There’s a lot of pressure on marketers today to secure customer data responsibly. You need a data solution for using attribution models that don’t rely on third-party cookies if you want to continue ensuring your marketing campaigns are effective.
Data clean rooms for marketers are often posed as one solution — but it’s complicated. They let marketers and other platforms share first-party data to create useful audience segments and measure campaigns together. Read on to learn how they work, get step-by-step instructions for getting started and find the most popular vendors on the market.
What are data clean rooms?
Data clean rooms are cloud services that let companies share and analyze data with specific rules that limit how the data is used. Despite the name, they’re not physical spaces, don’t clean data and don’t inherently protect privacy. You’re responsible for carefully setting up and enforcing the rules within your clean room to prevent misuse.
The benefit of working within a clean room is that you can control how you collaborate and exchange data with other companies without sharing everything. For example, instead of handing over full customer lists, two companies can use a clean room to match specific data points, such as who saw an ad and later made a purchase, without revealing unrelated information.
While the technology isn’t new, it has gained attention recently in response to new privacy regulations and diminishing access to third-party data. They’re most popular amongst advertisers at enterprise companies looking for insights on a massive scale. For example, they might be used to:
- Combine point-of-sale data from several retail locations with that from consumer packaged goods (CPG) companies to identify which campaigns made the most impact.
- Merge data from an airline’s loyalty program with travel booking platforms to identify high-value customers and target them with offers based on their travel habits.
- Combine customer interaction data from an e-commerce site “or a CRM system” with data from social media to see which influencers drive the most sales or opportunities.
Marketers most commonly use data clean rooms for attribution.
Think of it this way: you might use shared data to measure a campaign you’ve run on LinkedIn. The platform has data that shows which emails are associated with ad interactions. You have data on which emails are associated with certain sales. By comparing the two data sets, you could determine whether or not the audience exposed to your LinkedIn campaign became a customer.
The biggest benefit is the ability for multiple parties to collaborate while maintaining privacy.
Key features of data clean rooms
Researchers estimate that by the end of 2024, 75% of the global population will have their data protected by privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This shift may make data clean rooms very relevant over the next couple of years for companies who want more control over how shared data is handled.
Yet, adoption remains low. Over half of the 266 marketers surveyed by clean room provider Habu (acquired by Liveramp) reported never using one. This could be due to the substantial resources required to set one up, but its privacy-focused features could make it worth the effort and cost for larger companies.
Data clean rooms have specific features designed to keep data clean.
It's up to marketers to ensure they have the proper consent and opt-out options in place. Different organizations interpret privacy laws differently, but using a data clean room can help by providing a controlled environment that minimizes the risk of non-compliance. These main data clean room features are the reason they’ve gained momentum in the past few years:
- Data segregation keeps each party's data separate and inaccessible to others.
- Encryption secures data during transfer and storage, preventing raw data exposure.
- Access permissions restrict who can view or use the data.
- Audit trails log all activities, tracking who accesses data and when.
As more marketers weigh the benefits against the challenges, understanding the pros and cons of data clean rooms can help clarify when the investment might pay off.
Pros and cons of data clean rooms
Data clean rooms have a lot going for them, especially when it comes to safely sharing first-party data. But they’re not without their downsides — they can be tricky to set up and aren’t a magic fix for every measurement challenge marketers face.
Data clean rooms have a lot to offer, but there are challenges.
Their pros include the ability to:
- Merge datasets from multiple parties while maintaining privacy
- Avoid issues related to privacy-centric browsers and ad blockers
- Stay compliance-friendly as long as data is lawfully obtained
- Replace traditional tracking pixels for attribution purposes
- Build multi-touch attribution models if all relevant data is shared
Their cons include the following:
- The need for direct relationships with ad platforms to use them well
- Their high cost (the average set-up cost is $879,000)
- A complex setup, especially with cloud-based solutions
- Real-time data processing limitations that make ad optimization slow
- Difficulty achieving holistic measurement across multiple channels
To really understand the benefits and limitations of data clean rooms, it helps to know how they work.
How do data clean rooms work?
Clean rooms work by referencing data from multiple parties in a shared environment — the clean room. While on its way to the clean room, data is hashed or anonymized using an algorithm or technique that suits your needs, usually becoming a fixed-length string of random characters.
