They have made additional acquisitions to increase their technical capabilities such as Mighty TV, Niland, Sonalytic and SoundTrap. Since, Spotify has continued to focus on machine-learning powered personalization. The software then combines this information to make recommendations based on both how the music sounds and how it is being reviewed externally. Specifically, EchoNest uses machine listening software to analyze songs (including features such as tempo, pitch, vocals, and energy) and crawls the web to index descriptions and reviews of music. In 2014, Spotify acquired EchoNest, a “music intelligence company” that many of its competitors used in their recommendation engines. Spotify recognized early on that to keep listeners engaged at scale, they needed to use machine learning to personalize recommendations for listeners. Source: Financial Times , “How Streaming Saved The Music Industry” Spotify’s Investment in Machine Learning Spotify has invested heavily in both capabilities and to win in the space they will need to continue to innovate. Core to continued user engagement is a strong recommendation and personalization engine powered by data and machine learning – satisfied listeners will find the right song for each moment throughout the day. With more musical content accessible online than ever before, Spotify must compete to keep users (and in turn brands and advertisers) engaged with and paying for its platform. Apple Music has approached Spotify’s lead and in May 2018, reported that it had 50M subscribers (paying and on trials). The most popular paid streaming services are Spotify, Apple Music, Deezer, Rhapsody, Google Play and Tidal. As it has grown, the industry has also become highly competitive. The music streaming industry has grown rapidly over the past few years and in 2017, surpassed 100M paying subscribers worldwide. The Music Streaming Industry & Personalization
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