Paying for Precision—The New Economics of Music Usage Data

For decades, the music industry has accepted a compromise in how royalties from public performance are distributed. When music is played in cafes, restaurants, retail stores, gyms, and other licensed venues, businesses pay fees with the expectation that the rights creators will be compensated for those plays. But in practice, collective management organizations (CMOs) have relied on proxy data to allocate those royalties rather than concrete data. That proxy data is derived from radio airplay, surveys, and partial reporting from a limited pool of venues.

This article was created in collaboration with DMN partner Audoo.

This system was designed to balance cost, practicality, and precision. However, it comes with a major structural limitation—it does not measure what is actually played across the majority of physical locations. Instead, music usage and therefore royalty payments are estimated based on indirect signals. That creates a fundamental issue for musicians and rights holders who want to be paid when their music is utilized in this way.

Without a benchmark tied to real-world playback in these environments, there is no way to quantify how accurate royalty distributions truly are. The industry is not working within known margins of error, but rather without a measurable understanding of how far distributions diverge from actual usage in public performance settings.

This lack of precision in royalty distribution has financial consequences for everyone involved. When proxy-based systems are used, value is redistributed along blurred lines. Some rights holders benefit disproportionately because their music is over-represented in proxy datasets. Others are underpaid because their music, while played in venues, is not captured. This is particularly relevant for independent artists and niche genres that may perform strongly in public spaces, but lacks broader airplay data.

Historically, this compromise reflected technological limitations. Tracking music across thousands of venues in real-time was not cost effective. As a result, CMOs made an implicit decision on behalf of rights holders to instead determine how much accuracy was worth investing in. That context is now changing as music recognition technology enables direct measurement of what is played in physical venues. Platforms like those developed by Audoo can capture usage data from a physical location, creating a more reliable link between playback and payment. While not perfect or universal, these music recognition systems introduce something new—measurable accuracy that can be tested and improved over time.

Despite rapid advances in music data tracking, many public performance royalty systems still reflect frameworks developed decades ago when data collection was more sparse. CMOs have historically depended on proxy models shaped by the technical and economic constraints of the 1990s. Today, those technology limitations have been removed, yet many of these fossilized proxy systems remain in place for royalty distributions. This creates a growing disconnect between what is technologically possible and what is operationally standard. It raises important questions about how long estimation-based models can continue to underpin royalty distribution systems in environments where actual usage data can be measured and assessed with little upfront cost.

This shift in being able to identify public performance tracks helps reframe the economic question of how music royalty distribution should be impacted by accuracy. Rather than asking whether this level of granular tracking is feasible, the industry must consider what level of investment is justified. Some estimates suggest that allocating around 3% of collected royalties could deliver a meaningful improvement in royalty distribution accuracy. For rights holders whose music is currently under-represented, this 3% is not an incremental cost. It is the difference between being paid and being overlooked—making it well worth incurring.

Other sectors outside the realm of music routinely invest in better data to improve outcomes. Music royalty distribution for public performance is now approaching the same inflection point. The central issue is no longer technological feasibility, but whether the industry is willing to move beyond systems that are not capable of measuring their own accuracy. As expectations around fairness and transparency increase, reliance on proxy data becomes harder to defend.

Audoo’s approach centers on small, passive audio monitoring devices installed in licensed venues. These devices do not record conversations or store raw audio. Instead, they capture brief audio snippets, convert them into anonymized digital fingerprints, and match them against a database of recorded music. This allows the system to identify which tracks are being played in near real-time, without collecting personally identifiable information. The data is then aggregated and reported to inform royalty distribution, creating a direct link between actual playback in physical locations and payments made to rights holders for their music use.

Accuracy in music royalty reporting is no longer an abstract goal. In the context of public performance royalties, it is becoming an achievable standard thanks to companies like Audoo.