Say your company sells shoes. You’ve just run an Instagram campaign and uploaded a list of transaction data that includes the email john.doe@example.com to your clean room. The hashing process transforms that email into a hashed key, like "5f4dcc3b5aa765d61d8327deb882cf99,” so it becomes unrecognizable.
Instagram also uploads ad exposure data to your clean room. Their data also includes john.doe@example.com, indicating they clicked on an ad for shoes. The clean room hashes the email to “e99a18c428cb38d5f260853678922e03.”
Then, the clean room recognizes that hashed key "5f4dcc3b5aa765d61d8327deb882cf99" from your data and matches the hashed key “e99a18c428cb38d5f260853678922e03” from Instagram’s data. Next, it aggregates the data into a pool of user-level data containing those who both clicked on your Instagram ad and bought shoes in the past week.
You can then use this data pool to measure your campaigns, enrich your buyer profiles and create audiences for future campaigns while keeping your customer’s data secure.
How data clean rooms work (adapted from Twilio Segment).
For example, the clean room would aggregate metrics like impressions, traffic and conversions without including personally identifiable information (PII). You could analyze whether or not Instagram drove traffic to your site and led to more conversions, generating a report based on the aggregated email data.
Use cases for data clean rooms (backed by real-world examples)
Data clean rooms are useful for marketers to measure the impact of their campaigns. They’re also useful in several other contexts like finance, healthcare, media and e-commerce.
Data clean rooms aren’t just for marketing measurement.
These other industries might use data clean rooms in the following ways:
- Finance: Banks can improve fraud detection by combining sensitive data from other financial and government agencies or build credit scores by aggregating customer data across partners.
- Healthcare: Doctors and pharmaceutical researchers can share data in a clean room to learn how patients react to treatments.
- Media and entertainment: Companies can get more audience insights by combining streaming and advertiser data.
- E-commerce data analysis: E-commerce brands can improve product recommendations by combining data from mobile applications and partner websites.
- B2B sales cycle attribution: Marketers in B2B organizations can gain new insights into what’s influencing sales by combining opportunities and deals closed/won from their CRM to campaign interaction order.
These use cases also rely on sharing first-party data between organizations. While the benefits could be substantial, other industries — such as marketing — have also been slow to adopt clean rooms.
4 reasons marketers might use data clean rooms
Marketers are now juggling more regulations than ever, and with consumer pushback on cookie tracking, third-party data is dwindling. It’s tougher to measure and target ad campaigns effectively.
Data clean rooms can help you compliantly scale your usage of first-party data to fill the gap third-party data left behind, but they’ll never totally replace it. Instead, clean rooms should be one tool used as part of a bigger, more holistic approach to measurement and targeting in a cookieless world.
1. Stay compliant with data privacy regulations
Clean rooms can help you further comply with regulations like GDPR and CCPA, which are critical laws regulating how personal data is collected, stored and used. These laws require marketers to get explicit consumer consent before collecting personal data.
A data clean room lets you work with personal data that partners have collected with consent — without violating data privacy laws — because the data remains anonymized and individual identities are protected.
How data clean rooms achieve compliance.
Clean rooms use three methods that work together to keep identities from being exposed:
- Encryption: Keeps data safe during transfer and storage, preventing breaches and meeting compliance by protecting personal information.
- Anonymization: This process strips out identifiable details from datasets to meet the rules regarding protecting or removing personal data when shared.
- Data governance: Manages who can access and use data, keeping clear records of access, processing and sharing to ensure transparency and accountability.
Regulations like GDPR and CCPA demand that personal data be secure, private and controlled. Data clean rooms cover all those bases: encryption locks data down, anonymization lets us use it without exposing anyone and governance keeps access in check.
2. Move forward without third-party cookies
Even if third-party cookies stick around, privacy regulations will still push you to find secure ways to handle customer data. But third-party cookies will diminish, so you’ll have to rely more on first-party data to reach your audiences.
Other methods, such as contextual targeting, federated learning or server-side tracking, might promise to do so. However, data clean rooms will allow you to build richer audience segments, personalize targeting and get detailed insights without violating privacy regulations.
Compared to federated learning (where data stays on each person’s device) or server-side tracking (which still has compliance issues), clean rooms balance security, usability and privacy compliance really well.
3. Improve audience targeting
Data clean rooms let marketers create more precise audience segments by blending data from different sources.
