Put that feather duster away! We're not going to be discussing how clean your office may or may not be.
No, in this chapter of the Data Dictionary series, we’re going to give you a comprehensive overview of Data Clean Rooms and how they can help your business to remain competitive, and compliant with new privacy regulations.
If you've never heard of a Data Clean Room—or you have, but you're not sure what all the fuss is about—then this is for you.
What is a Data Clean Room?
A data clean room is a secure, regulated, online software environment where two or more parties can collect, share, and analyze first-party user data.
The primary aim of a data clean room is to ensure that user data remains private, anonymous, and compliant with various privacy laws and regulations.
Imagine a data clean room as a kind of virtual vault that automatically collects generalized information about people without ever asking them for their name, address, bank details, or any other sensitive personal data which might identify them. This kind of non-sensitive, first-party data is known as non-personally identifiable information (non-PII).
The collaborating parties within a private clean room can submit their proprietary data to a shared dataset and analyze it safely, without fear of their information being exposed to other participants.
Each party has full control over where, when and how their own data can be used within the clean room—for example, they can determine which individuals can perform certain analysis on their data—without compromising any confidential information.
Alternatives to clean rooms include universal user IDs, Google Privacy Sandbox, and contextual targeting.
Why Do Data Clean Rooms Exist?
More stringent data protection and privacy laws introduced in recent years have forced the marketing industry to adopt a privacy-first mindset. This has led the marketing tech industry to develop new ways of collecting and sharing consumer information.
In 2018, amid growing pressure from consumers and governments, GDPR legislation was rolled out across the 27 member states of the EU. These new regulations were also applied to non-EU companies who either offer goods or services to Europeans, or who monitor their online activities.
In the same year, Apple launched Intelligent Tracking Prevention 2.0, as part of an ongoing campaign to position themselves as the kings of privacy regulation.
In January 2020, the California Consumer Privacy Act (CCPA) was brought into effect, and in April 2020, Apple announced that they were introducing App Tracking Transparency (mobile app data opt-in requirements) with the launch of iOS 14. This gave users of Apple devices the freedom and flexibility to easily opt out of app tracking.
In October 2021, Facebook declared that it will provide user-level campaign data to Mobile Measurement Partners (MMPs) rather than advertisers directly, and other networks are likely to follow suit soon.
Apple's ATT framework, Facebook's new user-level data policy, and the impending demise of Google's third-party cookies means that traditional data sharing methods are becoming increasingly limited, making it more difficult than ever for advertisers to measure performance and optimize their campaigns.
No business wants to be the subject of a huge data breach scandal, but businesses still need the insights that customer data can unearth. Data clean rooms can serve as a safe haven for companies to store their valuable audience targeting information without any risk of data exposure. They allow advertisers to access, process and store user level data without ever violating users’ privacy.
Although privacy is the main driving factor behind the rapid emergence of data clean rooms, businesses are also seeing advantages in the ability to join together different datasets.
Clean rooms encourage increased transparency and control over how data is collected and used, which ultimately gives all parties access to higher-quality data. This can be mutually beneficial for both publishers and advertisers.
How Does a Data Clean Room Work?
In a data clean room, brands, multiple companies, or divisions within a single company, are able to add their own first-party user data. This might include historical and transaction data—for example, the types of data that can be loaded from a CRM system or an e-commerce platform.
Once inside the clean room, the data is encrypted and secured, allowing access only to those who are explicitly authorized by the data owner. The data is "cleaned" using processes such as pseudonymization, differential privacy, restricted access, and noise injection. The brand/company alone has unrestricted access to the clean room—their clients and partners can only obtain data that is completely anonymous and that abides by all regulations.
Once this process is complete, audience data is sorted into cohorts. It can then be accessed, shared, collated and jointly analyzed according to strictly defined guidelines and privacy regulations.
Such analysis enables companies to determine how their own data compares with the aggregated data. Reports and insights from the data clean room can then be used by advertisers and publishers for marketing processes - for example, to target specific demographics, and to inform, measure, and improve ad campaigns.
Who Uses Data Clean Rooms?
Any business wishing to gain deeper insights into data attribution, customer worth, segmentation and consumer behavior will benefit from using a data clean room.
Clean rooms offer numerous applications across a diverse range of industries.
Marketing & Media
Advertisers can leverage joint datasets from data clean rooms to help them reach broader audiences, refine their target audiences, and to make sure that they are sending relevant messages to the right people at appropriate times. They can obtain greater transparency on the effectiveness of their ads, whilst conforming to stricter data privacy standards.
