Machine learning has played a crucial role for Spotify and the music streaming industry more broadly. Before services like Spotify or iTunes, producers, artists and content distributors did not have the level of access to listener’s individual music preferences that they have now. Through listener’s real time data, streaming services can detect popular music trends and microtrends with much greater velocity and accuracy than previous music distribution channels, including radio or album sales, could ever accomplish. Streaming services’ unprecedented depth (individualized) and breadth (macrotrends) of data has provided fertile ground for emerging technologies such as machine learning to play a crucial role in content management.<\/p>\n
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As streaming services play a more prominent role in the music discovery and listening process, they compete and differentiate in their ability to deliver the right content to listeners. Streaming services therefore act as a marketplace where their goal is to adequately match music supply and demand. Streaming services that can accomplish this effectively, enter a virtuous cycle as a high number of quality artists (supply) attract more listeners (demand); then more listeners attract more artists to join the platform. This positive feedback loop incentivizes streaming services to invest heavily in tools such as machine learning. For instance, on October 2018, Apple acquired a machine learning startup called Asaii which lets producers and labels “discover, track and manage talent” by analyzing listener’s data from streaming services and social media. [1] The company claims that, through their machine learning algorithms, they are able find artists 10 weeks before chart.<\/p>\n
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Machine Learning as a Competitive Differentiator<\/em><\/strong><\/p>\n <\/p>\n Management at Spotify recognized early on that optimizing its marketplace capabilities would be a key source of differentiation among streaming services as customers would derive more value from the platform. In 2013, they acquired a startup called Echo Nest which uses machine learning and acoustic analysis to create tailored recommendation to users based on their individual listening. [4] Recommendations come in the form of notifications and bespoke curated playlists such as “New Music Friday” and “Discover Weekly.” The relevance of the content attracts users to the platform which stresses the need to have accurate and ever improving machine learning technologies. More recently, Spotify has continued improving on its machine learning algorithms and in August 2018, it filed a patent on technology that identifies personality traits based on user\u2019s listening history. The system assigns a “first personality trait” which then provides personalized content on this trait. [2]<\/p>\n <\/p>\n While tailored content might still prove to be a key differentiator in the near term, competitors, like Apple through its acquisition of Asaii for instance, are developing similar capabilities pressuring Spotify to find other ways to use machine learning to differentiate its product offering. In order to do this, Spotify hired researcher\u00a0Francios Pachet to develop tools that will help artists in the creative process. [3] Dr. Pachet has focused his research on how machine learning can help computers understand musical style and composition. More importantly, machine learning has started to be recognized more broadly as a viable way to augment the artist’s creative process. In the Journal of New Music Research, researchers Sturm et al partnered with musicians to utilize existing machine learning models such as folk-rnn, DeepBach, among others, to complement their creative process. While the machine generated music did not completely remove the artist’s creative process, it did help build on the creative process. One artist noted that, “[content] was ‘foreign’ to me, but served as\u2026 inspiration as well as a way of developing my own compositional language.”[3] Through Spotify, Dr. Pachet can access data on listener’s preferences to refine processes and identify what elements can be appealing to the targeted listener. Competitively speaking, Spotify would be providing tools to artists so they can create more meaningful content for listener’s and increase the overall value of the marketplace.<\/p>\n
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