It is no secret that the current music-streaming payment model is no longer working — created more than 15 years ago, the so-called “aggregate” model, whereby artists and songwriters are paid according to their percentage of total streams, disproportionately rewards superstars while most others are earning a fraction of what the top artists are collecting.
There have been any number of theories on how to “fix” it, and Universal Music and France-based Deezer — which is approximately the sixth- or seventh-largest streaming service globally but is No. 1 in France and several other countries — have announced the launch of what they are calling an “artist-centric” streaming model, “designed to better reward the artists, and the music that fans value the most.” Deezer will launch the model in France in the fourth quarter of 2023, with additional markets to follow.
The two companies, which announced a partnership earlier this year, say they are aiming to “develop an economic model that better reflects the true value of artist-fan relationships.” While it does not resolve the central problem of the aggregate model, it does aim to boost earnings for what they call “professional artists” and recognize which artists and songs are most engaged with by users, and de-emphasize what it calls “noise audio” — for example, recordings of water running or crickets chirping — that were created with minimal creative effort yet still earn royalties.
According to the announcement, “based on Deezer’s in-depth data analysis,” the following changes are being integrated into the new artist-centric model:
• Focusing on artists – Deezer will attribute a double boost to what they define as “professional artists” – those who have a minimum of 1,000 streams per month by a minimum of 500 unique listeners – in order to more fairly reward them for the quality and engagement they bring to the platforms and fans (the company’s data shows that “only 2% of all uploaders—those artists attracting a consistent fanbase—had more than 1,000 monthly unique listeners”);
• Rewarding engaging content – additionally assigning a double boost for songs that fans actively engage with, reducing the economic influence of algorithmic programming;
• Demonetizing non-artist noise audio – Deezer is planning to replace non-artist noise content with its own content in the functional music space, and this won’t be included in the royalty pool (the streamer will also work to limit uploads of such recordings); and
• Tackling fraud – continuing to drive an updated, and stricter, proprietary fraud detection system, removing incentives for bad actors, and protecting streaming royalties for artists.
“This is the most ambitious change to the economic model since the creation of music streaming and a change that will support the creation of high-quality content in the years to come,” said Jeronimo Folgueira, CEO of Deezer. “At Deezer we always put music first, providing a high-quality experience for fans and championing fairness in the industry. We are now embracing a necessary change, to better reflect the value of each piece of content and eliminate all wrong incentives, to protect and support artists. There is no other industry where all content is valued the same, and it should be obvious to everyone that the sound of rain or a washing machine is not as valuable as a song from your favourite artist streamed in HiFi.”
Michael Nash, UMG’s EVP and Chief Digital Officer, said, “The goal of the artist centric model is to mitigate dynamics that risk drowning music in a sea of noise and to ensure we are better supporting and rewarding artists at all stages of their careers whether they have 1000 fans or 100 thousand or 100 million. With this multi-faceted approach, music by artists that attracts and engages fans will receive weighting that better recognizes its value, and the fraud and gaming, which serves only to deprive artists their due compensation, will be aggressively addressed. As the ever-evolving music landscape continues its rapid transformation, UMG and Deezer will rigorously address the impact of these changes as we incorporate new insights from data analysis, and fine-tune the model, as appropriate.”
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