How InfoSum’s decentralized data solutions are solving consumer identity challenges
Produced in partnership with Marketecture
The following article provides highlights from an interview between Brian Lesser, InfoSum’s Chief Executive Officer, and Ari Paparo, founder and CEO of Marketecture Media. Register for free to watch more of the discussion and learn how marketers are employing clean rooms to address consumer identity challenges.
As marketers and brands scramble for new identity solutions following the deprecation of third-party cookies, one set of technologies is standing out among the rest: data clean rooms. These digital environments — also known as data collaboration platforms — enable marketers to compare data sets without having to risk sharing consumers’ data.
InfoSum’s CEO Brian Lesser recently spoke with Ari Paparo, founder and CEO of Marketecture Media, about how an increasing number of brands are seeking clean room technologies that allow them to onboard their customer data effectively.
The interview spotlighted how InfoSum is helping companies move away from the traditional centralized processes of comingling data and embrace technologies that decentralize audience information, enabling better analysis and privacy protection.
“There is a tried and true way of normalizing data, which mostly has to do with finding a common identity and commingling data in a third-party database,” Lesser said. “But now the industry has moved us into data clean rooms.”
Clean rooms are helping marketers assess and understand their customer data
Clean rooms are allowing marketers to develop more accurate customer-centric marketing strategies. These neutral environments enable more effective CRM and ad exposure data analysis. In addition, clean rooms are helping brands bring together first-party consumer data and perform in-depth analysis while protecting audience identities.
However, many data collaboration solutions create siloed views due to the limitations of the provider’s specific channel. According to Lesser, brands are typically forced to work with multiple partners to dissect this audience data. Under this model, one partner provides the identity information, and one offers the onboarding solution — pulling the data together, anonymizing it and sending it back for analysis.
“That whole process is time-consuming, inefficient and also increasingly fraught with privacy and security challenges,” he said.
Further complicating identity resolution issues, traditional clean rooms are often built using centralized databases. This requires each data owner to upload personal data into a third-party environment, which raises significant consumer privacy issues.
Lesser said marketers should adopt decentralized clean room solutions that license technologies to parties using data “bunker” technologies to address these challenges. Advertisers upload CRM data into these isolated segments and then sit back as the information is transformed from individual rows of customer data to descriptive mathematical models that are protected from being reverse-engineered into a customer file.
Decentralized solutions such as these are helping protect consumer privacy and build a bridge between data sources and consumer identities.
“The bridge can be anything,” said Lesser. “These data collaboration systems will recognize where there is overlap. If those two data sets have email addresses in common, the system will tell you that the biggest overlap between them is email. It could be any descriptor of the data, and it can be online and offline.”
How identity resolution technologies can enhance clean room capabilities
In years past, brands primarily relied on identity resolution technologies to match customers across various data sets, but as marketers continue to lean into first-party data, many are finding these technologies work better when paired with clean rooms.
When evaluating compatible identity solutions to work with clean room technologies, savvy marketers are taking note of their match rate capabilities — the percentage of consumer records that can be matched to another data set. These metrics help highlight how effective identity technologies are at resolving identities in a clean room environment. Lesser says marketers should also look out for features such as identity precision level, the accuracy of matches and the reach of addressed users.
Meanwhile, new clean-room frameworks are unlocking greater data transparency, flexibility and decentralized analysis to power more effective data matching and measurement. Still, when the data is too distinct, Lesser says it’s best to work with identity solutions, especially if the clean room technology allows for effective data collaboration.
“Data collaboration works well when those data sets have something in common,” he said. “It doesn’t work as well when those data sets have nothing in common. That’s when we can pull in an identity provider to bridge that gap.”
To learn more about clean rooms and identity resolution technologies, listen to more of the conversation between Marketecture and Brian Lesser here.