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If you&#;ve been paying attention to the unfolding of data privacy regulations and technology changes, you&#;ve likely encountered the term &#;data clean room&#; (DCR) at least once. The growing buzz around this concept tells you just how important the new solution is in today&#;s digital landscape.
And how can you tell that a new technology category is getting momentum and attention? The answer is simple: When it earns its very own acronym, of course! So welcome to the world of data clean rooms, where privacy, collaboration and innovation converge.
Let&#;s take a quick step back to better understand how we arrived at data clean rooms. Data clean rooms have quickly become a focal point in discussions about data privacy and collaboration. But what exactly is driving this surge in interest? The answer lies in the evolving dynamics of data usage, privacy concerns and of course &#; third-party cookies. As brands grapple with a confusing and quickly-changing customer data landscape, they need ways to make the most of their data &#; with data clean rooms offering a compelling solution.
Let&#;s take a moment to revisit the journey that brought us to the era of data clean rooms. Over the past few years, we have witnessed a shift in how customer data is handled, largely driven by growing customer data privacy concerns. This shift has triggered a cascade of events that reshaped the digital landscape:
We could keep going, but the event that really created the highest reaction on the market is Google&#;s announcement in to also remove access to third-party cookies from Chrome. Third-party cookies are the main currency exchange in the advertising industry. Despite its flaws, this is cheap and provides scale. And while the recent news that third-party cookies aren&#;t going away has given some advertisers relief, the truth is that solutions built on first-party data will continue to gain traction.
So, what&#;s next for advertisers, agencies, publishers, and tech vendors who have relied on third-party cookies for so long?
The industry agrees: the best alternative for brands is to focus on their first-party data strategy. Provide value to the consumer in exchange for their (consented) data.
Some industries &#; such as retailers (thanks to customer transactions) and premium media (thanks to their early subscription and paywall strategy) &#; are leading the pack in getting access to first-party data. But this is not the case for everybody: most CPG and automotive brands don&#;t have the same customer data maturity or opportunity.
Across the board, first-party data won&#;t provide the same scale known with third-party data. So what is the next best alternative? Second-party data.
Second-party data refers to first-party data collected by another brand. It&#;s incredibly valuable because it represents someone else&#;s first-party data, which is typically of high quality and relevance. However, there are significant privacy concerns associated with sharing such data, and brands are understandably reluctant to part with their most valuable assets.
So, how can brands safely access and utilize second-party data?
In , Google led another major industry change: it &#;quietly&#; launched a new product called Ads Data Hub. At the time, this beta product was presented as a &#;next generation insights and reporting&#; tool.
It was the inception of data clean room technology, but the term was not coined yet. Google announced that it would stop sharing log-level data with advertisers. At least the IDs identifying an individual would no longer be provided.
The challenge? Without access to this data, advertisers would not be able run advanced analytics and reporting on their advertising campaigns on Google properties such as YouTube.
The solution? Ads Data Hub would provide access to the raw data for insights and reporting &#; without allowing advertisers to export this information in their own tools. Walled Gardens were born, and they don&#;t give away the valuable collected data anymore, in the name of privacy.
So what is a data clean room (DCR)? In a nutshell, it&#;s a potluck &#; but not the traditional one you are familiar with.
First of all, to access the room in which you will taste other people&#;s food, you need to show your credentials. The entrance is safely guarded 24/7. Then, the room itself is dark so you can&#;t actually see anybody&#;s food. And you can&#;t leave the room with the food either: it&#;s meant to be tasted &#;on-site.&#;
In essence, a data clean room is a secure environment for data collaboration. Let&#;s dive deeper into the key characteristics of data clean rooms:
While a couple of customer data platform (CDP) vendors announced clean room technology or capabilities in , the purpose and technical capabilities required are different for a CDP and a data clean room.
A CDP&#;s primary focus is on collecting, organizing, and activating first-party data sets to deliver personalized customer experiences. It&#;s designed to help business teams take action on customer data across the entire customer lifecycle.
On the other hand, a data clean room is specifically designed to enable collaboration between two parties using each other&#;s first-party data. It&#;s a secure environment where data can be combined, analyzed, and used for insights without compromising privacy.
While CDPs and data clean rooms serve different purposes, they can complement each other within an organization&#;s tech stack. Together, they offer a powerful combination of data management, collaboration, and activation capabilities.
