As we advance towards 2024, the European Union (EU) continues to strengthen its stance on privacy. The European Data Protection Board (EDPB) is prompting companies like Meta to halt their current behavioral and contextual advertising techniques due to privacy concerns regarding personal data collection. This trend towards prioritizing consumer privacy has been an ongoing effort by the EU, creating complex challenges for marketers who seek precision in audience targeting.
In this climate, data clean rooms have become crucial to marketing strategy. We will showcase their importance through several case studies. For further details on the advantages of data clean rooms, please refer to our extensive article on the subject.
Table of Contents:
Case Study: Qteam, Bridgestone and Vroom.be
Insights from Meta: First-Party Data Utilization
Case Study: Renault and Axel Springer Partnership
Qteam, the leading tyre expert with over 50 locations in Belgium together with Bridestone, wanted to target the owners of actually, car owners. In collaboration with Vroom.be, an automotive news platform with over 600,000 monthly visitors, they focused on a specific user group: those who frequently engaged with Vroom's newsletter over the last three months. This strategy pinpointed a segment of 50,000 users.
This segment was anonymized through the wehave data clean room service and activated across platforms like Meta and Google to maximize audience reach. The effectiveness of data clean room segmentation was evident when compared to conventional targeting and with a lookalike audience of those 50.000 Readers.
As we are writing this, the very first results are rolling in.
The Vroom data test resulted in 32% better click-through rates, 28% better cost-per-click, and 35% more applications for their win a tyre set campaign, all within the same advertising budget. The lookalike audience performed 17% better in general.
Meta's research into the use of first-party data in advertising has highlighted its potential.
Integrating first-party data for targeting from different sources, particularly when combined with lookalike audiences, has proven to improve advertising performance significantly.
A 27% boost in conversion rates
An 18% decrease in customer acquisition cost (CAC)
A 23% uplift in customer satisfaction
A 20% increase in per-customer spending
Deloitte’s paper states that if you use a data clean room, you should do it correctly and make full use of it. It’s works the same as working with a CDP or CRM, if you are not using it properly, the results will be accordingly. Create great strategies and thrive.
Renault and Axel Springer's collaboration is a testament to the power of first-party data. Two agencies representing Renault compared the traditional use of third-party cookies against a first-party data strategy. By partnering with Axel Springer and their substantial first-party data, Renault significantly enhanced their advertising results.
The collaboration focused on identifying overlaps in customer data to create a new segment for lookalike audiences, which led to:
An 18% increase in conversions
A 38% enhancement in ad targeting accuracy
A 15% decrease in cost per action
A 19% reduction in cost per click
The results are in. First party data will be the best performing strategy for the coming years. Don't be the last one to join the party. You want to start leveraging first party data strategies today rather than tomorrow. Delaying these tactics will only result in losses.
Hey, you can read all day long about data clean rooms but why don’t you just test one for free? (no credit card needed)
Go to wehave.io, create an account and start using it. The only free data clean room on the market.
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