Are data cleanrooms the answer to what David Cohen, CEO of IAB, calls the “slow train wreck” of processing capability? The voices at IAB will tell you they have a huge role to play.
“The problem with addressability is that once cookies are gone and identifiers are gone, about 80% of the addressable market will become anonymous audiences and that is why privacy-centric consent and better consent-value sharing is needed,” said Jeffrey Bustos, Vice President of Measurement, Addressability and Data. in IAB.
“Everyone talks about first-party data, which is very valuable,” he explained, “but most publishers who don’t have a login have about 3 to 10% of that first-party data for their readers.” First-party data, from the perspective of advertisers who want to reach relevant audiences, and publishers who want to offer valuable inventory, isn’t enough.
Why do we care. Two years ago, who was talking about clean data rooms? The increased interest is recent and significant, according to the IAB. DCRs have the ability, at least, to keep brands connected with their audiences on the open internet; to maintain publisher inventories current; And to provide advanced measurement capabilities.
How clean rooms data can help. DCRs are a type of privacy-enhancing technology that allows data subjects (including brands and publishers) to share customer first-party data in a privacy-compliant manner. Cleanrooms are secure spaces where first party data from a number of sources can be parsed into the same customer profile while that profile remains anonymous.
In other words, DCR is a kind of Switzerland — a space where a truce is called upon on competition while first-party data is enriched without compromising privacy.
“The value of a clean data room is that the publisher is able to collaborate with a brand via its data sources and that the brand is able to understand audience behaviour,” Bestos said. For example, a brand that sells eyeglasses may not know anything about their customers except basic transaction data – and that they wear glasses. Matching profiles to publisher behavioral data provides enrichment.
“If you are able to understand the behavioral context, you will be able to understand what your customers are reading, what they are interested in, and what their hobbies are,” Bustos said. Armed with these insights, the brand has a better idea of the type of content it wants to advertise.
A publisher needs to have some level of first-party data in order for a match to occur, even if it doesn’t have general login requirements like the New York Times. A publisher may only be able to match a small percentage of the eyeglasses seller’s customers, but if they like to read the sports and arts sections, that at least gives some directional guidance about what audience the seller should target.
Dig deeper: why we care about clean data rooms
What is considered a good match? In the State of the Data 2023 report, which focuses almost exclusively on cleanroom data, concern is expressed that DCR’s effectiveness may be threatened by poor match rates. Average match rates hover around 50% (lower for some types of DCR).
Bustos is keen to put this in context. “When you’re matching data from a cookie perspective, match rates are usually around 70%,” he said, “so 50% isn’t bad, though there is room for improvement.”
One hurdle is the continuing lack of interoperability between identity solutions – although it exists; LiveRamp’s RampID, for example, can run with UID2 of The Trade Desk.
However, Bustos said, “It’s very difficult for publishers. They have a bunch of identity pixels that trigger all these different things. You don’t know which identity provider to use. We certainly have a long way to go to make sure there’s interoperability.”
Keeping the Internet open. If DCRs can contribute to solving the problem of addressability, they will also contribute to the challenge of keeping the Internet open. Walled gardens like Facebook contain a rich collection of first-party and behavioral data; Brands can reach these audiences, but with very limited visibility to 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. “Normal News or an editorial publisher doesn’t have that. I mean, The New York Times has moved into that, and it’s been incredibly successful for them.” In order to compete with walled gardens and streaming services, publishers need to offer some degree of addressability — and without relying on cookies.
But DCRs are a heavy burden. Data maturity is an important qualification for getting the most out of DCR. The IAB report shows that more than 70% of brands that evaluate or use DCRs have other data-related technologies such as CDPs and DMPs.
Bustos explained: “If you want a clean room for data, there are a lot of other technology solutions that have to be there before. You need to make sure you have robust 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,” and then look at the tools that will get you there. “
Also understand that execution will require talent. “It’s a demanding project in terms of setup,” Bustos said, “and there’s a huge growth in companies and consulting agencies helping to set up the data cleanrooms. You need a lot of people, so it’s best to hire outside help to set up, and then have a maintenance crew in-house.” .
Underutilization of measurement capabilities. A key finding in IAB’s research is that DCR users tap audience matching capabilities much more than they perceive measurement and attribution capabilities. “You need very strong data scientists and engineers to build advanced models,” Bustos said.
A lot of the brands looking at this are saying, “I want to be able to do predictive analysis of my high lifetime value customers who will buy in the next 90 days.” or “I want to be able to measure which channels are driving the biggest increment.” They want to do very complex analytics.
He cautioned that trying to understand the incremental increase in commercialization can take a long time. “But you can easily do reach, frequency, and overlap analysis.” This will identify wasted investment in channels and as a by-product indicate where the additional lift will occur. “There is a need for companies to know what they want, to define the outcome, and then there are steps that will get you there. This will also help prove the return on investment.”
Dig deeper: Failing to make the most of clean rooms costs marketers money
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