DCR out, DCP in: Why retail media can’t deliver on its promise with data clean rooms alone

In the dynamic world of data-driven marketing, the standalone data clean room (DCR) is being overshadowed by a new star (and acronym – always industry favorites) —the data collaboration platform (DCP).

Once known as the ultimate solution for addressing siloed data, privacy regulations, and diminishing identifiers, standalone clean rooms promised secure and private data sharing between partners. Yet, their limitations—high costs, engineer-heavy operations, and limited scalability—have exposed their shortcomings.

Today, clean rooms are finding their true potential not as independent entities but as integrated features within broader platforms. These platforms are redefining how marketers collaborate, unlocking scalable insights, and enabling actionable strategies to turn overlapping data into meaningful outcomes. This shift signals a pivotal moment for the industry: a move from isolated solutions to interconnected ecosystems that drive real value.

In this blog we’ll review some of the key characteristics that differentiate data collaboration platforms from data clean rooms. I’ll also explain that although both are important for advertisers and commerce media networks, a DCR on its own is not a sufficient solution in today’s landscape.

Data Clean Room → Data Collaboration Platform

A data clean room (DCR) provides businesses with a secure environment for seamless data collaboration. Within these protected spaces, multiple entities can effectively merge sensitive data without undermining privacy or security.

However, simply combining data within a data clean room lacks substantial value for marketers without additional applications. In fact, DCR technology has been largely commoditized and has transitioned into a more evolved state, marked by its transformation into a Data Collaboration Platform (DCP).

Unlike the traditional DCR setup, a DCP not only facilitates the secure merging of 1st party data (1PD) but also acts as a centralized hub for data collaboration processes. This includes functionalities such as audience creation, campaign activation across diverse channels supporting both endemic and non-endemic scenarios, and most significantly, the ability to measure campaign effectiveness.

Why measurement still falls short

In recent months, we have engaged with over 200 advertisers and commerce media networks (CMNs). According to Nielsen, while all advertisers recognize the potential of personalized campaigns tailored to well-segmented audiences, a significant 84% of brands prioritize measurement as their top consideration when contemplating a major shift in budget allocation from existing channels to CMNs.

Although the necessity for measurement is straightforward, it’s important to dive deeper into the relevant challenges. The CMN attribution model resembles a three-legged stool, necessitating engagement data (views), conversion data (transactions), and a mapping mechanism to connect various touchpoints.

It’s also worth highlighting that brands often seek specific event or SKU-level attributions rather than broader attributions.

Adding another layer of complexity, campaigns may operate across multiple platforms including mobile, web, open web, CTV, etc., and can run either on-site (utilizing CMN digital assets) or off-site. This multi-platform  landscape introduces another challenge as off-site campaigns can run on either the advertiser or the CMN account, which makes measuring campaign results even more complex.

1. Total Cost of Ownership – Most DCPs require their customers to manually upload data to provide engagement or conversion insights, increasing operational complexity and costs.
2. Fragmented Use Cases – There are at least 12 distinct use cases where engagement and conversion data are owned by different parties across various platforms. Most DCPs fail to offer a comprehensive solution that addresses all these scenarios.
3. Tamper-Proof Data – Since advertisers pay CMNs based on performance, ensuring data integrity is crucial. However, most DCPs cannot provide a tamper-proof mechanism for attribution data through automated methods like SDKs or APIs. Instead, they rely on file exchanges, which are prone to tampering, human error, or inaccuracies.

Self service audience building is the secret sauce

Despite holding exclusive and highly valuable data on consumer purchasing preferences and behaviors, CMNs encounter various obstacles when attempting to provide this information to advertisers in the form of pre-built audiences:

• Advertisers’ Business Logic – Each advertiser possesses specific industry knowledge and tailors campaigns according to distinct objectives. For instance, a financial services company may not understand how to create an audience for a consumer packaged goods (CPG) brand, just as US retailers may not grasp what European fashion brands find valuable within their 1PD.
• Data Consumption -All businesses collect data to serve their own business needs. CMNs are not different in this aspect. However, when a CMN needs to make this data accessible to advertisers, they now run into a number of challenges:
  • Privacy restrictions: as CMNs don’t want to expose one brand to the purchasing data of another brand.
  • Lacking or no UI: Advertisers must be able to segment this data in a simple user interface without any coding
  • Unuseful data: CMNs must be able to group the data in a useful manner. For example, a CMN has data about how much customers spend in a relevant category, while the advertiser wants to build more granular audiences, e.g. for consumers who spend more than a certain amount a year and have purchased in the last 3 months.
• Scale and Maintenance – Developing and maintaining specialized audiences for different advertisers, continuously updating these datasets without proper infrastructure, is an arduous and costly process for CMNs. Customizing these audiences to optimize campaign performance for each advertiser further compounds the challenge.

A unified solution delivers a 1.5x uplift

The silver lining is that a unified solution addresses these problems, allowing CMNs to upload data once to help brands independently construct their audiences. Implementing this approach not only resolves existing challenges but also boosts campaign results by 1.5 times compared to audiences generated solely by the CMN, according to an analysis we’ve performed for a number of AppsFlyer clients.

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Innovating towards an interoperable Data Clean Room

When discussing the evolution of DCRs, it is vital to recognize that not all are created equal. This discrepancy largely stems from the fact that various clean rooms were tailored to different user profiles.

Clean rooms established by major cloud providers were designed for developers who sought  to build their applications on top of the technology. Conversely, other solutions embedded within a DCP were crafted as a component of a comprehensive solution primarily aimed at marketers, who were looking for an end-to-end solution for leveraging 1st/3rd PD.

With the commoditization of clean room technology, we now observe isolated data silos maintained by both CMNs and advertisers. Due to the reluctance of all parties to transfer data between multiple DCRs—driven by risks, high costs, and privacy concerns—interoperability becomes essential. This entails seamless collaboration across diverse data types, sizes, formats, cloud providers, and data warehouses, enabling DCRs to address the key challenges faced by advertisers effectively.

As DCR vendors grasp this challenge, we’re now witnessing the emergence of early versions of interoperability between clean rooms. The primary hurdles lie in enhancing the customer experience and ensuring the provision of essential functionalities like campaign measurement across clean rooms.

Key takeaways

From Clean Rooms to Collaboration Platforms:

Standalone data clean rooms (DCRs) have evolved into data collaboration platforms (DCPs), offering enhanced scalability, audience creation, campaign activation, and measurement, making them more effective for marketers.

Measurement Remains a Challenge:

Retail media struggles with attribution complexity across platforms, engagement and conversion locations which impact CMN proof advertisers the quality for their data and inventory

Self-Service Audience Building is Key:

Empowering advertisers with no code/SQL user-friendly tools for audience creation, using their own business logic boosts campaign performance by 1.5x, showcasing the value of simplifying data collaboration.