PP&A ran the study as a series of in-depth expert interviews with practitioners who use clean rooms day to day: data and analytics leads at media agencies, marketing and measurement leaders at brands, and data and advertising sales specialists at media owners, across the United States, the United Kingdom and India. Candidates were screened for hands-on experience with at least one major clean room platform, so that ratings reflected use rather than reputation.

A structured interview guide covered five areas: the use cases each organization had deployed and their relative importance; adoption and frequency of use; the provider landscape and how walled-garden platforms compare with independent ones; ease of use, data ingestion, identity matching and the skills required; and expectations for the next few years. Each participant scored platform usability on a consistent scale so that results could be compared across providers, including the client's product.

The findings were synthesized into a final report that combined quantitative ratings with the qualitative reasoning behind them, a use-case map, a provider comparison and a forward view, together with an appendix profiling the interview base.

The study gave the client a practitioner-level map of the market. Campaign measurement and attribution, privacy-compliant data integration, and matching online exposure to offline sales emerged as the primary use cases, with audience segmentation, lookalike modeling and incrementality testing as secondary ones. Adoption was moderate but rizing, and usage was largely campaign-driven rather than continuous, which shaped how the client thought about engagement and pricing.

On usability the picture was mixed. Practitioners rated clean rooms as moderately difficult overall, with walled-garden platforms considered easier than independent ones. The client's own clean room scored among the stronger platforms for audience targeting, campaign optimization, reach and frequency analysis and brand-lift integration, but participants were clear that it demands SQL and data-warehouse skills that many marketing teams lack. Hashed email addresses were the dominant identifier, with match rates typically in the 30 to 50 percent range, and the shortage of people fluent in SQL, Python or R and data warehousing was the most common barrier to scaling usage.

Looking ahead, practitioners expected clean rooms to become essential infrastructure as cookies are deprecated, with tighter AI integration, greater interoperability between providers and falling prices. The client received a clear set of usability, skills and interoperability themes to address in its roadmap, grounded in how its own customers and its competitors' customers actually work.

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