Panel Exchanges are easy – it’s what happens before and after that delivers gold standard audience measurement

As audiences increasingly fragment across devices and platforms combined with regulatory changes, measurement frameworks must adapt to capture a holistic view of behaviour, culminating towards the cross-media measurement solutions so many of us are invested in. However, this requires not only integrating diverse datasets but doing so in a way that respects privacy, ensures accuracy, and maintains industry trust. Achieving this balance is no small feat, especially when working with platforms that operate as distinct silos, restricting external tagging and access. 

While the need for cross-media measurement is clear, the pathways to achieving it are still being charted. Here, industry bodies for instance have a crucial role to play in facilitating collaboration between data providers, platforms, and measurement partners. This commitment naturally extends to panel exchanges too, making a deeper understanding of pre- and post-panel exchange technicalities an essential requirement as the industry evolves. 

At its core, a modern panel exchange is the process of matching data from a research panel with data from external platforms, such as Google, Meta, TikTok or Amazon (‘Event Data Provider’, or EDPs). This involves highly secure protocols, such as double-blind exchanges or clean rooms/Trusted Execution Environments, designed to protect privacy, the independence of the panel and ensure data use is limited to specific, agreed purposes. While the mechanics of data exchange might seem straightforward, the process is nuanced. Platforms differ in their data structures, ranging from device-level identifiers to household or account-level information, which poses challenges in creating consistent and actionable datasets. 

Whilst the actual panel exchange itself can be replicated with relative ease. The pre- and post-exchange stages are where the true complexity – and value – of this work lies, often ignored by providers. For example, before any data is exchanged, platforms and panel providers must align on protocols, matching variables, and the technical and contractual frameworks that underpin the process. These agreements, which can take significant time to finalise, are essential to ensure trust and compliance on both sides. Beyond the technicalities, the pre-exchange environment also requires a strategic focus, involving an understanding of each platform’s unique constraints and opportunities and tailoring approaches to suit them, such as matching the right matching variables to the platform’s environment. 

After the data has been exchanged, the task shifts to refining and individualising it into a format that can support meaningful analysis. Device-level or household-level data, while valuable, is not sufficient for robust measurement at a person level. A streaming platform, for example, might provide total impressions for an account holder, but this information does not reveal who within a household viewed the content. Similarly, data from social platforms may require additional modelling to address issues like incomplete matches or mismatched identifiers. These challenges necessitate advanced methodologies to assign exposures to individuals, account for co-viewing scenarios, and integrate data across platforms into a cohesive measurement framework. 

Further, mapping a consistent sample that remains fully consented between multiple platforms or media owners for the purposes of deterministic deduplication is essential for gold standard cross media measurement. 

A new level of cooperation 

While the core concept of panel exchanges has been around for a considerable period, the way they are conducted and the value they provide have been significantly enhanced by technological advancements and evolving data analysis techniques. Consequently, panel exchanges have become essential for tackling the complexities of modern cross-media measurement, enabling data from diverse platforms to be connected in privacy-preserving ways. The mechanics of data exchange are well understood and widely adopted, but the true challenges extend beyond the exchange itself. What occurs before and after data matching often determines the effectiveness and integrity of the measurement process. These crucial aspects, however, are frequently overlooked or undervalued. It’s time to change that. 

Indeed, the increasing adoption of panel exchanges, along with other existing approaches, reflects a broader shift in the industry towards collaboration and interoperability to make progress on shared challenges. Historically, measurement systems were siloed, with individual providers handling data collection, processing, and reporting. Today, the emphasis is on combining multiple data sources to achieve a unified view of audience behaviour. This shift requires a new level of cooperation, not just between measurement companies and platforms but across the entire ecosystem. In the cross media measurement space data ownership can be a balancing act between the raw EDP data and dual need for both panelist and advertiser consent for campaign impressions to be exchanged. 

Looking ahead, as more platforms adopt advertising models and privacy regulations evolve, the complexity of cross-media measurement is likely to increase. The growing role of artificial intelligence and machine learning in these processes also adds both opportunities and challenges. Indeed, Kantar Media’s Data Science team are using a Large Language Model to support the deterministic processing of Origin post exchange data for campaign deduplication between platforms. As the industry grapples with these changes, thought leadership and shared learnings will be key to advancing best practices and ensuring that measurement frameworks continue to meet the needs of all stakeholders. 

As we explore new approaches, the focus must remain on the principles that underpin effective measurement: accuracy, privacy, and collaboration. By addressing the often-overlooked complexities of pre- and post-panel exchange work, the industry can move towards a future where cross-platform and media insights are not only possible but reliable and actionable. 

Kantar Media’s panel exchange process explained