The Impact of Data on the Success of the Music Streaming Sector

Tens of millions of people listen to music every single day. Brands such as Spotify are rapidly accumulating data, consisting of keywords, song preferences, geographical locations and most-used devices. In regards to Spotify, data drives almost every decision across each department. The information used to train algorithms helps to enhance the user experience. One prime example of this would be the Discover Weekly feature. 40 million people were reached in the first year of this being brought in, with users being presented with a curated playlist of thirty songs every Monday. What makes the feature interesting is that users are often presented with songs that they might not have heard before, meaning AI is being used to predict the type of music someone may like.

User Data is Helping to Personalise Artificial Intelligence

It’s not just streaming services like Apple Music and Spotify that are using AI either. Paddy Power online slots use data from the games a user has played, including the duration, the theme and even game mechanics to make suggestions for new games. AI is also used in the form of random number generators, ensuring each spin is unique, while ensuring a personalised experience to the user. AI is indeed all around us, and when combined with machine learning, it’s a very powerful thing. 

Machine learning can be used to recommend games, songs or artists to users who they might not have discovered otherwise. When you look at the music sector, this means that artists who would not have been searched for organically, get the chance to be heard. A lot of this comes down to collaborative filtering. Users' behavioural trends are monitored over time, to ensure that recommendations are justified. Content streaming platforms, such as Netflix adopt collaborative filtering, by using the rating a user has given to a movie or series, to make further recommendations. Even though music streaming sites don’t often have a star-rating system for songs, they do use implicit feedback. This is done by documenting the amount of times that someone has played a song or even the amount of times that an artist’s page has been clicked. This helps to provide recommendations for artists who may be deemed as being similar.

The Power of Natural Language Processing

NLP analyses human speech in the form of text. Streaming services use metadata, as well as discussions online regarding musicians, such as news stories to find out what people are saying. It is then able to identify noun phrases and descriptive terms that could be associated with the song or artist. This helps to create a cultural vector that changes on a daily basis. Each term is given a weight that relates to how important that term is to the musician or artist. This helps to refine the data even more, ensuring that artists are bundled together when it makes sense, rather than just focusing on song data such as structure, chord progression and genre. 

It’s an exciting time for music, and the rise of AI is helping to ensure that the artist and user are always put first.