In plain English: What are data clean rooms? And why should advertisers consider adding them to their tech stack?
By: Abhishek Chakraborty, Product Manager, MiQ
A blog series explaining some of the concepts, processes and technologies we need to do our jobs – in plain English.
(NB. There’s a glossary of terms at the bottom of this post for explanations of some of the technical terms marked with *).
A great way to understand the concept of clean rooms is actually found in the opening credits of The Simpsons. Think of the way Homer handles the radioactive material through a wall with gloves. He can move and manipulate the material, but he can’t take it out of the sealed environment. A data clean room is very similar to this (without any radioactive threats) and allows for privacy-compliant data science*.
So, what are data clean rooms?
A data clean room is a platform where advertisers and agencies can perform analysis and data science on any dataset in an aggregated way (meaning, at a group level rather than data that can be linked to specific individuals), and without moving it out of the platform. One data policy will cover all use cases.
Data has been the fuel for driving effective programmatic advertising* since the industry’s start. In fact, data has been dubbed the ‘new oil’ in the digital industry, due to its high value, but also because it needs to be expertly refined in order to be useful.
Read our Identity ebook, ‘What activation looks like in a world without cookies’, to learn more about data cleanrooms.
Why can advertisers use clean rooms in their tech stack?
There are three main uses for clean rooms for marketers.
Marketers need to measure their campaigns. But, the main thing you need for measurement is data – from campaign performance, sales data, and so on. Without cookies, getting access to this kind of data is becoming more of a challenge, as brands and platforms both have to rethink how much data is safe to share without compromising user privacy.
Clean rooms are a great way of getting around this problem. In data clean rooms, advertisers don’t need to share their data with measurement partners, who drive the analytical heavy lifting on measurement and attribution. And, likewise, other players in the ecosystem, like publishers, DSPs, SSPs and others, don’t need to share their data with brands so they can understand campaign performance. Everyone can put their data in the ulta-secure clean room, the measurement experts can do their thing, get all the insights they need, and then everyone gets their untouched data back again.
2) Brand collaboration
Clean rooms are a great way for brands to collaborate with each other, connecting their first-party data to find insights for mutual benefit.
It’s particularly exciting for brands in adjacent spaces who have a shared but non-competitive audience. Think about a food delivery app and a fast-food restaurant, or an airline and a car rental company.
Without clean rooms, there’s no way brands would share their first-party data. But in a clean room situation, both the brands can compare their audiences and find insights without ever compromising security.
3) Comparing multiple datasets
Campaign footprints from any demand-side platform and ad-server foot-prints or log-level data are now available in data clean rooms like Ads Data Hub and Amazon Marketing Cloud. These datasets are hidden gems for an advertiser to unlock the behavioral trends of online users, showing things like device type, websites, browsers, geo, and much more.
Using these contextual learnings works wonders for advertisers when they are layered on top of first-party data, as they allow them to build a much deeper view of their customers. Data clean rooms make this kind of layering possible in a privacy-compliant way.
Data clean rooms are the new powerhouse for driving data science-driven campaigns in a privacy-compliant way. As clean rooms become more and more commonplace, we will cover the topic in more depth in upcoming blogs.
Tech stack: The technology stack is a combination of programming languages, frameworks, and tools that developers use to build a web or mobile app. The two main components of any app are client-side (front-end) and server-side (back-end). Each layer of the application is built atop the one below, thus creating a stack.
Data science: Data science means preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Analytic applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed insights.
Programmatic advertising: This is the automated buying and selling of online advertising. Targeting tactics are used to segment audiences using data so that advertisers only pay for ads delivered to the right people at the right time, and depend less on the “spray and pray” method of digital advertising.
Contextual signals: When you’re searching on mobile, Google will now factor in what they call “contextual signals” – time of day, date of search, and the location of you and your mobile phone to allow for even better personalization. They will also keep track of your past searches.
Closed web: A closed platform, walled garden, or closed ecosystem is a software system where the carrier or service provider has control over applications, content, and/or media, and restricts convenient access to non-approved applicants or content.
Open Web: The Open Web Platform is the collection of open technologies which enable the web. Using the Open Web Platform, everyone has the right to implement a software component of the web without requiring any approvals or waiving license fees.