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.
The name “clean room” originates from manufacturing sites that require extreme protective measures to prevent contamination.
Foggy? Let start with some explanations:
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.
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:
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.
“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.
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!
Special thanks to Lily T. who provided me with insider tips on clean rooms considerations.
Some data clean rooms are available on the market: