Right now, publishers and advertisers have data collaboration firmly on their minds. That’s because collaboration is key to driving the most value from their respective first-party data. And there are good reasons why data clean rooms (DCRs) have been cited as publishers’ best bet for successfully collaborating with advertisers – and their data. However, it’s a challenge to understand how today’s DCR options differ and which one (or more) to select.
Armed with the right DCR, publishers stand to take back control of their customer data and media revenue. Some factors may put publishers at an advantage: percentage of authenticated traffic from logged-in users, breadth and depth of first-party data categories, and overall footprint of data per region. The end goal is to bring as much first-party data, on a common identifier, that is relevant to an advertiser – and to do it in a controlled environment. That’s all necessary to produce a meaningful, useful output.
A needs evaluation
Before unpacking four common DCR categories and how they vary, it is critical to have a clear understanding of your specific needs so that you can make the best choice for your media company (and maximize the value of your data).
Honestly evaluate your organization’s:
- Use case. What are the specific needs of your advertising clients?
- Technical resources. What current staff do I have or need to properly prepare first-party data and execute clean room implementation, analysis, and connectivity into the tech stack?
- Time to value. What timelines am I willing to work with, without negatively impacting revenue?
- First-party data. What data do I own? And what volume of it is tied to a hashed email (HEM), mobile ad ID (MAID), or other common identifier?
Categories of digital clean rooms
Ok, now that you have a better understanding of what you really need, here are the four main DCR categories we’ve seen emerge this year along with some tips about which might be best for your needs:
In this variety, the data is stored in a warehouse, then transferred to a clean room for partner collaboration. This is how Snowflake operates – built on Amazon Web Services. Many pubs favor the cloud model, as it reduces both the need to port data sets from one location to another, and the related risk of data leakage. There are opportunities here for technical customization, for a business with the engineering or data science resources to take on the task. Attribution and measurement are a good use case for this category.
Some Big Tech platforms offer their own DCRs – Google’s Ads Data Hub, for example – while also partnering with other tech providers’ clean rooms. Walled gardens may offer straightforward implementation for businesses with limited tech resources. They may also provide some flexibility for publishers and advertisers to work with the data sets and partners they want – but any customizations may demand an assist from a data scientist. Keep in mind walled gardens are often resistant to move data from their own respective ecosystems, after participants port it over. Use cases for advertisers include activation, suppression, and measurement.
Here, participants onboard their data and use one of several available common identifiers to collaborate. In some cases, a participant can bring in another partner to collaborate without paying extra fees. Data collaborations allow for activation in environments outside of a walled garden – a feature walled gardens themselves don’t offer. They deliver user-friendly implementation, and advertiser use cases include data analysis, data enrichment, and activation via monetization of a desired audience.
Query clean rooms are engineered for collaboration in a neutral environment. They connect various clean rooms and environments, and they don’t require participants to port over their data. They’ll support any common identifiers the participants choose. As such, analyzing data overlap is an advertiser use case here. Query clean rooms will require tech knowledge and resources to oversee data orchestration, but a generalist with some training may be able to manage it successfully.
Looking beyond factors like the use case and the resources available, publishers will need to ask questions about the data itself while vetting potential partners. Some critical questions to keep in mind:
- Do the participants have enough compliant data available now for successful collaboration, or can they count on having it in the future?
- Can the data be enhanced or modeled out using machine learning?
- Who needs to access the data, internally or externally?
- Where is the data currently stored?
Tailoring clean rooms to publisher needs
Ok, so it’s clear that one size does not fit all when it comes to DCR’s. But your specific needs may not be met out of the box, regardless of the solution you choose. Media companies may also find that getting all of their preferred advertiser partners into the same clean room is a challenge in itself. You need to consider whether your organization has the tech resources to support clean rooms from multiple providers. The tech itself and its capabilities (or limitations) might not be the core problem, though. The more basic issue may be with relationships between partners. This is where a strong identity framework can be a bonus, as it can provide interoperability.
One more consideration: Publishers will find that many clean rooms are engineered for marketer use cases. And brands that have a good deal of influence in the marketplace will also want to push their publisher partners into their own preferred clean room environment. Publishers need to make sure they or the clean room provider can manage integrations across various clean rooms, to provide the reach that incentivizes advertisers to spend.
There’s no such thing as a universal DCR that can manage all relevant use cases and business needs. While there has been plenty of talk about clean rooms, turning talk into action has been minimal thus far. Publishers would be wise to coordinate with their largest, most strategic advertiser partners to identify which clean room(s) support their primary goals and align with available resources.
About the author
Alexandra Theriault is the Chief Growth Officer for Spherical, a suite of CDP accelerator solutions, at Lotame. Alex oversees the Spherical team working alongside brands, helping them maximize the value of their first-party data to enable personal development, enrichment, analysis and advertising activation. In her prior role as Chief Customer Officer, she led the organization’s customer focus, programs and strategy across six continents. Alex brings 16 years of experience designing, developing and delivering world-class customer success for global brands, agencies, platforms and publishers across martech. She serves as an advisory board member on Lotame’s Diversity & Inclusion council, and served as an advisory board member for a martech start-up in the affiliate space. Prior to joining Lotame, Alex was with IndustryBrains, a leader in monetizing B2B & Financial publisher content through contextual advertising, a subsidiary of Marchex.