknown audience, so there is a need for privacy-centric consent and a better consent-value trade-off,” Jeffrey Bustos, IAB’s said vice president of measurement, addressability and data.

“Everybody talks about first-party data, and it’s very valuable,” he explained, “but most publishers who don’t have access have about 3-10% of their readers’ first-party data.” For advertisers who want to reach relevant audiences and publishers who want to offer valuable inventory, first-party data is not enough.

Why do we care? Who was talking about data clean rooms two years ago? According to the IAB, the increase in interest is recent and significant. DCRs, at the very least, have the potential to connect brands with their audiences on the open web; maintain viability for publishers’ inventories; and provide sophisticated measurement capabilities.

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

In other words, DCR is a sort of Switzerland—a place where a competitive truce is called while first-party data is enriched without compromising privacy.

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

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

Even if it doesn’t have a universal requirement for access like The New York Times, a publisher must have some level of first-party information for compliance to occur. Publishing may only suit a small portion of an eyeglass retailer’s customers, but if they like to read the sports and arts sections, it at least gives the retailer some guidance as to what audience they should be targeting.

Dig deeper: Why we care about data cleanrooms

What constitutes a good match? The State of Data 2023 report, which focuses almost exclusively on data cleanrooms, expresses concern that DCR’s effectiveness may be threatened by poor compliance rates. Average compliance rates hover around 50% (less for some DCR types).

Bustos wants to put it in context. “From a cookie perspective, when you match data, the match rates are usually around 70 percent,” he said, so 50 percent isn’t scary, although there’s room for improvement.

One obstacle is the lack of continuous interaction between identity solutions – although it exists; LiveRamp’s RampID works with, for example, Trade Desk’s UID2.

Even so, Bustos said, “it’s incredibly difficult for publishers. They have a bunch of personality pixels firing for all these different things. You don’t know which authentication provider to use. There’s definitely a long way to go to make sure there’s interoperability.”

Maintaining an open internet. If DCRs can contribute to solving the addressability problem, they will also contribute to the problem of keeping the Internet open. Walled sites like Facebook are rich in first-party and behavioral data; brands can reach those audiences, but with very limited visibility to them.

“The reason CTV is a really valuable proposition for advertisers is because you can identify a really powerful user on a 1:1 basis,” Bustos said. “You don’t have that in your standard news or editorial publishing house. I mean, the New York Times jumped on it, and it’s been incredibly successful for them.” To compete with walled gardens and streaming services, publishers need to offer some degree of targeting — and without relying on cookies.

But DCRs are heavy loads. Data maturity is an essential quality to get 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 CDP and DMP.

Bustos explained: “If you want a clean room of data, there are many other technology solutions before. You have 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 DCR. ‘I want to do this,’ then see what tools will lead you to it.”

Also understand that implementation takes talent. “It’s a demanding project in terms of installation,” Bustos said, “and there’s been a significant increase in consulting companies and agencies helping to build these data cleanrooms. You need a lot of people, so it’s best to hire outside help for the installation and then have an in-house maintenance crew. it’s more efficient to launch.”

Underutilization of measurement capabilities. One of the key findings in the IAB’s research is that DCR users are using audience customization 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 be able to do predictive analysis of my high lifetime value customers that they’re going to buy in the next 90 days.’ Or ‘I want to measure which channels are driving the most growth.’ What they want to do is very complex analysis; but they really have no reason why. What’s the matter? Understand your bottom line and develop a consistent data strategy.”

Trying to realize incremental growth from your marketing can take a long time, he warned. “But you can easily do reach and frequency and overlap analysis.” This will identify the investments made in the channels and offer as a by-product where the increase in growth is happening. “Companies need to know what they want, define what the outcome is, and then have the steps to get you there. This will also help you prove your ROI.

Dig Deeper: Failing to make the most of cleanroom data is costing marketers money

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