Data clean rooms can facilitate deeper campaign analysis. They can help marketers to better understand intent and trends in the way consumers interact with brands, determine audience gaps, spot untapped market potential, identify ad spend inefficiencies, evaluate customer lifetime value (CLV), and avoid duplicate events across channels.
Further potential benefits of a data clean room for marketing activities include granular audience segmentation, reach and frequency analytics, consumer journey analysis, performance measurement and optimization, A/B testing, and machine learning.
Compared to in-platform reporting, data clean rooms generally offer more data points and fields. Though more data doesn't necessarily guarantee better insights, it can offer new ways for advertisers to examine the effectiveness of user targeting when paying for ads.
Furthermore, data clean rooms offer advertisers opportunities to create tailored audiences which can be directly routed to a specified advertising platform. By leveraging advanced advertising data and relevant first-party data, businesses can optimize their ad targeting and maximize their media expenditure.
Retail & CPG Companies
E-commerce businesses can submit CRM data—unique identifiers such as email and postal addresses, mobile IDs, purchase date, SKUs (stock keeping units), etc—to a data clean room. Once each party has uploaded their ad exposure data and the unique identifiers used to create their campaign audiences, the overlap between new customers and those exposed to the campaign across each media channel can be accurately evaluated. From there, it is possible to calculate the percentage share of new customers each channel is generating.
Another use case for data clean rooms is for Consumer Packaged Goods (CPG) companies. Since they do not sell products to consumers directly, they have limited transaction data. They do, however, have first-party data from direct-to-consumer interactions, marketing, advertising, and loyalty programs.
Companies in the CPG sector can benefit by combining their own ad data with the Point of Sale transactional information of retailers they partner with. This aggregated data will help them comprehend how their marketing efforts are resulting in sales from the retailer. By combining the data, they can optimize their campaigns and offers for certain high-performing segments of the market via a retailer's media network.
Healthcare organizations can use secure data sharing and analysis of a wide range of datasets to enhance patient outcomes.
Financial services businesses can collaborate in data clean rooms to create effective fraud prevention and anti-money laundering tactics.
Data clean rooms can help travel enterprises to develop strategies to optimize loyalty, promotions, bookings, and pricing, in order to drive revenue growth at a franchise or segment level.
Data clean rooms can help businesses within the entertainment industry to better understand their customers. This can lead to improved engagement with audiences, as well as product and service innovation such as personalized customer experiences.
Use Case Examples
Increase Return on Investment (ROI) throughout a campaign's duration
A media conglomerate offers its advertising partners a one-of-a-kind, customized selection of their expansive brand portfolio. They use secure clean rooms to enable advertisers to link their first-party data to impression logs, audience segments, and user attributes for more precise consumer insights, whilst still conforming to current privacy regulations.
Redesign Customer Pathways
A major bike retailer streamlines its customer journey, using a data clean room to securely collect signals from a major classifieds site, which provides up-to-date behaviors, intent signals, and other perceptual qualities to form a more complete consumer profile. By amalgamating comprehensive data about their customers' interests and behaviors while concealing private specifics, the publisher empowers its advertising partners to offer customers better experiences, resulting in more successful campaigns.
Improve Campaign Insights
A global sports brand accesses user-level and impression-level data from Amazon Marketing Cloud. This reveals more detailed consumer insights which can make their campaigns more efficient and effective.
Key Benefits of Data Clean Rooms
Some of the key benefits advertisers can gain from using a data clean room include:
Automated privacy compliance
Data clean rooms can automate data privacy processes, reducing the need for manual solutions which can be a drain on company resources. Clean rooms have built-in privacy compliance support for GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), and CPRA (California Privacy Rights Act), and they anonymize user-level PII data so that it can be used for evaluation and analysis.
Improved marketing data strategy
- The ability to build more granular segmented audience groups for ad targeting with tailored content;
- Identification and analysis of audience trends from cohort-level data;
- Highly-optimized reach and frequency measurements;
- Opportunities to showcase user quality to prospective partners. Data clean rooms offer the perfect sandbox for publishers and advertisers to prove the value of their acquired users.
Today's consumers face many choices in terms of how they access, consume, and interact with content. This inevitably leads to fragmented digital footprints across various networks. In order to build a comprehensive view of their target consumers and what they need, companies now need to collaborate with their associates more than ever.
Data clean rooms enable varying degrees of collaboration between providers and potential users of the data. For example, companies can forge strategic first-party data marketing partnerships. This is something that can be risky, both legally and commercially, outside of a clean room.
By cross-referencing their databases and identifying the customers they have in common, companies can analyze anonymized reports and construct more accurate user profiles.
Most businesses have monetization strategies for their data and IP, or are looking to develop them. In light of today's privacy regulations, they need solutions that can generate revenue from their data without risking violations. This presents a chance for publishers and data vendors to combine data for large-scale analytics without direct access to the information.
