Building on its rich data set and leveraging best-in-class intelligent automation, the Swedish company is testing a new hyper-personalized feed on its app home screen. The stream is intended to introduce users to new music for their enjoyment. Each day, the thread recommends about fifteen songs accompanied by “Cloth” loops – Spotify dynamic GIFs that appear in the background of songs and are presented in a ICT Tac– vertical video stream inspired. Spotify listeners are more likely to continue listening to songs with accompanying videos on canvas, as well as more likely to share the track, either by text or on other social platforms like instagram where web loops also appear.
Daily song recommendations can be “refreshed” with Improved Spotify feature, which gives the app’s powerful algorithm the green light to automatically add songs it thinks will fit into the “vibe” of the curated playlist. What’s unique about the new feed is how Spotify searches for songs it recommends to listeners: by mood and emotion.
There are more than four billion user-created playlists on the Spotify platform, and more are being generated every day, and the composition and naming patterns of these playlists give Spotify data scientists clues. important about the emotional content and context of the songs they contain.
For example, if a song is on, say, twenty thousand playlists with the word “Happy” in their title, chances are the song is upbeat and cheerful. Whereas if another song is in a “Rainy Bus Ride” playlist, it’s more likely to be reflective and introspective. By sorting this information and combining it with user-specific behavioral data, Spotify can provide tailored playlists and follow-up recommendations that deliver the perfect fit for the experience a particular user is hoping for when browsing. leads to the platform.
This article originally appeared in the PSFK iQ report, Optimizing Customer Journey Personalization.