A Side-by-Side Comparison: Customer Data Platform vs. Data Clean Room:
Customer Data PlatformData Clean RoomOVERVIEWPurposeEnables business teams to take action on customer data via the delivery of personalized customer experiences.Enables data collaboration between two or more parties.Primary UsersIT, Analytics, Advertising, Marketing, Marketing OpsAnalytics, Advertising, MarketingData Type&#; Any customer data, PII and non-PIIFuture-proof Your Acquisition Strategy
Manage data governance, costs and performance to drive growth through extraordinary CX in an ever-evolving environment.
As the digital landscape continues to evolve, brands must focus on managing data governance, controlling costs, and optimizing performance to drive growth through exceptional customer experiences (CX). In this ever-changing environment, data clean rooms offer a future-proof solution for safe and effective data collaboration.
Not long after Google, Meta (Facebook at the time) deployed its own clean room technology. These first clean rooms were really built for the biggest advertisers on Google and Facebook properties, the cost made them out of reach for smaller brands.
The data collaboration process itself was run by data engineers and analysts using SQL queries. No marketer would be able to access an interface and discover the insights themselves at the time.
Amazon was even quieter. They were suspected of developing a similar technology, but no public documentation or information was available.
The initial use cases for data clean rooms were focused on improving attribution and measurement for ad buyers. As we saw with Google suppressing the option to export raw logs with IDs, an alternative was required to understand the performance of advertising campaigns.
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With the announcement of third-party cookie deprecation, we are seeing the emergence of new use cases for DCRs. They are no longer limited to attribution and measurement post-campaign execution.
This technology can help access insights from audiences, match data between advertisers and other parties including media owners, data owners, and identity providers. Improved targeting is enabled by allowing an advertiser to activate matched data set directly to an addressable channel &#; when the data clean room provides an integration with that channel &#; or export owned data back to another system of the brand.
To start any collaboration, you need at least two parties. Typically, a DCR will be used for a collaboration Brand &#; Brand, Brand &#; Publisher or Business Unit &#; Business Unit.
Organizations are building and growing their partnership ecosystem and we are seeing synergies between industries and/or markets. Some of these partnerships could be directly visible to the end customer, such as what we&#;ve seen with Delta and Lyft and Chipotle and e.l.f.
More generally, we expect advertisers to create more direct partnerships with publishers and CPG brands to collaborate with retailers selling their products.
Once two parties have found an agreement, legal and technology to use for the collaboration, the process will happen in three steps:
Data clean rooms offer numerous benefits for businesses across various industries, making them an essential component of modern data strategies. Here are some key advantages:
The adoption of data clean rooms within an organization is still generating a lot of debate. The cost of entry is particularly high, especially during a time of uncertainty. And we are not talking about the technology cost alone, as the first barrier will be a legal and process one.
How do you start with creating these relations directly with brands and publishers? How do you ensure that privacy laws are respected? That consumer consent is collected and respected? Once these matters are clarified, which data clean room(s) should my organization even adopt?
All these questions and more will have to be answered. But as we shared earlier, first-party data in itself won&#;t provide the scale necessary moving forward. So organizations need to explore partnerships and data collaboration.
&#;As third-party cookies and other unstable identifiers become obsolete, businesses will need a new way to reach their customers while protecting and prioritizing their privacy. was very much the year of clean room curiosity, but shaped up to be the year of clean room construction, with innovative organizations now building new first-party solutions on top of them,&#; said Valerie Mercurio, VP, Business Development at InfoSum.
&#; was very much the year of clean room curiosity, but is already shaping up to be the year of clean room construction,&#; said Valerie Mercurio, VP, Business Development at InfoSum.
Testing data clean rooms early is the best option to be prepared for the future, and as Mercurio is adding, &#;data clean rooms are no longer a trend, companies have realized that they are a must-have in the first-party era. As a foundational element of an organization&#;s tech stack, they are a significant opportunity to further differentiate from competitors.&#;
&#;Today, data clean rooms are enabling advertisers to collaborate quickly, easily and effectively with their media and data partners. We&#;ve seen incredible results among our clients and partners &#; reducing costs, improving return on ad spend and increasing conversions upwards of 20%,&#; Mercurio noted.
ActionIQ integrates with the leading data clean room solutions on the market. Learn more about how ActionIQ and InfoSum can help with your data collaboration and data clean room use cases and get in touch with our experts.
A data clean room is a secure environment for data collaboration. It provides an environment to host privacy-compliant, sensitive data sets to collaborate between two or more parties for better data insights and decision-making.
Data clean rooms ensure that sensitive information is protected, reducing the risk of data breaches and compliance issues.By enabling secure data sharing between partners, data clean rooms facilitate better collaboration, leading to more effective marketing strategies and customer insights.
