Microphone Check: What Platform-Level Data Could Change in Music Market Analysis

Microphone Check: What Platform-Level Data Could Change in Music Market Analysis Antal, Dániel Table of contents Price analysis 1 Conceptualframework.................................. 3 Observedvariables ................................. 3 Price as a micro-level variable . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Homogeneity assumption within territory and sales point . . . . . . . . . . . 4 Aggregation and price indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Aggregateunitprice ................................ 4 Dispersionindicators................................ 4 Platform and package aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Premiummusicstreaming............................. 5 FamilyPlans .................................... 6 Socialmediaplatforms............................... 7 Dispersion......................................... 9 Globalcomparison .................................... 10 Priceconclusion ..................................... 10 Volume analysis 12 Data construction and analytical scope . . . . . . . . . . . . . . . . . . . . . . 12 From price observation to the limits of volume measurement . . . . . . . . . . 13 References......................................... 15 Price analysis ĹNote This working paper explores the analytical possibilities opened by platformand distributor-level streaming data for music market analysis. Using a fictitious sound recording and simulated contractual conditions, the paper demonstrates how detailed reporting can be used to reconstruct prices, quantities, and revenues across streaming 1 platforms, subscription tiers, and territories. The analysis focuses on methodological transparency rather than empirical claims, and all data used are synthetic, simulated, or anonymised. The paper is motivated by the limitations of publicly available national and international market reports, which typically present aggregated revenues without distinguishing between domestic and foreign earnings or revealing platform-specific price–quantity dynamics. By contrast, distributorand platform-level reporting can, in principle, support the separation of territorial revenues, the identification of effective unit prices, and the alignment of observed consumption volumes with remuneration outcomes for different rightsholder groups. This document is released as an early-stage working paper (version 0.1.0) in line with the Guidelines for Open Policy Analysis (available at https://www.bitss.org/opa/ community-standards/), the document has been released early to support consultation, incorporate stakeholder input, and ensure transparency throughout its development1. It is intended as a methodological exploration and proof of concept, not as a source of representative market results. The paper does not rely on confidential commercial data and does not disclose or approximate real contractual terms. Its purpose is to illustrate how increased transparency at the reporting level could enhance economic analysis, policy evaluation, and regulatory dialogue in digital music markets. The analysis is part of ongoing research and is expected to evolve. Later versions may refine assumptions, extend the modelling framework, or integrate additional data sources. The source repository linked to this record contains the underlying analytical materials and will document future revisions. Funding acknowledgement: This project has received funding from the European Union’s Horizon Europe programme under Grant Agreement No. 101095295. The views expressed are those of the authors only and do not necessarily reflect those of the European Commission or its agencies2. Citation note: When citing this Green Paper, please use the latest versioned DOI 2The Guidelines for Open Policy Analysis form an actionable and practical set of directives that the OpenMusE consoritum was mandated to use under the Grant Agreement. It can be seen as good implementation framework for Evidence-based policy making in the European Commission and The practice of reproducible research [BITSS (2019); Open Music Europe (2023); (J 2015; Kitzes, Turek, and Deniz 2018). 2This document has been prepared by Open Music Europe (OpenMusE) project partners as an account of work carried out within the framework of this contract. Any dissemination of results must indicate that it reflects only the author’s view and that the Commission Agency is not responsible for any use that may be made of the information it contains. Neither Project Coordinator, nor any signatory party of Open Music Europe (OpenMusE) Project Consortium Agreement, nor any person acting on behalf of any of them: (a) makes any warranty or representation whatsoever, express or implied, (i) with respect to the use of any information, apparatus, method, process, or similar item disclosed in this document, including merchantability and fitness for a particular purpose, or (ii) that such use does not infringe on or interfere with privately owned rights, including any party’s intellectual property, or (iii) that this document is suitable to any particular user’s circumstance; or (b) assumes responsibility for any damages or other liability whatsoever (including any consequential damages, even if advised of the possibility) resulting from your selection or use of this document or any information, apparatus, method, process, or similar item disclosed herein. 2 available on Zenodo, and include the date of access if referring to material hosted on our GitHub repository.