Data Clean Rooms (DCRs) are becoming a valuable tool for marketers, allowing them to match consumer data without sharing sensitive information. A data clean room provides advertisers with anonymised and aggregated user information, giving them access to non-personally identifiable information (non-PII) for effective targeting.
To make full use of DCRs, brands must have a solid first-party data strategy, usually through a customer data platform (CDP) that provides a single view of their customers. Data Clean Rooms can be used to match this data with multiple second or third-party data sources, allowing brands to build a holistic view of their customers in privacy compliant and data-secure way. This leads to a deeper understanding of consumer preferences and behaviours, lookalike audiences, exclusion lists, etc., resulting in improved targeting and ultimately reducing the risk of overwhelming consumers with ads.
DCRs can be used in conjunction with other techniques, such as Universal User IDs, SKAN, and contextual targeting. For instance, Disney announced their integration with The Trade Desk’s UID2, allowing advertisers to activate programmatically against Disney’s first-party data.
As a performance media agency, we understand the importance of data to drive successful campaigns while protecting consumer privacy. We typically see brands using Data Clean Rooms for one or more of the following reasons:
- Cross-device mapping
- Customer insights and segmentation
- Data privacy and security
- Audience measurement and attribution
- Fraud detection
The biggest challenge for Data Clean Rooms is cross-channel measurement, as brands will need multiple DCRs to access all available inventory and utilise their first-party data.
In the emerging DCR ecosystem, there are two categories: the walled gardens, such as Facebook and Google, and the multi-platform/ neutral parties, such as Infosum, that combine multiple publishers into one ecosystem. As highlighted above, to make the most of DCR technology, brands will need multiple DCR providers and a holistic measurement framework to optimise their media mix across channels.
This is where agency data and technology teams can play a crucial role by advising clients on the optimal mix of Data Clean Rooms for their brand and goals, and by putting in place a measurement framework that ensures efficiency and effectiveness. However, not all brands may need a Data Clean Room. For example, brands with a limited budget and no first-party data strategy may find the benefits limited.
In 2023, prioritising a first-party data and measurement strategy will be crucial for brands. While DCRs can help replace some of the signal loss due to privacy restrictions, a robust cross-channel measurement framework, which includes methods such as Media Mix Modelling and Matched Market Testing, is necessary for long-term success.
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