How data science is changing the music industry (2024)

Industry success has long been measured by metrics like first-week album sales. In today’s digital era the landscape has evolved dramatically. Record labels and music firms are no longer relying solely on traditional measures — instead, they’ve embraced granular data analytics to understand their audience’s listening habits. This shift has propelled record labels to unprecedented revenue heights and has given them a competitive advantage. Data analytics has become the driving force behind identifying trending artists, emerging genres, regional preferences, and global listening patterns.

have employed data-centric business models to understand their audience, boost profits and stay ahead of the competition by analysing the trajectory of trends and creating a personalised experience for the user.

Spotify’s renowned features, like Discover Weekly, Daily Mix, Release Radar, and On Repeat and Apple Music’s ‘For You’, work by using data and algorithms.

Here’s how:

• Natural Language Processing (NLP): They read the words written on the internet about songs. This helps them figure out what the songs are about — then, they suggest songs to you that have something in common with the music you like.
• Collaborative Filtering: They check what songs people who like similar music as you are listening to — then, they suggest those songs to you.
• Convolutional Neural Networks (CNN): They listen to the actual sounds in the songs. They pay attention to things like BPM, what key the song is in and its rhythm. They then suggest songs that have similar rhythmic and sound qualities to the ones you enjoy.

by interacting with streaming platforms to unravel audience listening habits and platform behaviours. This creates a map indicating where an artist’s work finds popularity — artist managers craft strategies toward greater artistic success and increased revenue.

• Content Promotion: Music labels work closely with streaming platforms to promote their releases, labels can utilise data insights from these platforms to identify the most suitable tracks and albums for promotion.
• Performance Analytics: Labels like Sony provide detailed analytics on how users interact with music. They can track metrics like the number of streams, listener demographics, and geographic reach. This information informs decisions about future releases and marketing strategies.
• Artist Development: Labels use streaming data to assess the artist’s growth and potential. They can track how an artist’s music resonates with listeners over time and make decisions about investing in further development or promotional activities.

In a bid to use data to go beyond viewing only streaming activity, the industry is exploring new ways to present data:

• Automatic Fanbase Segmentation: This approach segments an artist’s listeners into different categories based on their level of devotion. Machine learning is used to infer listener profiles across a large dataset, allowing artists to be easily compared.
• Artist Clustering: Artists are grouped based on listener data to identify similarities and growth opportunities. For example, artists with overlapping fan bases or similar listener demographics may be placed in the same cluster.

Read more on artist-centric analytics here

While data has primarily been used to boost revenue and audience engagement, understanding algorithms and shaping data presentation could empower artists to know their audiences better. Data-driven decisions are taken from user behaviour and listening patterns have the potential to enhance an artist’s creative journey while also ensuring commercial success.