‘Multiplier effect’: Lower match rates in data clean rooms are being given a much-needed boost
Publishers looking for a bit of good news in their search for sustainable ways to scale their data sans third-party cookies may have gotten some — thanks to data clean rooms.
More specifically, the match rates in those rooms. Normally, they aren’t anything to shout about. They can be pretty low and sometimes inaccurate — not a great precedent for tech predicated on correctly matching data sets. There are ways to boost those odds, of course. But they’re challenging at best, unrealistic at worst. It makes then that data clean by searching for ways to improve those match rates.
“After all, having a clean room that doesn’t lead to maximizing match rates is like investing in a gym membership when you are morally opposed to healthy living,” said Kevin Bauer, data and identity strategy lead for Prohaska Consulting in North America. “What is the point?”
It’s perhaps unexpected that there’s a fair amount of expectation around data clean room InfoSum’s latest partnership. It is working with the IPG-owned data management business Acxiom — or rather the part of the business set up to boost match rates between different data sets, called “Match Multiplier.” In layman’s terms, the tie-up works like this: Acxiom’s “Match Multiplier” data sits in an InfoSum clean room that Acxiom owns and controls.
Advertisers and media owners wishing to expand the match rates they can achieve natively on a one-to-one match can use the clean room in a three-way match where it acts as a bridge of sorts between them all. This matching takes place in the clean room without exposing or sharing the underlying data with any of the respective parties.
Better match rates should mean more interest from ad execs. And the partnership has the potential to boost those rates considerably. Through testing, customers have seen a 40% incremental lift in matching between two or more data sets, according to InfoSum.
Those rates could be lower or higher depending on the data sets involved, but even a slight lift in match rates could (in theory) have a material impact on both advertisers and publishers. Moreover, better match rates ease the pressure on marketers having to do the heavy lifting to get data clean rooms working for them. Indeed, successful targeting via these technologies often requires large data sets to overcome the low match rates. The reality is most marketers have never actually had to define these audiences on their own, much less track performance etc. across a media lifecycle.
“We certainly anticipate and are already seeing increased interest from advertisers,’ said Brian Lesser, chairman and CEO of InfoSum. “With every major partnership we strike in the market, we have reached an inflection point where the success of data clean rooms has become inevitable. Right now there’s a bit of a waiting game with publishers to see where the major brands make their choices for data clean rooms before they commit to their plans.”
In other words, publishers are going to back the data clean rooms that have buy-in from the most advertisers. And advertisers will back the clean rooms where match rates are highest. As Lincoln Gunn, vp of programmatic revenue, operations and data partnerships, explained: “Specific clients have specific deals with specific vendors and so as a result we have to try and make sure that we service their needs. So if they’re working with one brand that uses one data clean room and then another is using an alternative then we have to make a call on where we focus our attention and resources because those platforms don’t necessarily talk to one another.”
Don’t expect partnerships like this to be a silver bullet to woes of data clean rooms. Interoperability is still a major point of confusion and not all identities are the same. Each brand, publisher and software provider defines identity and audiences slightly differently which poses a business planning and technical challenge and feels like an oxymoron for brands.
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