Big data’s place in music streaming

vish-nandlall-music-streaming-data

 

Music has been created and enjoyed for centuries, and electronically, songs and albums have undergone a vast evolution of formats and mediums. The most recent trend in this continuous evolution is the advent of music streaming. Streaming outlets allow users to accumulate, organize, and listen to countless artist discographies for a monthly rate, in lieu of offering each song and album for a specific price to signify ownership. Resources such as Spotify and Apple Music have popularized this concept in recent years, and they have been able to make their services more personalized and efficient via big data analytics.

 

Here are a few ways big data is currently being used in music streaming.

 

Your daily mix

 

Arguably most noted on Spotify, music library personalization is easily one of the biggest ways big data has been leveraged to provide a quality user experience. Spotify users are able to enjoy a library uniquely tailored to their interests, past activity, and genres or artists of choice. This data is compiled automatically and, through machine learning, results in a range of recommendations, including playlists, artists, and other factors that may be of interest to a user. For example, a user with a long history of listening to the Foo Fighters may notice the new Foo Fighters album included in their premade “Release Radar” playlist, which compiles new releases that may interest the user.

 

Digging up hidden gems

 

Music streaming analytics share a symbiotic bond with the artists they bring exposure to — especially those that may not be bonafide superstars such as Taylor Swift or David Bowie. Playlist recommendations tend to employ a mix of artists, both popular and relatively unknown, in an attempt to provide users with mix of what they consider familiar and new. Once again, this feature is mainly the product of machine learning technology, which is able to make a distinction between the artists and similar artists played by a user, tracing key similarities between these artists and lesser-known ones (at least to the user). The result is a sophisticated music advisor, of sorts, constantly working to produce a variety of options.

 

Helping artists

 
The aforementioned section is not the only way streaming analytics aid new or lesser-known artists, however; they also provide these artists with key statistical information with which to track audience growth, song interactions, and other customizable features to build the biggest fanbase possible. Spotify, for instance, recently launched its Spotify for Artists app, which gives artists free range to control their Spotify presence via features such as “artists pick,” which allows them to showcase a new release and ultimately increase its traffic.