How AI Is Enabling Streaming Services To Personalize Customers’ Mixes

How AI Is Enabling Streaming Services To Personalize Customers’ Mixes
Entertainment

Streaming services are changing the way consumers listen to music and enhancing their discovery process by providing a data-driven, customized experience

PSFK
  • 12 september 2018

Finding tracks that simultaneously are new and inspiring, yet that still appeal to one’s specific tastes can be a time-consuming process for the musically inclined. To ease their discovery odyssey, music streaming services are increasingly employing AI and machine learning to curate music suggestions, leveraging people’s listening patterns and behaviors, tastes, and preferences to help reduce information overload and automatically customize recommendation lists.

Making suggestions that listeners are likely to enjoy naturally requires knowledge of them, which technological advancements like AI can help to establish: They streamline the process of not only tracking users’ histories, but also of accumulating data on what listeners with similar traits and preferences tend to like to predict alternatives with high-probability for affinity with the user in question. Here’s how four companies are enhancing their streaming services in this manner:

Google Play Music
Google’s New Release Radio feature personalizes music recommendations based on users’ listening history on Google Play Music, as well as broader musical preferences, using machine learning technology to select from singles and album releases from the past two weeks.

Amazon
Amazon uses machine learning to learns users’ musical tastes and affinities based on playback duration data to infer song preference and predict their musical inclinations, combined with collaborative-filtering techniques to estimate how a particular customer might rate a song that he or she has never requested.

Apple Music
Apple Music uses machine learning to suggest music tailored to the particular user’s tastes, preferences, and listening patterns.

Saavn
India-based music streaming service Saavn uses AI for gender prediction, age prediction, genre prediction and churn-risk prediction to recommend music.

These are just a few examples of ways that major brands are enabling better music experiences. For more on how technology like artificial intelligence is enhancing how listeners discover and enjoy music, see PSFK’s report Leveraging AI To Remix The Music Experience. 


Lead Image: listening to music stock photo from George Rudy/Shutterstock

Finding tracks that simultaneously are new and inspiring, yet that still appeal to one’s specific tastes can be a time-consuming process for the musically inclined. To ease their discovery odyssey, music streaming services are increasingly employing AI and machine learning to curate music suggestions, leveraging people’s listening patterns and behaviors, tastes, and preferences to help reduce information overload and automatically customize recommendation lists.

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