Spotify analyzes your data so that you can be provided with songs that match your musical preferences. There are many ways that the algorithm does this and when you break everything down you’ll soon find that it’s a very interesting system.

Artist Generated Data

When you distribute music through any service, you will be asked to provide certain information about it. This could include the genre, the label and even the release date. This data is then used to fuel Spotify’s algorithm, through AI and machine learning

When the music is accepted by Spotify, a band can then create an artist profile, and you can even submit it to a Spotify playlist. If you choose to go ahead with this, you will be asked to provide instruments in the song, the style, and the culture. This helps again to add to the algorithm that Spotify uses to generate playlists. It’s not just Spotify that uses algorithms, either. Netflix uses them when creating lists of shows you may be interested in, which is a great way to get personalized recommendations. Algorithms are also prevalent in the casino sector. 

Algorithms determine when a win is going to be awarded to a player, with each bet being completely independent. This stops the casino from taking into account previous bets, so that a fair and enjoyable experience can be provided to every player. Casinos also use RNG, to ensure that there is a fair chance of winning at any time of day. Algorithms like this are very sophisticated but help to add to the entertainment experience.

Song Analysis

Spotify does listen to the songs you play. There’s a developer’s handbook, which states 13 different features that come into play when analyzing a track. This includes the loudness, energy, duration, accoustics, danceability, and valence. Even the time signature is taken into account, which helps Spotify come up with further recommendations. 

Lastly, when listening to music on Spotify, your user engagement is tracked. While metadata is a huge part of the algorithm, your listening data also plays a part. Stream likes, shares, and skips play a huge part in what song may be recommended to you next. Playlists that are generated by Spotify for you rely on that quite heavily. Whenever you create a playlist, you may find that your data is analyzed too. If you add multiple songs to a playlist that match the same metadata then Spotify will assume that these songs go together, and therefore recommend them to other people. This helps to create an algorithmic network of data, which helps to make the next decision the system has to make, more accurately as time goes on.

Algorithms are the key to Spotify and they are also the reason why the company is so successful. As the years go by, more listening data is added and algorithms become more refined. This makes it easier for people to find new artists and it also helps to draw new users to the platform, which is the key to the company’s long-term prosperity.