Spotify: Tapping Big Data to Enhance Customer Experience

Spotify: Tapping Big Data to Enhance Customer Experience
Case Code: ITSY128
Case Length: 15 Pages
Period: 2006-2021
Pub Date: 2022
Teaching Note: Available
Price: Rs.400
Organization: Spotify
Industry: Leisure & Entertainment
Countries: Sweden
Themes: Big Data Strategy, Customer Relationship Management
Spotify: Tapping Big Data to Enhance Customer Experience
Abstract Case Intro 1 Case Intro 2 Excerpts


The case is about the Swedish audio streaming and media services provider Spotify and its use of big data to enhance customer experience. Launched in 2008, Spotify, the largest on-demand music service in the world, had more than 381 million active users in the third quarter of 2021. Its success is credited to its data-driven approach, where the company gathered data generated by millions of active users based on the tracks they are downloading, listening to, and sharing. Spotify used this data to generate suggested curated playlists like Discover Weekly, which have become highly popular and made Spotify a brand to reckon with in the online music industry. Spotify has also created several more curated playlists. Apart from playlists, Spotify used data to come up with analytics for artists, whereby the artists can get to know where their fans are located and what kind of tracks they are listening to. Further, the data is used to come up with targeted ads and for running ad campaigns. As Spotify continues to expand to more locations and keeps adding new users, it only means more data and better recommendations and predictions.


The case is structured to achieve the following teaching objectives:

  • Understand the advantages that organizations can derive from employing big data capabilities.
  • Evaluate the use of big data in the media services industry.
  • Analyze how the use of big data can help a music streaming company connect with its customers.



Spotify; Big data; customer experience; media services; Big Data Strategy; data analytics; analytics for artists; analytics for advertisers; prediction; data mining

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