Bringing machine learning to music – Spotify’s vision to win the hearts and ears of humanity
Every Monday, millions of Spotify users find a new playlist waiting for them on Spotify’s Discover Weekly, a curated list of 30 songs that Spotify’s machine learning algorithm predicts each user will love based on the users’ past listening behaviors . Gone are the days of vinyl records; machine learning has fundamentally altered the way users listen to music, and competition to create ever-sophisticated algorithms that provide more personalized recommendations is intensifying. Enter Spotify, one of the world’s leading on-demand music-streaming services, which boasts 87 million subscribers, 191 million monthly active users, over 40 million tracks, and operations in 65 countries .
Trends in the streaming music landscape
Spotify is a company in the online streaming music space. Acquiring consumers depends on delivering the right personalized recommendations to each user. Songza, launched in 2007, is often credited as one of the earliest companies to define the space, which based its music curation process through highly labor-intensive manual curation which was then voted up or down by users . Since then, more sophisticated entrants such as Spotify and Pandora applied machine learning techniques which allowed them to deploy algorithms that analyzed both the audio and lyrics of songs to create personalized recommendations for their users.
In the short term, Spotify’s strategy is to develop unparalleled personalized music curation services using machine learning. It does this by mixing three machine learning techniques to create an accurate recommendation engine, namely collaborative filtering models, natural language processing models, and audio models, which analyze user behavior, lyrics, and raw audio tracks respectively.
Enabling this innovation is Spotify’s agile approach to its organization structure, which is highly informal. Working groups called “squads” work in loose structures to encourage creative design thinking. In addition, the company mandated that 10% of all employee time is spent on “hack time”, whereby employees were asked to test and come up with new ideas for implementation .
In the medium term, Spotify still needs to achieve profitability. Spotify’s main expense is its payments to rights holders, normally publishing labels, for licensing the usage of their songs. Spotify’s current sources of revenue are based on a two-tier model of free and paid subscribers . It may consider levers such as rethinking Premium pricing or lengthening the ad time of its Free offering, but it will need to do so in a way that does not alienate its user base.
Spotify will need to consider its reliance on published content. Netflix, which used machine learning to provide personalized recommendations for streaming video, managed to begin making profits by adopting a strategy of vertical integration, whereby it began to create its own content and reduce its dependence on licensing content from film and television studios . Given how Spotify has yet to achieve profitability, Spotify’s management may be wise to consider how the company can reduce its reliance on licensed content, though in the short-run the high upfront costs of developing original content may further harm Spotify’s profitability.
Yet another issue is the recent trend towards artist-owned streaming music services. Artists, who increasingly feel that they are getting slimmer portions of the revenue generated from their music, have turned to creating their own B2C solutions that cut out intermediaries such as Spotify. For example, Tidal, launched under the leadership of artist Jay Z, recruited Beyonce, Rihanna, Kanye West, among others . If artists reach out to their fans directly instead of relying on platforms such as Spotify, then Spotify’s value proposition of using machine learning to deliver what users want to hear will be materially diminished since the content can no longer be offered.
Finally, there has been a trend towards artists being encouraged to sign exclusive deals. For example, Apple Music, which is quickly growing to become one of Spotify’s biggest competitors, signed an exclusive deal with the hip-hop artist Drake, formerly Spotify’s biggest artist of the year, to launch content only available via Apple Music . Not only does this directly limit Spotify’s ability to stream more content, but it also indicates that Spotify may have to pay a higher price to attract and retain artists towards its platform.
Some are skeptical of Spotify’s long-term ability to remain competitive. In particular:
- Despite Spotify’s innovative machine learning solutions, the company remains unprofitable, primarily due to the high cost of licensing of its content. How do you think Spotify can change its business model to achieve profitability?
- Given the entrance of large tech companies in this space, such as Apple Music, how can Spotify maintain a technical edge in its personalized recommendation service?
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 Music Business Worldwide (2016). Apple Music’s biggest swipe at Spotify yet: Drake’s exclusive new album | Available at: https://www.musicbusinessworldwide.com/apple-musics-biggest-swipe-at-spotify-yet-drakes-exclusive-new-album/ [Accessed on 12 Nov 2018].