The consumer pathway is intricate and often unpredictable. Their journey generally doesn't start with a brand's ad. For instance, a customer looking to purchase a new bike will most likely start their search by looking at online review sites. The top-of-funnel data that review sites collect would be of great use to a bike retailer. As the data contained within a data clean room is non-PII, review sites could construct a compliant third-party commodity for brands and e-commerce retailers to access.
Data Clean Room Challenges and Limitations
Of course, all emerging technologies present users with challenges as well as benefits. Here are some of the limitations existing data clean room solutions face.
- It is not yet possible to evaluate performance between publishers inside walled garden clean rooms. There may also be restrictions on how data can be utilized.
- The formats and processes for aggregating, anonymizing, and accessing data are inconsistent between providers due to a lack of standardization. This can sometimes lead to incompatibility issues when parties attempt to match datasets, requiring manual intervention and interpretation.
- Many companies are hesitant to share data due to security concerns such as cyberattacks and data breaches, or other potential risks that might damage their reputation.
- Aggregated data may be less accurate than ID-based data.
- First-party data-rich companies, such as direct-to-consumer brands, have a significant marketing edge over brands that have no direct customer relationships.
Types of Data Clean Rooms
Thanks to advances in cloud technology, distributed data clean rooms make it unnecessary to move data from one place to another, as the data can be hosted in the cloud. This arrangement permits every partner to manage their own information while also facilitating managed analytics with individual/multiple partners at the same time.
All data clean rooms protect privacy by obscuring individuals' user-level data and grouping them according to shared characteristics. However, there are three distinct types of data clean rooms. These are: Walled Gardens, Pure Players, and Multi-Platform (or Neutral Players).
Let's look at how these types of clean rooms differ from each other.
"Walled gardens" are closed platforms where the founding provider has far-reaching control over hardware, software, and/or content. They are used by major corporations to provide advertisers with matched data about how their ads are performing, and were first introduced by the three global ad media publishing giants: Google, Meta (Facebook) and Amazon.
When working with a data clean room from any of the walled gardens, ads' performance analysis is limited to that individual publisher's platform, as cross-platform insights are not available.
These platforms can also be unwieldy and complex for the average business person or marketer to use.
Google Ads Data Hub
It will come as no surprise that Google was the first company to create a data clean room for commercial markets. In 2017, with the new GDPR legislation looming, their Ads Data Hub was set up to collect first-party data from CRMs, CDPs and other sources within the Google ecosphere.
Google Ads Data Hub is an API for data analysis and processing. Built on Google Cloud and BigQuery infrastructure, ADH enables advertisers to upload their own first-party data to Google, so that they can aggregate marketing data, segment audiences and analyze reach.
Ads Data Hub is optimized for businesses that manage data across multiple Google platforms (such as Google Campaign Manager, Display & Video 360), Google Ads, and YouTube. Google's walled garden clean rooms work best for businesses who have large amounts of first-party data (e.g. CRM data) to contribute.
Facebook Advanced Analytics
Barely a month after Google launched their Ads Data Hub in 2017, Facebook introduced its own data clean room - Facebook Advanced Analytics - stating that its purpose was to share data with specific customers. FAA has since been discontinued, and advertisers now have to rely on alternative analytics tools, such as Meta Business Suite, Facebook Page Insights, and Hootsuite.
Amazon Marketing Cloud (AMC)
A little late to the party, Amazon launched Amazon Marketing Cloud in November 2019.
Amazon is the second-largest data clean room provider, offering large amounts of accurate and granular consumer data to marketers. Amazon Marketing Cloud also permits enterprises to collate consumer data from its auxiliary companies, such as Whole Foods and Twitch.
The AMC data clean room is built on Amazon Web Services. It offers an end-to-end solution and enables companies to better understand the impact of their cross-media investments by analyzing diverse anonymized datasets from multiple advertisers, as well as data gathered through Amazon Advertising activities. From this aggregated data, brands can generate accurate reports.
Several other Big Tech companies and independent vendors are now building their own private data clean rooms, where they can generate an omni-channel view of customer information from multiple partner datasets.
Beyond the media corporations, other large and mid-size companies are building their own omni-channel data clean rooms, where no PII data is stored and only aggregated data is shared (this does not include data from walled garden publishers). They are able to do this with the help of "Pure Players."
Pure Player agencies collect third-party data from publishers and ad networks, allowing advertisers to get an overview of their ad spend, as well as a comprehensive overview of their customers through aggregated data from multiple partner datasets.
Omni-channel data clean rooms are still relatively new, but they are growing rapidly in popularity due to ever-evolving user privacy regulations.
