{"id":34862,"date":"2018-11-13T18:32:58","date_gmt":"2018-11-13T23:32:58","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/spotify-jammin-away-with-machine-learning\/"},"modified":"2018-11-13T18:32:58","modified_gmt":"2018-11-13T23:32:58","slug":"spotify-jammin-away-with-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/spotify-jammin-away-with-machine-learning\/","title":{"rendered":"Spotify: Jammin\u2019 Away with Machine Learning"},"content":{"rendered":"

The music industry owes its survival to streaming services, which contributed 65% of U.S. music industry revenues in 2017 [1]<\/em>. The current market leader in number of subscribers, Spotify relies heavily on machine learning to create a highly personalized product for music consumption. Moving forward, their critical advantage will continue to be their ability to use their evolving dataset of customer preferences to refine and build a customer-centric product. \u00a0Machine learning is central to Spotify\u2019s core product development, as each customer interacts with content that evolves constantly based on customer behavior and feedback.<\/p>\n

Spotify has shown its commitment to cutting-edge machine learning through a series of acquisitions of startups with advanced AI search and content recommendation algorithms [2]<\/em>. In the short term, Spotify will continue to use these capabilities to personalize playlists and content for their 83 million paying subscribers [3]<\/em>. Spotify uses machine learning based recommendation engines to package and elevate the most relevant content for each user with a variety of radio and playlist products, including Daily Mix, Discover Weekly, Release Radar, and Spotify Radio [4]<\/em>. The result is essentially a curated \u201cnews feed\u201d of content for each user based on their historical use as well as behavior of users with similar profiles. Spotify can use this personalized content to drive users towards specific artists, based on both user preference as well as Spotify\u2019s strategic goals.<\/p>\n

According to Spotify Insights, part of their goal with personalized, algorithmic playlists is to drive an increase in artist diversity for customers, which correlates with an increase in the number of minutes users spend on Spotify [5].<\/em> This capability likely has benefits down the road as Spotify may be able to drive users towards artists that Spotify is prioritizing for strategic reasons like cost. Beyond the consumer, Spotify is beginning to create products to share insights with other stakeholders in the music ecosystem. For example, Spotify recently released an analytics tool for music publishers focused on new opportunities and customer trends [6]. <\/em>In the short term, Spotify should continue to create data driven products that assist other players in the ecosystem with music and lyric idea generation and development to supplement core processes in music sourcing, development, and promotion across the value chain. Spotify has hinted at these aspirations: \u201cWe want to be the R&D department for the entire music industry,\u201d said\u00a0Gustav S\u00f6derstr\u00f6m, Spotify\u2019s chief R&D officer, \u201cWe don\u2019t think the industry has ever had an R&D department before — and we\u2019re it. That\u2019s our mission [7]<\/em>.\u201d<\/p>\n

As the music industry evolves, traditional record labels stand to lose much of their influence as streaming players dominate the customer interaction and distribution. Right now, record labels like Sony and Universal still retain significant bargaining power, as they own the content of today\u2019s most popular artists. \u00a0Spotify has historically paid just over 50% of its revenue to major record labels and does not directly own the rights to any content [8]<\/em>. However, Spotify is quietly shifting into more direct partnerships with artists and it seems likely that in the future music landscape, fewer artists would need record labels. With this future in mind, machine learning could very much enhance Spotify\u2019s strategic direction with both artist sourcing and product development. With machine learning, Spotify will have greater forecasting visibility into future musical trends and could use this to identify and develop new artist talent. Using their existing dataset of customer preferences and trends, Spotify should identify unaffiliated new artists with potential that they can invest in. Current record labels will be at a disadvantage without Spotify\u2019s data stream as Spotify moves to fill the sourcing and marketing role that record labels have traditionally held.<\/p>\n

Additionally, Spotify has an opportunity to further expand its role in the music supply chain process, by using machine learning to supplement or replace the traditional music development process. Last year, Spotify faced controversy that it was promoting AI composed music in its ambient and chill playlists [9]. While Spotify has denied giving preference to AI composed music, they recently hired a pioneer in AI composed music to lead Spotify\u2019s Creative Technology Research Lab [10], <\/em>as well as a Chief Content Officer [11].<\/em> It seems that content development, whether through machine learning or direct artist partnership would allow Spotify to control more of the music supply chain and reduce dependency on record labels in the future.<\/p>\n

The two open questions that I have for my classmates on this topic is:<\/p>\n