For example, a luxury watch brand could merge its own data with ad engagement data from an online lifestyle magazine. The brand knows who’s already bought a watch, while the magazine knows who reads about high-end timepieces. Combined, this data helps the brand find a high-value group, such as men aged 35 to 50 who clicked on watch ads and made a purchase within the last two months.
Because this data is hashed and anonymized, it’s fully compliant with GDPR and CCPA. It balances privacy and targeting by creating aggregated segments rather than individual profiles. It’s not as detailed as third-party cookies, but it’s one solution for targeting the right audiences in a privacy-safe way.
4. Improve measurement
The ability to combine data from different sources also helps with accurate attribution. You can see which channels or touchpoints drive results, which is a huge help. That said, they have some limits — like only giving insights on single platforms (think Google or Facebook) instead of across channels. Plus, they’re not always fast enough for real-time ad tweaks.
Even with those downsides, clean rooms still balance privacy with more precise measurement. Balance your measurement strategy with other methods for a more holistic solution. Triangulation involves using probabilistic data (data based on very educated guesses) instead of just first-party data. This approach could give a more rounded view of campaign performance and make targeting even sharper while keeping everything privacy-friendly.
Vendor options for data clean rooms
Your choice of data clean room provider shapes how you’ll get insights, keep data secure and comply with privacy laws. There are plenty of options, from general cloud platforms to clean rooms designed specifically for marketers.
Popular data clean room vendors.
Here’s a quick look at some of the main players to consider when choosing a data clean room solution:
- Google’s BigQuery: Part of Google’s analytics ecosystem and popular with advertisers who want detailed insights into performance and audiences.
- Amazon AWS Clean Rooms: This one is through Amazon Web Services. It’s widely used in retail and advertising because it’s flexible and integrates easily with other AWS tools.
- Infosum: A SaaS provider that’s all about secure data collaboration, it’s especially useful for advertising and media companies.
- Habu (acquired by LiveRamp): Another SaaS option, Habu is focused on making clean rooms easy to use, even for teams without deep tech expertise.
- Snowflake: A powerful cloud option with great security features, especially after it acquired Samooha in 2023, which added extra cryptographic protection.
Each platform has its own strengths, so the best choice depends on what’s most important to you. Think about a few key factors:
- Security: To keep data safe, you want strong encryption and hashing. Snowflake, for example, stands out for its high-level cryptographic security.
- Flexibility: Snowflake allows for custom queries and integrations but this can require more technical expertise. Simpler SaaS options like Habu or Infosum are also flexible, including the ability to run your own SQL queries and models and are easier to set up.
- Ease of Use: Habu (LiveRamp) and Infosum have a reputation for being user-friendly, even for non-technical teams, while something like AWS may require more technical knowledge.
- Compliance: Privacy laws like GDPR and CCPA are big deals, and platforms like Google and Amazon have plenty of experience in this area.
- Scalability: For handling huge amounts of data, especially for larger companies, Snowflake and Microsoft Azure are designed to manage billions of records, making them ideal for large, data-heavy projects. For mid-sized teams, Habu (LiveRamp) and Infosum should work well. These platforms scale well by optimizing the virtual machines and resources used to run complex queries and workloads. However, for massive data volumes, a more scalable solution like AWS might be necessary.
For small to mid-sized teams, a SaaS provider with a simpler setup might be all you need. Larger enterprises might find the flexibility and scale of a cloud-based platform more valuable.
FAQs about data clean rooms
What is the difference between a CDP and a data clean room?
A customer data platform (CDP) combines all your first-party data to give marketers a complete view of each customer. A data clean room, though, lets other companies share and analyze data while keeping each party’s data private and secure.
What is the difference between a data clean room and a DMP?
A data management platform (DMP) is all about collecting third-party data from customers for ad targeting. A data clean room is different. It securely shares first-party data that’s already been collected from multiple sources with compliance.
What is the difference between a CDP and MDM?
A CDP focuses on combining customer data specifically for marketing. Master data management (MDM) has a broader role, standardizing and managing data to keep everything consistent — not just for marketing but also for other departments.
Data clean rooms for marketers can’t fix everything
Data clean rooms are a good option for marketers trying to work around privacy regulations and the loss of third-party data. They’re not a magic fix, but they allow sharing and analyzing first-party data without breaking privacy laws.
Clean rooms are worth adding to your toolkit if you want to balance privacy with effective targeting and measurement.