Privacy changes have brought a lot of new trends and buzzwords into the mobile advertising environment.
Data clean rooms are one of them, and they are a big one. Many advertisers have heard this term, but they are not completely familiar with it. Upon reading this article, this will no longer be an issue.
Let’s start with the basics.
What is a Data Clean Room?
A data clean room (DCR) is a piece of software that allows advertisers and their partners to trade user data in a privacy-compliant way.
Data clean rooms act like neutral territories.
For this reason, they are frequently called “Switzerland for data”.
Within these rooms, two parties can share data without revealing any kind of PII (Personal Identifiable Information) on their users.
How is this possible?
All the raw, first-party data that enters a data clean room gets encrypted, anonymized, and aggregated. Such data can then be used by advertisers and their partners to compare and analyze their audiences.
How Data Clean Rooms work
To be able to understand how data clean rooms work, it’s useful to observe them as a series of steps. Here is a simple breakdown of these steps:
- Each party brings its own data into a neutral space
- An advertiser’s first-party data is combined with the partner’s data
- The audiences of both parties are compared in a clean room setting
- Different privacy methods ensure that PIIs are never shared
- Data is aggregated and shared with both parties
One of the key points here is the fact that data clean rooms deliver aggregated data.
What does this mean exactly?
Aggregated data is summarized group data. It answers questions like “How many users have performed a certain action?”. However, it does not give the answer to “Who has performed the action”?”.
From this kind of data, both parties can find out what their audiences have in common. For example, things like their purchasing habits, demographics, or interests.
Where do Data Clean Rooms come from?
Data clean rooms have been used in data science for decades. For example, in industries like fintech and healthcare. However, it wasn’t so long ago when they entered the advertising ecosystem.
Here's how it all started.
The first company to market a DCR solution was Google. This was back in 2017 when the company launched its data clean room called Ads Data Hub. It was developed as a privacy-based replacement for their previous advertising solution called DoubleClick.
Ads Data Hub was created to give marketers a private space where they could analyze their first-party data across all Google properties.
Back when this solution was first developed, data clean rooms weren’t such a big deal. The advertisers still had access to user-level data, and they could measure everything.
However, with the rise of privacy restrictions, more companies have started developing their own data clean room solutions, and more advertisers are interested in them. You can also learn more about Apple's Private Relay technology here.
Types of Data Clean Rooms
Data clean rooms don’t come in one shape and size. Generally, they fall into two categories – walled gardens and multi-platform data clean rooms.
Walled gardens
The term “walled gardens” is used to describe closed ecosystems like Google, Facebook, and Amazon. These companies have huge banks of advertising-related user data and significant control over them.
These three companies have developed their own data clean room solutions.
Within their walled gardens, advertisers can access platform-owned advertising data and compare it to their own first-party data. They can then utilize this data to analyze campaign performance, audiences and optimize ad budgets.
This data can be used within the platform only. For example, an advertiser cannot get mixed data from Google and Facebook.
For this reason, using this type of data clean room makes the most sense for advertisers who spend a lot of money on a single platform.
Multi-platform (partner) Data Clean Rooms
Multi-platform data clean rooms are based on direct partnerships.
Brands form these partnerships to trade aggregated data in a secure environment. Thanks to this data exchange, the two parties can compare audiences and gain valuable insights. All while keeping raw, user-level data all to themselves.
Unlike in walled gardens, here, marketers can also view cross-channel performance. The data is not limited to a certain platform.
What kinds of businesses can partner up?
These are usually businesses that have shared audiences but aren’t competitors. For instance, a food delivery app and a restaurant that doesn’t deliver might be a good match.
Data Clean Rooms: What’s in it for marketers?
Marketers are excited about data clean rooms for a variety of reasons. For the most part, the benefits come down to two things – measurement and privacy.
Let’s cover the measurement part first.
Because in-platform reporting is getting more limited by the day, advertisers are craving additional data. Data clean rooms help them with this, providing them with some extra data fields.
The most significant insights DCRs provide are audience-related. They can provide valuable audience insights such as:
- Cohort-level audience data
- Reach and frequency analyses
- Segmentation
- User lifetime value reporting
Among other things, data clean rooms help marketers understand if their ads are reaching the right audiences. For example, they can find out there is a big untapped potential audience just waiting to be reached.
Advertisers can use these findings to build custom audiences and reach them with the right messages. Of course, all of this helps them spend their advertising budgets more effectively.
The privacy part is pretty self-explanatory.
As mentioned before, data clean rooms are completely privacy-compliant. They don’t expose any kind of individual data to any party in the process. All user-level data is anonymized and can be safely used for measurement and other purposes.
Data Clean Rooms: What’s in it for app users?
It is pretty clear that data clean rooms are useful for marketers. But what about users?
App users can benefit from data clean rooms too, and not just from the privacy standpoint.
Whether they know it or not, users have certain user experience expectations. For instance, when using an app, they expect customized and relevant content. Without user-level data, delivering great user experiences has become more challenging.
This is where data clean rooms come in.
Thanks to data clean rooms, it is possible to optimize for better in-app user experiences for different groups of users.
Is Data Clean Room the perfect technology for the privacy-first era?
Data clean rooms are far from the perfect measuring tool. However, one thing is for sure – they bring advertisers access to information they would not otherwise have.
Today, every piece of information is gold.
Different user privacy regulations have put an end to attribution as we know it, and advertisers are finding new ways to adapt.
All in all, data clean rooms make a great alternative for measuring advertising efforts outside of restricted ad platforms.
As the privacy rules become stricter, we can expect data clean rooms to become more and more popular. The prediction is – by 2023, 80% of advertisers with media buying budgets over $1 billion will use data clean rooms (Gartner).
We have yet to see how mobile advertisers will utilize data clean rooms, but it’s definitely happening.
We can expect that some advertisers will settle for walled gardens, while others will establish meaningful partnerships. Finally, some will utilize both types of data clean rooms to try and get the broadest picture.
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