Cookie Life - Part 4: Data Clean Rooms

As part of our series discussing the evolution of digital advertising triggered by the concerns about privacy on the web, we have published articles on how to target consumers without third-party cookies and how to start collecting first-party data. This piece aims to clarify how you can benefit from data clean rooms when you use your first-party data for advertising purposes.

Content:

• What are "data clean rooms" in the context of advertising? 

• How do data clean rooms work?

• Why set up a data clean room for your company?

• How to get started with a data clean room?

• What are distributed clean rooms?

• What are deficiencies of data clean rooms?

What are “data clean rooms” in the context of advertising?

The name “clean room” originates from manufacturing sites that require extreme protective measures to prevent contamination.

Foggy? Let start with some explanations: 

• Safe space: privacy-first closed-environment with pre-defined requirements to keep the data anonymized.
• Aggregated & Anonymized: individual first-party user information is #ed (read hashed for unrecognizable characters) and grouped by clusters. No cluster can be limited to an individual or a few profiles.
• Matching: level of relevance of the two distinct audiences, one versus the other.
• PII data: (Personally Identifiable Information) any information that can identify a user: email, phone number, name, etc.

How do data clean rooms work?

Let’s take an example! The most famous use case is the walled gardens: they have individual data from billions of users, but they only provide access to anonymized data in cohorts to the marketers willing to leverage it. Advertisers of all sizes can define target groups by selecting a set of variables, from location to interests. The profiles of users are anonymized, and the advertisers can see the delivery data but no individual identities (PII). A clean room is a “neutral“ storage in the sense that no personal data can be extracted by any stakeholders: in the case of walled gardens, the advertisers can leverage it without seeing the names and profiles of users, which solves the issue of user privacy.

If the advertiser has its own first-party data collection solution, it has the capacity to create its own cohorts. By building a proprietary solution or using partners available on the market, it can aggregate and anonymize its own data to match with other partners. No individual profile will ever be shared.

Why set up a data clean room for your company?

• Accessing data from partners without giving away your first-party data: Brands can protect the PII information they have been entrusted with by their customers and fans. Still, they can use it to find their alter-egos on the web (open or walled gardens) without any risks of breaching privacy laws.
• Scaling your first-party audience from the start: When you decide to ramp up your first-party data collection effort, optimising the strategy and acquiring a loyal customer base takes time. Clean rooms allow brands to multiply their audience very quickly.
• Assessing external partners based on the audience matching rate: Building the right partner synergies requires thorough scrutiny of both sides - the most efficient partners and audiences. 

How to get started with a data clean room?

Data clean rooms provide the right technical environment to apply your first-party data before interacting with other data sets. There are precise requirements to fulfill in order to connect CRM solutions to it. They need good quality data and good data governance to be used to the fullest, which may entail hiring data scientists and an analytics team to manage clean rooms. 

To get you started in your setup, please consider the following:

• If you have already set your own tech stack, which clean room is compatible with it? If you have not started your first-party data collection, you may need to consider choosing your tech stack and clean room accordingly.
• If you use the service of a network agency, which solution are they powered by? Most agencies have chosen a partner to power their media buys, it is worth checking with them.
• How competent is your current data science team in terms of leveraging the system? Clean rooms require specific efforts and their power needs technical skills to be properly unleashed.
• Do not underestimate the legal aspects of data privacy and data sharing. It takes time and should be anticipated as part of all partners’ negotiations. 

There are multiple providers in the market who help to set up a data clean room and analyze the various requirements and options available. We are there for you if you need first-hand help.

What are distributed clean rooms?

“Distributed”, or “decentralized” clean rooms means that different partners keep their databases on different platforms without the need to migrate, share or centralize the data - it can still be analyzed in a seamless way.

This concept is relatively new. For example, InfoSum lets advertisers load their CRM data into a personal “Bunker” whereas media owners can upload their addressable audience data into another “Bunker”. After that, multiple Bunkers can be virtually connected through anonymous mathematical representations, which enables audience matching and subsequent comparison of conversions to advertising exposure to attribute new conversions to specific channels and measure the efficiency of the campaign.

Image based on Merkel's graphic

This distributed setup, however, implies difficulties with governance and security and should be handled professionally.

What are the deficiencies of data clean rooms?

Most data clean rooms nowadays are still limited to one specific platform, which means marketers cannot gain a full picture of a customer's journey or trace cross-platform attribution.

For example, the walled gardens only allow the use of their data on their own platform, thus forcing advertisers to focus their budgets on respective channels. But that may change in the future - and we will make sure we keep you posted. Stay tuned! 

Sources:

Special thanks to Lily T. who provided me with insider tips on clean rooms considerations.

• Can Data Clean Rooms Be The Answer For Privacy-Safe Marketing? | forbes.com: https://www.forbes.com/sites/forbescommunicationscouncil/2021/07/06/can-data-clean-rooms-be-the-answer-for-privacy-safe-marketing/?sh=6f483ce56f56
• Data Clean Rooms Will Play A Key Role In A Cookieless World | AdExchanger: https://www.adexchanger.com/data-driven-thinking/data-clean-rooms-will-play-a-key-role-in-a-cookieless-world
• How data clean rooms unlock omnichannel measurement | InfoSum Blog: https://www.infosum.com/blog/how-data-clean-rooms-unlock-omnichannel-measurement
• Are Data-Clean Rooms Good in a World without Cookies? | clickguard.com: https://www.clickguard.com/ppc-news/data-clean-rooms
• What data clean rooms mean for the privacy-first internet | The Drum: https://www.thedrum.com/opinion/2021/08/20/what-data-clean-rooms-mean-the-privacy-first-internet
• Walled gardens, data clean rooms and third party cookies - how campaign measurement is poised to change in 2021 and beyond! | Louder: https://louder.com.au/2021/02/09/how-campaign-measurement-is-poised-to-change-in-2021

Some data clean rooms are available on the market:

• Distributed data clean rooms - Epsilon, Merkle, Infosum, Measured, Snowflake, Neustar
• Walled gardens clean rooms- Facebook, Google, Amazon