[^index-5] This is an early version ## Scope and illustrative setup We construct a purely fictional illustrative asset, “Microphone Check” by “Open Observatory”, identified by the fictional Slovak ISRC SKZZZ2500001. This asset serves exclusively as a neutral label for demonstrating the statistical treatment of royalty statement data. The underlying royalty lines are based on real, observed revenues and inferred unit prices originating from ALOADED’s Hybris system, with all identifying and commercially sensitive details removed or aggregated. The asset name and identifiers therefore do not correspond to any real recording, while the numerical values retain realistic statistical properties. The objective of this exercise is to: • identify micro-level variables observable in royalty statements, • define a statistical model that derives meaningful aggregate indicators, and • calculate price, volume, and revenue statistics suitable for comparative analysis. The focus is explicitly on financial and statistical outputs, not on modelling contractual entitlements or full royalty account structures. Conceptual framework Observed variables At statement line level, we observe the following core variables: • territory 𝑟 • sales point 𝑠(e.g. Spotify, Apple Music, YouTube) • usage quantity 𝑞𝑖,𝑟,𝑠 • reported royalty revenue 𝑅𝑖,𝑟,𝑠 • currency 𝑐 Each index 𝑖refers to an individual royalty line within a reporting period. Price as a micro-level variable We begin with the simplest derived variable: price per usage unit. For a given royalty line 𝑖: 𝑝_𝑖,𝑟, 𝑠 = 𝑅𝑖,𝑟,𝑠 𝑞𝑖,𝑟,𝑠 This unit price is not a contractual tariff but an implied net revenue per usage unit, derived ex post from statement data. 3 Homogeneity assumption within territory and sales point Our working assumption is that, within a given territory and sales point, unit prices do not materially depend on the individual asset. A single stream in Slovakia on Spotify (premium or ad-supported) should yield approximately the same net revenue for “Microphone Check” as for any Slovak or foreign recording streamed by Slovak users. This assumption allows aggregation across assets while preserving meaningful price signals at the territory × sales point level. Aggregation and price indicators Aggregate unit price For each territory 𝑟and sales point 𝑠, we define the average unit price as: 𝑝_𝑟,𝑠 = ∑𝑖𝑅𝑖,𝑟,𝑠 ∑𝑖𝑞𝑖,𝑟,𝑠 This is a ratio of aggregates, not an average of individual prices. Dispersion indicators To illustrate heterogeneity within a territory and sales point, we additionally report: • unweighted mean price, • minimum observed unit price, • maximum observed unit price. Formally: 𝑝min 𝑟,𝑠 =min 𝑖(𝑝𝑖,𝑟,𝑠), 𝑝max 𝑟,𝑠 =max 𝑖(𝑝𝑖,𝑟,𝑠) These indicators demonstrate that even within a single territory, sales conditions may vary significantly due to product mix, user type, or monetisation channel. 4 Platform and package aggregation To reduce disclosure risk while preserving analytical meaning, individual sales points are aggregated into analytically meaningful platform–package groups. We pre-selected platform categories that are present in most EU countries and generate sufficient volume for statistically meaningful comparison. Although European artists generate significant export revenue outside Europe, extending the present framework to Asia, Africa, or Latin America would require region-specific modelling beyond the scope of this analysis. Our focus is price comparison within Europe. We classify usage into four platform–package groups: premium subscriptions, family subscriptions, ad-supported streaming, and social media platforms. These groupings reflect distinct monetisation regimes rather than branding or contractual structure. We created the aggregates so that they do not refer to a single sales platform. Premium music streaming • Deezer Premium • Spotify Premium • Apple Music • YouTube Music Figure 1: Our base chart is the “Premium” segment. We ordered the EU countries in the same ranking for comparability. 5 As we see on the first chart, the price differential in the value of a single stream in the EU varies by about a ratio of 1:4. We observed the highest revenues in Sweden. Obviously, this creates a competitive advantage for artists with a strong Scandinavian listener base. The affect is not only present in Scandinavia: we know from anecdotal evidence confirmed by these price comparisons that the main licensed revenue source of Serbian music is Scandinavia due to large local diasporas. The lowest revenues are observed outside of the euro-zone, Romania, Hungary, Bulgaria, and—despite eurozone membership—Croatia. It is important to realise that local artists usually account in the local currency. ALOADED collect revenues typically in euros and dollars, and converts it to Swedish krones. During periods of local currency depreciation, artist counting in Romanian leu or Hungarian forints may feel stabilising or rising revenues. In a former analysis for the UKIPO we have shown that after Brexit, UK artists for years did not feel the effect of decreasing streaming prices due to the depreciation of the GBP versus the export market currencies. Family Plans • Apple Music Family • Spotify Family Figure 2: dsfa Family subscriptions are economically closer to premium subscriptions, but they were introduced at different times and under different market saturation conditions across territories. This leads to slightly different unit price dynamics, which justifies separate treatment. ### Ad-supported 6 • Spotify Free • YouTube ad-supported The comparison of the ad-supported revenues highlights a long-standing problem. Adsupported revenues depend on the existence of a viable local advertising market; platforms only sell advertising inventory where maintaining local sales and agency relationships is profitable. Small countries disproportionately suffer from no advertising sales. The price difference is really great in this field, and it is not unrelated from the next segment, social media revenues. Social media platforms • Instagram • Facebook • TikTok We did not differentiate between potential different payment schemes of these platforms. 7 Despite differences in internal remuneration schemes, these platforms share a common reliance on advertising-driven monetisation and exhibit similarly low and volatile unit prices across territories. The unit price difference is roughly 1:6 within the EU countries. 8 Dispersion Figure 3: The prices in the combined chart are shown on a logarithmic scale; premium and social media revenue segments differ by several orders of magnitude. Various licensed users apply different sales configurations, pricing models; for example, Apple Music has trial periods and student prices; social media companies also differentiate between content and content. In the chart above we show the lowest, unweighted average and highest observed prices in various segments. 9 Kitzes, Justin, Daniel Turek, and Fatma Deniz, eds. 2018. The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences. 1st ed. University of California Press. http://www.practicereproducibleresearch.org/. Open Music Europe. 2023. “Open Music Europe (OpenMusE) – An Open, Scalable, Data-to-Policy Pipeline for European Music Ecosystems.” https://doi.org/10.3030/ 101095295. 16