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Data Clean Room: What It Is & Why It Matters in a Cookieless World

This is curated content from the best of the best blogs around IMHO, indispersed with a few of my own.

Summary: What is data clean room? All data clean rooms have stringent privacy protections that prevent organisations from viewing or retrieving any customer-level data. This article originally found on Search Engine Journal outlines the basics of Data Clean Room and why it is important.


Data clean rooms are key for measuring advertising effectiveness in a soon-to-be cookieless world. Here’s everything you need to know.

With Google joining Apple (Safari) and Mozilla (Firefox) in ending support of third-party cookies in Chrome, we are in the process of losing the accuracy of reporting we once relied upon to measure return on advertising investment.

We’re facing a huge advertising attribution challenge for which there just isn’t a simple solution.

PPC advertising is known for its superior measurability vs. traditional advertising.

So how do we prove value in a world where the accuracy of our reports will be severely impacted by enhanced user privacy regulations?

These are the issues we’re already encountering with the inaccuracy of Facebook Ad tracking on iOS 14 devices.

Advertisers will still have access to and can make use of their own first-party data, but matching data across networks will prove much more difficult. It will rely on a deeper analysis in each platform rather than the cross-platform view enabled by third-party cookie tracking.

This is where the concept of data clean rooms comes in.


What is a Data Clean Room?

A data clean room is a piece of software that enables advertisers and brands to match user-level data without actually sharing any PII/raw data with one another. Major advertising platforms like Facebook, Amazon, and Google use data clean rooms to provide advertisers with matched data on the performance of their ads on their platforms.

Data clean room visualization.

All data clean rooms have extremely strict privacy controls which do not allow businesses to view or pull any customer-level data.

The benefit to advertisers is a much clearer picture of advertising performance within each platform. But it does rely on a solid bank of first-party data in the first place in order to run any significant matching with platform data.

For example, Google’s Ads Data Hub allows you to analyse paid media performance and upload your own first-party data to Google. This allows you to segment your own audiences, analyse reach and frequency and test different attribution models.

There’s one major issue with this approach. Although many platforms claim to be able to offer a cross-channel clean room solution, it’s hard to see how this would be the case given the strict privacy controls in place by Google and other platforms.

This is fine if a brand wants to increase spend within each platform, but still creates a challenge in cross-network attribution.


An Example: Google Ads Data Hub

Google’s Ads Data Hub is expected to be a future-proof solution for Google-specific advertising (Search, Display, YouTube, Shopping) measurement, campaign insights, and audience activation.

Ads Data Hub is most effective when running multiple Google platforms, and if you have a substantial amount of first-party data to bring to the party (e.g. CRM data).
Google ads data hub.

Ads Data Hub is essentially an API. It links two BigQuery projects – your own and Google’s.

The Google project stores log data you can’t get elsewhere because of GDPR rules. The other project should store all of your marketing performance data (online and offline) from Google Analytics, CRM, or other offline sources.


Data Clean Room Challenges and Limitations

First-party data (the kind used to power data clean rooms) comes with fewer headaches around complying with privacy regulations and managing user consent.

But first-party data is also much harder to get than third-party cookie data.

This means that the “walled gardens” such as Google, Facebook, and Amazon who have access to the largest bank of customer data will benefit from being able to provide advertisers with enhanced measurement solutions.

Also, brands that have access to lots of consumer data – e.g., direct-to-consumer brands – would gain a marketing advantage over brands that have no direct relationships with consumers.

Most data clean rooms today only work for a single platform (e.g., Google or Facebook) and cannot be combined with other data clean rooms.

If you advertise across multiple platforms, you will find this limiting since you cannot join the data to build a full view of the customer journey without manually stitching the insights together.

Before marketers dive into a specific clean room platform, the first consideration should be how much of your ad spend is focused on each network.

For example, if the majority of digital spend is focused on Facebook or other non-Google platforms, then it’s probably not worth investing in exploring Google Ads Data Hub.


Alternatives to Data Clean Rooms

Data clean rooms are just one way of overcoming the challenges we face with the loss of third-party cookies, but there are other solutions.

Two other notable alternatives being discussed right now are:

Browser-based tracking.

Google claims its Federated Learning of Cohorts (FLoC) inside Chrome is 95% as effective as third-party cookies for ad targeting and measurement.

Essentially, this will hide users’ identities in large, anonymous groups, which many are skeptical about.

To be clear, FLoCs aren’t clean rooms – but they do anonymise user-level data and cluster audiences based on shared attributes.


Universal IDs.

Universal user IDs are an alternative to the browser-based tracking option presented in Google’s privacy sandbox. These would be used across all major ad platforms but anonymised, so advertisers wouldn’t see a person’s email address or personal data.

In theory, the use of universal IDs would make cross-network attribution easier for advertisers. as the universal ID tag would effectively replicate the functionality of third-party cookies.


What Will the Future Hold?

Tracking and reporting is no longer the background task that we used to take for granted, it now requires explicit user consent. This transition requires companies to ask users for their consent to give up their data more often.

It requires users to click through more obtrusive privacy pop-ups. It will probably create more friction for users, at least in the short term, but this is the trade-off for a free and open web.

Beyond the “walled gardens” such as Google, some companies are working to build omnichannel data clean rooms. No PII data is stored and only aggregated data is shared back to the business.

While this would certainly help with the challenge of cross-platform attribution, there will likely be a mismatch between the data provided between different ad platforms that will require manual interpretation.

Regardless of the “clean room” technology that will enable this data matching, there is a need to invest in building up your own first-party data now to enable any cross-referencing of data with advertising platforms or ad tech providers.

This requires creating and trading value for deep data on your customers.



Citation: If you would like to find out more about the source of this article – Data Clean Room: What It Is & Why It Matters in a Cookieless World, see here.


Mike Raybone Marketing Strategy Consultant

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