Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- Retail media networks are changing the traditional view of marketing as a cost center by turning it into a platform for revenue generation.
- The integration of data clean rooms (DCRs) into retail media networks supports scalable, privacy-complaint data usage, fostering trust and boosting advertiser engagement.
- Decentralized, federated data clean rooms are the future as they offer greater data privacy, security, and flexibility for collaborative analytics across diverse data sources.
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Retail media networks: Turning data to gold
Responsible usage of consumer data requires new ways to architect multi-party collaboration.
As retailers strive to deliver more relevant and engaging experiences, retail media networks (RMNs) serve as an apt conduit for them to monetize their customer data and ad inventory. This is shifting the traditional image of marketing and advertising from being cost drivers to revenue generators by offering brands targeted advertising opportunities across the shopper’s journey.
However, the rapidly increasing volume and complexity of customer data, the need for highly personalized and timely marketing strategies, and the need for responsible usage of consumer data will require new ways to architect multi-party collaboration between retailers and their business partner ecosystems. Retailers must create retail media networks that can handle vast amounts of data in real time, integrate diverse data sources, and provide actionable insights quickly. A key enabler in scaling retail media networks is the use of modern data clean rooms (DCRs), which allow retailers and advertisers to securely collaborate on data without compromising consumer privacy.
Data clean rooms are the fulcrum of retail media networks
Data clean rooms are the future of secure, privacy-compliant data collaboration for more precise targeting.
Retailers are investing in data clean rooms—a branch of privacy enhancing technologies (PETs)—for enriching customer data, using the data to deliver targeted advertising, and measuring the effectiveness of advertising campaigns, either through programmatic (automated) or non-programmatic (manual) methods. They also provide a secure and privacy-compliant platform for multi-party collaboration among retailers and retail media networks, media and publishing brands, advertisers, and other stakeholders. DCRs not only secure regulatory compliance but also foster ecosystem partnerships that are crucial to scale retail media networks. By using granular data for personalized interactions, data clean rooms help widen retailers’ ecosystem partnerships and deepen customer relationships. For example, a leading US retailer recently acquired a TV manufacturer to pursue monetization opportunities around automatic content recognition (ACR) and their shopping data. Similarly, a leading European retailer collaborated with a leading media ad agency and 13 other retail partners and media brands to tap advertising spend, highlighting the power of ecosystem partnerships in leveraging customer data and ad inventory.
Data clean rooms can disintermediate traditional advertiser-publisher value networks by enabling direct, secure data collaboration between stakeholders and new curated audience networks that can be used for more precise and effective ad targeting. They can enable self-serve ad platforms and curated audience networks that can ensure minimal data movement, maintain full data ownership, and anchor all activities through consent and data provenance.
We believe data clean rooms can hold the entire retail media network ecosystem together, enabling retailers to build, catalyze, or join multiple audience networks along with business partner ecosystems. This will help them drive more effective and efficient advertising campaigns while maintaining data privacy and security.
Traditional data clean rooms: Misfits in a broader data strategy
Centralized clean rooms limit interoperability and scalability.
Traditional data clean rooms pose several challenges. The centralized nature of these rooms involves moving the data to the clean room provider controlled by a third party (shifting the responsibility from the data owner to the third party), which raises significant privacy and security concerns. Additionally, centralized clean rooms limit interoperability and scalability.
- Data centralization: As data from multiple participants is centralized, there are concerns about centralizing vast amounts of data with a single entity, increasing vulnerability to breaches and compliance issues.
- Lack of audit trail: There is no consistent and concurrent record of consent and purpose for data use. This leads to non-compliance with GDPR (especially article 30, which mandates the record of processing activities (ROPA). This record should be passed back to the data owner to ensure transparency and accountability.
- Scalability: Only limited buy-side (data consumers) and sell-side (data owners) participants can be onboarded to traditional centralized data clean rooms as the infrastructure and operational processes struggle to keep up with the increased demand and complexity.
How decentralized and federated data clean rooms can help
Decentralized solutions ensure trust, transparency, and compliance while unlocking new revenue streams.
Decentralized and federated data clean rooms, powered by differential privacy, federated learning, and distributed ledger technologies, offer a robust solution to these challenges (see Table 1). Unlike centralized systems, decentralized clean rooms allow data to remain with the data owner while still allowing it to be accessible for joint data processing and analysis.
Here, data and control are distributed across multiple nodes or participants in the network; no single entity has full control over the entire system. This approach enhances data privacy by minimizing data movement and exposure, reducing the risk of breaches. Each business entity in the ecosystem can observe and control its data, fostering trust and collaboration. Confident that their data is fully protected, retailers are building expansive media ecosystems to include data from brands, data providers, media companies, and even other retailers.
Advantages of decentralized and federated data clean rooms include:
- Enhanced data privacy and security: By keeping data decentralized and leveraging privacy-enhancing technologies, they ensure data remains in its native location and is processed locally, ensuring that sensitive customer information is not exposed. This reduces the risk of data breaches and unauthorized access, maintaining consumer trust.
- Transparency: Purpose-built federated analytics provide insights, activation, and attribution without compromising privacy.
- Compliance: Distributed ledgers maintain a complete audit trail of processed data, ensuring compliance with data protection regulations like GDPR and California Consumer Privacy Act (CCPA).
- Improved collaboration and interoperability: Federated systems allow multiple organizations to collaborate without sharing raw data. They also facilitate seamless integration of data from various sources, making it easier to collaborate with different partners.
- High scalability: Decentralized and federated data clean rooms can support a larger and more diverse range of participants than traditional data clean rooms, while maintaining performance, security, and compliance.
- Enhanced customer engagement: Curated audience networks through data clean rooms enable highly relevant and personalized ad campaigns.
Table 1 highlights the differences between traditional data clean rooms and decentralized and federated DCRs powered by the distributed ledger technology.
Feature |
Traditional data clean room |
Decentralized and federated data clean room* |
Architecture |
|
|
Audience modelling |
Probabilistic metadata-based mapping (statistical replicas), resulting in lower resolution rates and diminishing accuracy over time |
Deterministic overlaps, dynamic management, and creation of private audience networks (such as retail media networks) |
Data layers |
Too many data layers; issues with reliability and audience identification, often mixing first-party and third-party data |
Focus on first-party data from partner ecosystem for better targeting; scalable ecosystem for data collaboration; and self-serve ad platforms for retailers |
Interoperability |
Limited; relies on third-party services; complex and costly integrations |
High with Web 3.0 readiness; privacy by design; and cost competitive compared to centralized models |
Table 1: Differences between traditional and decentralized data clean rooms
*Source: Aqilliz
Maximizing the potential of retail media networks
Investing in decentralized and federated data clean rooms is not just about data protection.
Retailers on a journey to establish a best-in-class retail media network cannot ignore multi-party collaboration around data with buy-side and sell-side partners. When every publisher wants to build a brand and every brand wants to become a publisher, data owners need to maintain full control of data. Investing in decentralized and federated data clean rooms is not just about data protection. It is about unlocking the true potential of retail media networks, driving growth, and ensuring long-term success in an increasingly data-driven world.
