Spotify is currently the world’s most valuable music company, and for good reason. Now worth around $25 billion, the company has had a major impact on both the popularity of music streaming and the way the music industry uses the data these streaming services generate in impactful ways.
How Spotify Uses Big Data
Streaming music platforms are using data collected by consumer interaction in an effort to hone their algorithms, improve user experiences, target audiences with ads, and make overall better-informed business decisions. Spotify currently has over 108 million paying subscribers—and another 124 million free users—meaning there are billions of streams contributing daily to these processes, whether listeners are directly aware of it or not. Read on to explore some of the most common trends in big data use by Spotify and how that data is being used to improve streaming services today.
1) To Enhance and Customize User Experiences
One of the most prominent ways Spotify uses the data generated by their customers is to help generate content that each user will consider in-line with their specific tastes. Although Spotify approaches this process from a variety of angles, the overarching goal is to provide a music-listening experience that is unique to each user, and that will inspire them to continue listening and discovering new music that they will be engaged with week after week. This is accomplished through the use of artificial intelligence and machine learning algorithms.
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Creating Custom Content
One of the key players in data collection is Spotify’s “Discover” feature, which was first introduced in 2012. This feature began as a playlist of tracks released by a user’s favorite artists but soon evolved to become a recommendation engine of sorts, suggesting a set of songs at the end of a user’s playlists based on the existing songs within it.
Today, “Discover Weekly” has become one of Spotify’s most popular features; it creates a custom playlist unique to each listener’s activity which is curated entirely by a machine learning algorithm. The algorithm analyzes other users’ playlists to find commonalities between tracks, and then utilizes that information to develop a new playlist that fits in with the listener’s existing song preferences. Additionally, each user has a “taste profile” comprised of microgenres that help further customize these playlists. (This full process is outlined in the flow chart to the right.)
In order to successfully customize these playlists, Spotify had to start paying attention to not only what users listen to, but how they interact with each song. If a subscriber plays a track and changes it within the first 30 seconds, for instance, Spotify recognizes this as a “thumbs down” interaction, and won’t use that song’s data when calculating playlists. When a user adds a song to a playlist or library and listens to it in full, on the other hand, it tells the platform that the song positively aligned with their taste, a factor which then helps the algorithm further develop the user’s overall taste profile.
Digitization of Your Taste
A listener’s taste profile is also used in a Spotify function called “Daily Mixes.” These playlists are separated by the genres of music the user typically gravitate toward and are comprised of songs that:
- The user has saved or added to playlists.
- Are written by the same artists the user has in their current playlists.
- Are from new artists or albums the user doesn’t yet know.
These playlists are bottomless and ever-changing, and while they tend to have more familiar content than the “Discover Weekly” playlists, Spotify may still sprinkle in some interesting tracks you don’t know for variety.
Who’s On Your Radar?
“Release Radar” is a weekly playlist comprised of new releases from the artists each user follows, similar in format to the original “Discover” playlist. To get the most out of this playlist, it’s important that the listener actually “follows” their favorite artists on the platform, as this helps the algorithm to develop a more accurate playlist of new song recommendations from that artist. The algorithm may also add a few extra new songs just to keep the mix fresh.
2) To Better Market Their Product
Alongside improving customer experiences, Spotify is able to use the massive amount of data generated by its users to inform its own ad campaigns and better target consumers. At the most basic level, this is done by reviewing what they’ve learned about their listeners and using those insights to develop ads that strategically target their ideal audience.
The Power of Targeted Ads
One display ad—which first ran in Williamsburg, New York—sparked a long-running marketing campaign for Spotify in which the organization used listening history to develop funny, targeted ads.
This first ad—which read, “Sorry, Not Sorry Williamsburg, Bieber’s hit trended highest in this zip code”—was engaging, impactful, and humorous in the local market, considering it was displayed in a “hipster” area known for its notoriously high concentration of “music snobs.”
Through this experience, Spotify was able to gauge the potential impact that using listener data to develop customized ad campaigns could have on their sales and user engagement. This experience sparked a series of strategic and well-received ads that are utilized to market the streaming platform to this day. Some of the most popular campaigns included a set of holiday ads, a set of 2018 Goals ads, and a current set of “Meme-Inspired” ads. (Some examples of these campaigns are included below.)
A Constantly Improving System
In April of 2018, the music giant announced its free users would no longer be forced to only shuffle through music on the platform. Instead, these users were granted the freedom to explore 15 of Spotify’s most popular playlists, including “RapCaviar” and “Discover Weekly.” While the free users were excited by the opportunity to experience more of Spotify’s top content, the company had a more data-driven motive behind their decision. This shift in access now generates data on the listening habits of over 124 million more users than before, which is incredibly impactful as the organization works to hone their suggestion algorithms and give users the most customized experience possible.
In general, now that streaming far outranks music purchases, the industry has had to shift its focus from record sales to the collection of this kind of data in order to decipher how the public is responding to an artist, album, or song. Since this data also provides a deeper insight into listening trends, audience markets, and more, it is hopefully a sustainable change for those within the field. Either way, trends predict that Spotify will continue to be one of the largest music data sources for some time, and that data will continue to make for better business decisions across industries.
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