Music streaming has transformed the music industry and changed the way consumers consume music. Many entertainment companies are now performing statistical analysis on their consumers to find trends and patterns in the listening behavior of their customers. Some companies have already achieved this level of sophistication, while others are only just beginning to enter this world. The future looks very promising for businesses wanting to use data analytics as a way to enhance their business strategy and maintain high customer retention rates.
Music distribution before streaming
In the pre-streaming era, you could only listen to music if you bought it in stores or at a concert. Physical distribution was expensive and inefficient: labels would spend money on promotion and advertising in an attempt to get people to buy their albums. They had no idea if people would even want the album, since they couldn't easily see how many times it was being downloaded illegally online or purchased legally online.
A turning point in the music industry was the beginning of Napster. Napster allowed users to download songs without compensating the recognized rights holders (the artists). The legal battle between Napster and the Recording Industry Association of America (RIAA) opened eyes across industries: consumers were demanding access to digital content. Apple then entered this space with iTunes; its radical approach led them from just selling individual songs for 99 cents each through their website, but then eventually allowing users full freedom over what they could download from within one's own devices—a huge game changer for both consumers and artists alike!
Data science in the music industry
According to a 2022 IFPI survey, 24.3 percent of weekly music engagement is attributed to streaming services like YouTube and Spotify. If you include digital downloads, the percentage rises to 35 percent.
Today, Spotify is the world's most popular audio streaming subscription service with 422m users, including 182m subscribers, across 183 markets.
For artists and record labels looking to understand who their fans are and how they engage with their music (or as an artist yourself), data science has become a key tool for understanding how your fans interact with your content on different platforms like YouTube or Apple Music.
Spotify is a pioneer in this field. While other services use more traditional methods of data collection — like surveys or focus groups — Spotify has taken a more scientific approach to understanding its users' habits while they listen to music on their phones. The company uses machine learning algorithms that analyze its users' listening patterns to determine what types of new music they might like, who influences them and where they're located. It then uses this information to recommend new artists and albums that might interest them based on their demographics (age, gender and location).
Spotify is all about finding the most effective way to craft their user experience for listeners.
Example: Spotify Wrapped uses data visualization to transform raw listener data into a compelling, personalized story that recounts each user’s listening habits over the past year and creates a playlist of songs listened to most over the course of the year. The Wrap was sent directly from Spotify HQ in Stockholm where it was personalized with custom artwork based on each recipient’s favorite band or musician and included fun facts about her/his listening history via email or social media post so everyone could share what they learned. All in all, Wrapped is a neat piece of storytelling through music data that allows users to feel an emotional connection to the music they listened to and drive engagement online.
Now with Spotify for Artists, they also track individual songs within playlists so artists can see which songs generate streams across multiple playlists, which playlists were creating new fans, as well as how many streams they were generating overall. All of this can be accessed on the mobile and can have a variety of uses such as helping decide which songs to play live during their concerts.
Spotify Publishing Analytics give publishers daily streaming statistics for the works and recordings they have identified, including playlist performance; plus access to detailed playback reports per track played through Connect & Premium Services
Spotify uses data to train the algorithms and machines to listen to music and extrapolate insights that impact its business and the experience of listeners.
One of Spotify's biggest competitive advantages is its impressive recommendation engine. Using machine learning (ML) algorithms, natural language processing (NLP) and convolutional neural networks (CNN), Spotify is able to transform historical listening data into personalized playlists and music recommendations, all packaged tightly with their 'Discover' feature.
With ML alone, it can learn from listening history and what similar users are listening to in order for it make better recommendations for each individual user based on their unique listening habits or tastes. In addition, with NLP they are able to scan thousands of articles, blog posts or message boards analyzing language used by certain artists or songs in order for them classify said entities as belonging within specific genre categories: Pop Rock, Pop Country, Electronic Dance Music, etc. They use CNN on raw audio data such as BPM musical key loudness etc., which allows them classify songs based on music type further optimizing their recommendation engine.
Other music streaming services have also implemented similar features. Apple has their ‘For You’ feature that recommends songs based on how you like your music, personalized for every listener and that it's a way to make recommendations more personal. The service also uses analytics to track the popularity of songs and artists, as well as their performance in charts across Apple Music, iTunes Store, and elsewhere on the web. These charts measure the number of streams or downloads that a song has received since its release date. The system then uses these statistics to create 'playlists' that you can listen to while you're at home or in the car.
The main advantage of using data analytics is that it allows music companies to accurately predict what's likely to become a trend and be successful, as well as what isn't. With this added ability, music companies and streaming services can put out more content that users want, rather than just having users come to them.
In a session at the Midem music conference in France, former Universal Music Group Chairman & CEO Doug Morris said that "we are going through an amazing revolution in terms of data and analytics" in which "data is moving faster than any other time in history". That's probably why the music industry is changing forever, thanks to the incredible amount of data it is generating, which can be analyzed to make the process of releasing and marketing music more efficient.