Spotify is a wildly popular music streaming platform that has become ubiquitous among it’s millennial user base. With more than 190 million monthly active users, and 87 million premium paid users, Spotify is growing quickly . As millions of users access the music platform every day, Spotify has access to an unprecedented amount of data about music preferences of the masses. Through the use of machine learning and artificial intelligence, Spotify has begun to utilize this data to provide better services to both the content creators and consumers.
Why Machine Learning?
Having reached a critical mass of users, Spotify is now using machine learning to capitalize on the quantity to make sure their users stick around. They are continuously collecting information about what a user listens to, how often, and when they save a song to a playlist. Using all of this information, Spotify is able to implement a few different methods of machine learning in order to predict what songs a user will like with uncanny accuracy . These methods are critical to product development and process improvement at Spotify, because it is their main differentiator. Pandora, Apple Music, and Amazon Music are just a few of its competitors, but none have the same amount of user data generated on a daily basis .
The ability to provide impeccable recommendations as well as unique content like the “Discover Weekly” and “Release Radar” is a significant pull for new users, as well as a critical for the stickiness of the product to existing users . As Spotify “learns” about a user’s preferences, the personalized playlists improve, and users are incentivized to continue the use of the product. The service Spotify provides beyond just access to music is hinged upon their ability to show users new music that is curated for their taste. This is why they keep coming back for more.
Spotify’s Machine Learning Innovation Strategy
Spotify has been making active efforts to stay agile and well equipped for the advances made in machine learning in recent years. In Spotify’s short term, they have acquired many data and machine learning-focused companies. For example, they acquired Niland in 2017, which is a machine learning product that helps provide better search and recommendation power. They also acquired MightyTV for its content recommendation service and Sonalytic, an audio detection service . They are using this strategy to keep abreast with the latest innovation in this space.
For their medium-term strategy, they have been making talent acquisition a priority by seeking out data scientists and statisticians that will be a source of future innovation. Francois Pachet, a science and AI music composition expert, was recently hired, signaling an interest in AI-generated music . With the launch of “Spotify for Artists”, Spotify is also starting to offer more services to the other side of the music industry – content creators. This tool generates analytics and trend information that can help artists to connect more closely with their audience . This is just the beginning of the applications of machine learning and big data to content generation.
Spotify should continue to invest time and money in both sides of their business – content curation and generation. By evangelizing this technology on both sides, they would be able to grow their user base (driving revenues) as well as develop greater relationships with artists and the music industry (their cost drivers). In the short term, Spotify should continue to make acquisitions where applicable and hire top talent. Seeking risk takers from related industries and academic will drive innovation forward.
In the medium term, Spotify should begin to explore the curation of other forms of media. Podcasts are already available on Spotify, live talk radio may be an interesting foray. One can also imagine music videos are a simple next step, giving way to longer form video content in the future. As Spotify’s artificial intelligence-generated content research continues, these other forms of media may be an interesting application of these innovations.
- How can Spotify capitalize on AI-generated content? Do you expect users to engage in this kind of content beyond the “novelty” phase?
- Should Spotify expand beyond music into video content? How can would this affect their place in the competitive landscape?
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