In the case of Spotify, personalized data has been the name of the game since their 2014 acquisition of The Echo Nest, an industry-leading music data and intelligence platform. Today, the Swedish company continues to invest money in systems like reinforcement learning, which transforms metadata into personalized song recommendations that are both within and outside of a listener’s typical taste.

These systems fuel the music discovery aspect of the app. When users tastes are broadened, more music on the app can be consumed, benefiting both the platform and the artists that reap exposure from it. 

As Oskar Stål, Spotify Vice President of Personalization, put it “There isn’t just one Spotify experience. There’s more like 365 million different experiences–one for each use–that’s deeply personalized to their wants and needs.” With individualized content, streaming services and fans are mutually benefited.

If a song customized to a listener’s musical preferences is recommended, that listener will stay on the app for an extra four minutes. This personalization contributes to overall user satisfaction, which leads to brand loyalty and repeat engagement. For listeners, this makes music discovery much simpler. 

 On the artist side, platforms like SoundCloud and Spotify offer products that provide creators with detailed analytics on the listeners of their music. Using this information, artists can use this data as a tool to fine-tune their craft. The metadata used in discovery algorithms can further match the music an artist creates with potential fans, casting your music out to a much wider audience. Artists at the beginning stages of their career can largely benefit from this algorithmic exposure, using it to gain momentum in their career while exerting minimal effort.