{"id":32709,"date":"2018-11-13T15:22:18","date_gmt":"2018-11-13T20:22:18","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/leveraging-machine-learning-at-spotify\/"},"modified":"2018-11-13T15:22:18","modified_gmt":"2018-11-13T20:22:18","slug":"leveraging-machine-learning-at-spotify","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/leveraging-machine-learning-at-spotify\/","title":{"rendered":"Leveraging Machine Learning at Spotify"},"content":{"rendered":"

Machine learning as Spotify\u2019s differentiator<\/strong><\/p>\n

With 159 million active users streaming 25 hours per month, Spotify is currently the largest music-streaming service in the world [1]. While the industry is growing in both users and streaming engagement, the space remains extremely competitive with big-name players like Apple, Amazon and Pandora. As of summer 2018, Apple Music surpassed Spotify in terms of paid subscribers in the US, and the gap expected to widen by this Christmas [2].<\/p>\n

Spotify\u2019s \u2018Discover Weekly\u2019 functionality, a personally curated playlist provided every week to subscribers, however, allows users to discover new music and is a key differentiator which allows Spotify to remain competitive. To deliver this unique and personalized experience, Spotify leverages three machine learning approaches in predicting what tracks go into a subscriber\u2019s playlist: \u201cCollaboration filtering (what you listen to vs. other listeners to), natural language processing (analyzing content on blogs and websites on the internet) and audio analysis (analyzing data behind each music track)\u201d [2]. This level of personalization is \u201ctough to copy and Spotify\u2019s scale gives it a distinct advantage\u201d over competitors [1].<\/p>\n

Strategic management decisions <\/strong><\/p>\n

In the short-term, Spotify is making strategic acquisitions in the machine learning space in order to further develop their recommendation engine and algorithms. With their 2017 acquisition of Niland, for instance, Spotify will use \u201cAPI-based product and machine learning to provide its users with better search and recommendations to help them discover music they like\u201d [3]. Earlier in 2017, they also acquired MightyTV, a service for recommending TV and films that users might enjoy based on their tastes [4], and Sonyaltyic, the makers of an audio detection technology that can identify \u201csongs, mixed content and audio clips [\u2026] and aid in music discovery\u201d [5]. These acquisitions allow Spotify to push innovation in their recommendation algorithms and continue delivering the best Discover recommendations to subscribers.<\/p>\n

In the medium-term, Spotify plans to use machine learning to alternatively help artists and labels with music and content creation. As the space of AI-assisted music has witnessed a \u201cdramatic growth in ability and output\u201d in recent years through \u201cprojects like Google\u2019s Magenta and IBM\u2019s Watson that feed musical rules into machines and teach them to mimic human creativity,\u201d Spotify has also started to make long-term investments in machine learning for music creation. Within the last year, Spotify brought on Francois Pachet, a French professor and artificial intelligence-focused researcher, to head their new Creator Technology Research Lab. Through the lab, the company intends to create tools which will help artists in their creative process. As Fast Company notes, an end goal is a system or tool \u201cthat can auto-generate music in a given style and serve as a sort of creative companion for artists\u201d [6]. These tools, for example, could identify upcoming trends in music and help artists incorporate these trends in new content.<\/p>\n

Other potential opportunities<\/strong><\/p>\n

As I reflect on broader recommendations for Spotify management, I believe there is an opportunity in the short-term for the company to use streaming data to make predictions on broader preferences a subscriber may have (what brands they may like, which fashion trends they may be into, etc.). A custom luxury menswear startup, Eison Triple Thread, is an example of a company which uses music tastes to better understand their customers. The company recently introduced an app that recommends clothes based on users\u2019 Spotify data. As their CEO mentions, you \u201ccan infer a lot from people\u2019s music choices. The algorithm sifts through a user\u2019s Spotify data and pairs music genres and favorite artists with styles [\u2026] the customer eventually is served pieces that reflect his personality as well as personal style\u201d [7].<\/p>\n

Furthermore, I believe there is an opportunity to use machine learning to identify patterns among subscribers and sell these insights to artists and labels. Are subscribers listening to my music and is now a good time to go on tour? If so, could I tour with another artist (e.g., an artist that my fans also like)? What cities and concert venues am I most likely to be profitable? These key questions could be answered by pairing streaming data and machine learning predictions.<\/p>\n

In the more medium-term, as Spotify further develops the AI-based tools to identify music trends, I recommend that the company also use machine learning to identify up-and-coming artists before they are signed to the large recording labels (where Spotify currently pays out more than 70 percent of its monthly sales to rights holders [8]). Spotify could potentially license these artists directly, removing the large labels from the value chain and capturing a greater share of profits.<\/p>\n

Two questions for the group<\/strong><\/p>\n