A brand first makes its first-party data available to the clean room from its systems. A common scenario is to leverage a CDP integrated with the clean room to share these first-party data sets. Then a common identifier is required. No matter which option is chosen, data will be de-identified using various techniques developed by the DCR, ensuring that this process cannot be reverted. The result &#; and not the raw data ingested &#; is used to match data sets.
Now the data can be used for planned activities, from audience segmentation and insights to activation and measurement. A data clean room often offers direct integration with paid media channels, but the activation can be orchestrated back into the system of choice, such as a CDP, to build personalized customer experiences.
It was working well - advertising at scale on the internet was a solved problem, but then along came tighter privacy legislation, Apple announced changing the mobile tracking world with App Tracking Transparency, and soon 3rd-party cookies are going away. How are marketers supposed to do proper campaign measurement and optimization if they can&#;t gather data about customers and get acquainted with how they are responding to ads?
Commerce over the internet relies on the ability of organizations to track users across domains. However new privacy laws and the deprecation of third-party cookies have led to increased signal loss impacting marketers&#; ability to efficiently plan and measure their campaigns. How do we maintain end-user privacy while preserving the ability to understand our customers and their journey?
Let&#;s first look at the current architectural landscape:
Companies use a Data Management Platform (DMP) to help assemble all of the data they gather (often third-party* data). DMPs rely heavily on third-party cookies for tracking users across multiple websites. Their ability to provide a holistic view of a user's journey and interests across the web is already being impacted by the depreciation of third-party cookies.
Since the announcement we&#;ve seen that many are moving to a Customer Data Platform (CDP), driven by 1st-party* data. First-party data in a CDP is arguably the most privacy-friendly way to operate. CDPs are designed to centralize first-party data from a variety of sources, including websites, CRM systems, mobile apps, and more. In a world moving away from third-party cookies, first-party data becomes even more valuable, and CDPs are well-suited to manage it.
However, we simply can&#;t learn everything we need to know about our customers through our direct interactions- some use cases require a view of the customer from an external perspective. This gap can be addressed by adding 2nd-party data* to the strategy&#; and this is where our initial problem statement enters the fray. How can we analyze data across organizational lines but maintain privacy?
*CALLOUT: For a refresher on first, second, and third-party classifications of data, check out this blog.
The problem is that we need to share data without exposing the customer&#;s identity; but how? So engineers did what engineers always do - they created a way to meet the need! The data clean room has emerged as the answer to this riddle.
A data clean room is a secure environment where advertisers, agencies, and publishers safely analyze and share data without revealing any personally identifiable information (PII), allowing them to adhere to privacy laws and regulations. They incorporate Privacy Enhancing Technologies (PETs) like encryption and differential privacy to protect individual identities and data misuse. Additionally, DCRs enforce strict access and privacy controls, ensuring users only access necessary data and resources for specific tasks. Data clean rooms are typically used by businesses to perform cross-brand marketing collaboration, measure the effectiveness of campaigns on advertising platforms, and gain insights into customer behaviors and attributes.
By no means is this list comprehensive, but here are some common uses and benefits of clean rooms that organizations employ:
Campaign Measurement, Planning & Attribution: Clean rooms enable advertisers to gauge the efficacy of their campaigns. They can determine the overlap between conversions on their site and impressions on publisher and walled garden platforms, to drive a holistic understanding of audience exposure, impact, and ad performance. This leads to a more accurate measurement of return on advertising spend (ROAS). Publishers can allow advertisers to answer questions like, how will a certain audience ad campaign perform on this platform, or what was the performance of an executed campaign?
Profile Enrichment: By collaborating in a clean room environment, companies can augment their existing customer data with information sourced from partner collaboration. For instance, a credit card company could partner with a data analytics firm to gain insights into user shopping behaviors, thereby refining their targeted marketing campaigns.
First-party Data Partnerships: Data clean rooms catalyze collaborations, enabling brands to delve into new segments and uncover opportunities for brand growth. Clean rooms empower marketers to create precise look-alike models, fostering segmentation based on consumer behavior signals from partners. Two or more brands can assess the performance of their joint campaigns via 1P customer transactional data (LTV, purchase history, etc.) Imagine an airline partnering with hotels, or CPGs partnering with retail companies. All of these brands can leverage insights from collaborations to power a connected experience across their platforms and issue personalized offers.
The relationship between a CDP and a data clean room is symbiotic. A CDP is essential for collecting and feeding organized, high-quality first-party data into the data clean room, forming the basis for deeper, multi-party analysis. In turn, the aggregated insights derived from the data clean room can be channeled back into the CDP, enriching customer profiles and enhancing data-driven activation strategies.
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