Do you care about your music recommendations?
Technology has changed the way we listen to music. In moments, we can listen to an album on Spotify that we had forgotten about for 15 years. Or be notified that our favourite band has released their first album in 7 years. Or find a long lost one-hit wonder that you once couldn’t get out of your head. All from our phones.
40% of streams on Spotify come from their own playlists and 55% of users report that they discover new music through Spotify’s recommendations. So what’s behind these recommendations? Or more precisely, what’s behind these playlists?
Algorithmic recommendations are ubiquitous and can sometimes feel like magic. We can be forgiven for not caring what drives them, or for thinking too simply about the elements that factor in. In the case of Spotify playlists, they remove friction, especially if you just want to put something on while doing some other task. This isn’t new. Over-the-air radio still serves this purpose for many. And their advertising-based revenue model seems simple. If they keep you tuned in, you’ll hear their ads. Stations optimize the tempo of music to commercials, the song selection, relevant information, and promises of an interesting story or hit song to achieve this end. Or is it that simple?
The listening and the radio stations are not the only parties involved. It is artists and record labels that provide the music and they have their own incentives too, which is the hope that listeners will want to buy an artist’s album. Payola is the practice of a station receiving payment in exchange for airplay without disclosure to the listener. It is illegal, has been for a long time, but still considered to be occurring by industry insiders despite the radio landscape being heavily regulated.
Music streaming, however, is not regulated - at least not to radio’s extent.
Spotify sees 88% of its revenue coming from premium users and they maintain a very low (<4%) churn rate. They are incentivized to keep their premium users happy and have them bring their streaming into many parts of their daily routines. In practice, this means removing friction from their lives by providing music with little to no input from them - and to keep the stream playing in a way that becomes essentially unnoticeable. This is witnessed by the popularity of “mood” playlists, where the music is there to support a mood or activity as background, as opposed to listening to music in an intentful way.
This creates an interesting problem. The recommendations do not want to risk upsetting your mood. If you skip songs on a playlist too many times, you’ll abandon this playlist. If it happens across playlists, you’ll abandon the playlist entirely. Or you’ll make your own - without Spotify’s input. Therefore, recommendations won’t take any risks in helping you find your new favourite song. In actuality, they’ll favour songs that will go mostly unnoticed or at least have a familiar sound. So while more than half of Spotify listeners report discovering music on the platform, is it really discovery?
Just like radio, there are other stakeholders - those who provide the music. And just like radio, there are incentives for artists and labels to pay for exposure (which they do). Spotify also exerts massive leverage and resources. They can choose which artists to include on their playlists, and much like song selection, artist selection will be influenced by an incentive to keep a premium subscriber’s stream uninterrupted.
By now, I hope you’ll agree that those recommendations are not generated in a straightforward way - and not entirely for your benefit.
For a deeper dive, I recommend Liz Pelly’s book - Mood Machine: The Rise of Spotify and the Costs of the Perfect Playlist.