With the advent of peer-to-peer (P2P) music file sharing software such as Napster, the music industry was the first media sector to be massively affected by digitization and the internet. As MP3s replaced CDs in the early 2000’s, the industry nose dived financially, shrinking from a $23.4 billion industry in 2001 to $14 billion in 2014. 

However, in recent years, the music industry has started to rebound and global music industry revenues were up to $21.6 billion in 2020 with 18.5% growth yoy in paid streaming revenues.  In other words, the music industry successfully transitioned from a product-based business (CDs and records) to a service-based business.

That being said, the journey back to growth has been nothing short of arduous. Here’s a look at how the industry has continued to reinvent itself amidst massive disruption. 

*Image sourced from IFPI"s Industry Data, https://www.ifpi.org/our-industry/industry-data/ 

Data-Driven A&R

Similar to the ways movie studios are leveraging predictive analytics to decide whether or not to greenlight films, records labels are increasingly embracing advanced analytics to identify high potential talent. 

Though record labels tend to be exceptionally secretive about what exactly goes into their predictive A&R models, numerous artificial intelligence-driven A&R tools have emerged over the past few years that shed light on some of these practices. For example, Sodatone, which was acquired by Warner Music Group in 2018, uses machine learning to mine the 40,000 or tracks uploaded to music streaming sites such as Spotify and sound cloud every day as well as social media, music blogs and touring data. Using these insights, A&R execs can more easily and effectively identify new, emerging artists that may be a good fit for their label. 

Another A&R tool making waves is Instrumental, a scouting platform that uses AI and machine learning to mine streaming and related internet data to discover high potential talent and align those artists with relevant partnerships (i.e. label, publishing, merchandising and licensing opportunities). 

Intelligent Royalty Collection

Since the industry’s inception, artist compensation, or lack thereof, has been a central point of contention. Though artists are legally entitled to royalties - compensatory payments received by rights holders (songwriters, composers, recording artists, and their respective representatives) in exchange for the licensed use of their music - accurately tracking music consumption across the digital landscape has proven to be incredibly challenging.

For example, record labels, streamers and other stakeholders use metadata (the underlying information tied to a released song or album, including titles, songwriter and producer names, the publisher(s), the record label, etc.) to track usage. However, according to reporting by The Verge, “Not only are there no standards for how music metadata is collected or displayed, there’s no need to verify the accuracy of a song’s metadata before it gets released, and there’s no one place where music metadata is stored. Instead, fractions of that data is kept in hundreds of different places across the world.”

As a result, hundreds of millions of royalties have been left uncollected. While The Music Modernization Act of 2018 seeks to simplify and centralize the music licensing process so that it’s easier for rights holders to get paid when their music is streamed online, challenges still abound. 

However, AI has the potential to help solve a number of these problems. In 2018, Spotify acquired Loudr, a music licensing platform, and Sonalytic Limited, an audio detection solution. The goal was to integrate these tools into Spotify’s tech ecosystem to provide artists and music publishers with more visibility into and control over royalty payouts. 

Indie music distribution platform, Amuse, has also created a number of AI-powered products to help their clients more efficiently and accurately distribute royalties as well as, using advanced analytics, predict upcoming payouts. 

Last but not least, AI-powered music tagging tools such as Musiio Tag and Muso.AI automate music tagging workflows. This includes legacy credit collection and metadata validation. Using these tools, copyright holders can not only more effectively calculate and distribute royalties, but also ensure new releases are assigned the right descriptors so that they’re easily found by the right listeners. 

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