Pure Players comprise small-scale and mid-scale data clean room software providers, such as SnowFlake, InfoSum, Habu, Harbr, LiveRamp, and Decentriq. They serve media companies and brands with huge volumes of content and user data.
Omni-channel data clean rooms tend to be easier to use, with flexible architecture and—in the case of Snowflake—excellent access to a comprehensive partner ecosystem.
On the flip side, Pure Player clean rooms can be limited in their ability to generate granular first-party data, lack extensive integration options, and may require external infrastructure for some data ingestion tasks.
The Snowflake Data Cloud is a warehouse software platform built on a distributed data ecosystem, where multiple parties can securely share and consume data, while still being able to closely control the security of their own data. Snowflake's clean rooms can run with minimal IT admin support, and there is no need to install extra hardware or software.
Using Snowflake data clean rooms, brands and advertisers can access real-time information, enabling them to perform deeper analysis more efficiently.
Participating parties are able to share data for analysis in an environment where only appropriate parties can view it, without extracting it from its database or disclosing personal customer information.
Snowflake's Data Cloud is well-suited to media corporations, entertainment, and advertising industries, and is especially advantageous for retailers and CPG companies. The software has enabled several Big Tech companies—such as Disney, TikTok and Spotify—to build their own private data clean rooms
Disney Data Clean Room
In October 2021, Disney Advertising Sales unveiled their own clean room data solution, which was created in collaboration with Snowflake, InfoSum and Habu, and is powered by Disney Select and Disney Advertising's Audience Graph..
Advertisers can use the Disney data clean room to gain access to more than a thousand first-party segments, providing them with a gateway to explore Disney's extensive portfolio of brands.
Disney's clean room is cloud-agnostic—in other words, it is compatible with any cloud environment. This enables brands to more easily access valuable consumer insights from Disney's customer data.
Multi Platform or Neutral Players
When two data owners, such as a publisher and an advertiser, safely share their information by putting it into one secure, neutral clean room environment, that data clean room is a Neutral Player.
What's Next for Data Clean Rooms?
The growing demand for secure, privacy-first data sharing beyond that offered by walled gardens has led to a recent surge in the provision of data clean room solutions by neutral players. This trend looks set to accelerate during 2023 and beyond.
According to the technological research company Gartner, 80% of advertisers with media budgets in excess of $1 billion will be using data clean rooms by 2023. It is thought there are currently 250-500 data clean room deployments that are either active or in development.
Furthermore, analysts at IDC —a global marketing intelligence firm—predict that, by 2024, two thirds of G2000 companies will have partnered with external stakeholders to share data through data clean rooms.
US privacy laws are likely to become even more stringent. In 2023, new regulations restricting how businesses share consumer data will be implemented in California, Virginia and Colorado. And in Europe, strict data privacy regulations are having a significant impact on the way global advertisers can obtain information.
Should advertisers fail to comply with relevant data protection regulations when sharing audience data, they are likely to face hefty fines and other penalties, greatly increasing the risks associated with traditional methods of data collection.
How to choose the right data clean room for your business
The best data clean room for your business will depend on your goals and needs, but there are some basic factors and best practices all businesses should consider carefully beforehand, to maximize value.
Some of these include:
- The size of your business;
- Whether you will be using mobile, app, or web as your main channel;
- Your specific marketing needs;
- How your data is currently structured;
- The size and expertise of your internal tech team and the extent of other human resources;
- The volume, quality, and variety of first-party data you need, and will be contributing;
- Potential data collaboration partners, either internal or external;
- How scalable and flexible you need your data clean room to be;
- How intuitive the interface is;
- How secure the platform is;
- The current and future needs of your consumers.
Whichever clean room you choose, you should ensure that it can anticipate changes in consumer behavior.
Timely, automated audience activation and the ability to track consumer behavior in real time is also important for connecting with audiences quickly and effectively.
The best data clean rooms will provide invaluable insights to help you accurately evaluate the impact of your marketing efforts, and an understanding of how best to optimize them.
Gone are the days when tracking and reporting was an automatic background task; users must now provide their explicit consent for their data to be used for such purposes.
As Google Chrome phases out third-party cookies, giving consumers more control over how their personal data is collected and managed, businesses need to find new ways to gain meaningful marketing insights whilst also preserving user privacy.
Data clean rooms offer a secure method to balance the privacy needs of individuals with the marketing requirements of brands and advertisers. They are also constantly evolving and adapting to comply with new privacy regulations. Consequently, the market for data clean room providers is rapidly growing and accelerating.
By using data clean room technology, businesses can remain competitive, engaged with their customers, and compliant with privacy laws.
Call To Action
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