Demystifying 3 Questions About Data Clean Rooms
Data clean rooms are the new kids in school. They have the cool backpack, are attracting attention, and incumbents are starting to question whether their appeal has staying power.
But data clean rooms have emerged to solve a problem that is not going away anytime soon: leveraging privacy-compliant customer data. Brands, retailers, and publishers need regulation-proof ways to share information with consumer consent. Understanding how data clean rooms solve this problem and what their limitations and opportunities are will be key to how advertisers, publishers, and tech companies evolve into the next era of marketing.
Let's break down three pervasive questions about data clean rooms. Namely, are data clean rooms scalable, interoperable, and actionable? Answering these questions will help advertisers and publishers navigate the space more strategically.
Are Data Clean Rooms Scalable?
Perhaps the most common question facing not just data clean rooms but first-party data-centric marketing strategies in general is whether they are scalable. Marketers are accustomed to chasing a 360-degree view of the customer across platforms thanks to third-party data and technologies like the mobile advertising ID and third-party cookie. But with these identifiers going away due to privacy regulations, the 360-degree view is fading, and marketers need to rethink their approach to understanding their customers.
So, what comes after the old version of scalable customer data? First up is transparent, deterministic data sourced directly from organizations with one-to-one consumer relationships. For example, a large gaming publisher might command a rich inventory of data on millions of customers who have consented for the publisher to collect and share that data.
The publisher can use a clean room to share that data with partners, where it becomes second-party data that can enrich and complement the first-party data the partner, possibly another publisher or advertiser, has on hand. This is privacy-compliant data collaboration at work.
The other scalable privacy-safe consumer data tactic is probabilistic modeling. In many cases, deterministic first-party data will simply be a seed against which organizations can model customer preferences to understand a customer's likely affinities.
AI will help here, and marketers will become more sophisticated at this kind of modeling in the coming years. In either case, the organization is using a privacy-compliant source of truth about customers to scale their data, a vast improvement on the fraud, inaccuracies, and privacy-unsafe practices that pervaded the third-party data market.
Can Clean Rooms Work Together?
Related to the scalability question is that of interoperability. Clean rooms are proliferating as pure-play companies are joined by Big Tech companies, CDPs, and identity and measurement providers, among others. Organizations do not want to work with a dozen vendors. How do they work across vendors and walled gardens to achieve a holistic picture of customers?
Like the scale of the era of third-party data, interoperability in marketing is never going to exist in the form of past years. Due to privacy changes and more restricted data sharing by walled gardens, the days of pulling data from dozens of platforms and establishing a single customer view in a multi-touch attribution or data management platform are over. Thus, the industry needs to figure out what the new standard for interoperability looks like.
Interoperability in the current era of marketing means being cloud- and identity-agnostic. Marketers should expect clean rooms to help them understand and share data across organizations whether information is stored on Azure or Snowflake, for example. They should also, without expecting a single user ID across walled gardens, ask data clean rooms to provide modeling capabilities that help them understand customers across different environments. Consumers should not need to be on the same ID key to be understood across properties. This is what interoperability for the privacy-oriented marketing industry looks like.
Is Clean Room Data Actionable?
A final critique marketing leaders sometimes levy against clean rooms is that they make more privacy-safe data available but not actionable. In other words, what matters is not just how much data marketers have at their disposal but what technologies like clean rooms allow them to do with it. After all, many media buyers do not have teams of data scientists on hand prepared to query large datasets and source actionable insights.
But clean rooms do not just offer data; clean room operators can define use cases that will deliver value to buyers and provide access to the data and insights users need to maximize the value of privacy-safe information. Clean rooms can also provide insight dashboards so that users can make sense of the data and understand audience segments, customer journeys, and other insights that power marketing needs such as targeting and measurement.
Data clean rooms are not a magic bullet for digital marketing's privacy challenges. They are not going to restore the same sort of scale or interoperability third-party data provided, though those forms of scale were never as desirable as their champions made them out to be. And they are not an all-in-one marketing solution.
But there is a reason many of the biggest tech players, media companies, and brands are rolling out their own clean rooms. The technology is playing a major role in the shift to a privacy-conscious marketing ecosystem. Early adopters are poised to lead the charge.
The views and opinions expressed are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.
Matt Kilmartin is co-founder and CEO of Habu, the leading data collaboration software company. For more than 20 years, Matt has led global enterprise sales, marketing, and business development for marketing technology and data companies. Prior to Habu, he was the chief customer officer of Salesforce's Consumer Engagement Platform, a role that focused on talking to hundreds of CMOs worldwide to understand their consumer data challenges.