Spotify is an on-demand music service that utilizes big data and artificial intelligence to stand out in a crowded music streaming space. As of December 2019, Spotify has 217M users that log over 100 billion data points per day. Utilizing user data and proprietary algorithms, Spotify has been able to sort and prioritize the content to create a superior, personalized user experience.
Spotify’s application hosts over 50M songs and 4B playlists, garnering massive amounts of data related to song preferences, search behavior, playlist data, geographic location and most used devices. Spotify performs analysis and creates machine learning algorithms based on this data to understand music tastes and ease discovery of new genres, artists and songs.
Spotify utilizes AI through their predictive recommendation engine which enables them to curate personalized playlists such as “Discovery Weekly” and “Release Radar.” The engine is built upon a combination of collaborative filtering, natural language processing and audio models to create a personalized list of thirty songs for each user. This type of recommendation engine creates value for artists who get more exposure to new users and makes customers stickier through increased satisfaction with the service. The recommendation will only become smarter over time as more and more data is fed into the ecosystem. Similar to the recommendation engine, Spotify is also experimenting with AI to facilitate the search process and streamline the user prompted discovery of new music.
Spotify has also created value specifically for the artists and managers on their platform by creating a “Spotify for Artists” tool which gives artists direct access to their data. To provide a holistic perspective on their content, Spotify has created visualizations to help artists understand user engagement, monthly/daily listeners, performance metrics and demographic details. This gives artists more control over their product and helps them understand their audience better. Managers have used these analytics to inform tour dates, locations and timing of new albums/singles. Another effort dedicated to the artist aspect of the platform is the Creator Technology Research Lab, led by Francois Pachet. The group’s goal is to utilize various AI tools to “push artists and songwriters into exciting and unchartered creative territory.” Spotify has even released songs that were wholly generated by these new AI tools.
Lastly, Spotify has been able to utilize the vast amount of data in various internal business processes. In a global ad campaign, Spotify rolled out ads that aggregated user data to create catching, playful titles that highlight specific user behavior on the platform. Spotify was able to roll out specific ads to the region in which it would resonate the most. This is a creative way to humanize the vast amounts of data that they have and encourage new users to check out the service. Another internal department that relies heavily on data analytics is the product team. They utilize A/B testing, paired with detailed quantitative and qualitative data analysis, for any new feature development to understand the true impact on user experience and behavior. For example, while testing a feature allowing users to skip ads, they realized that the number of ads that could be skipped needed to be in sync with the number of skippable songs to deliver a consistent user experience.
Opportunities and Challenges
As users continue to use the platform and more users join, Spotify’s competitive advantage stemming from their data, analytics and AI will continue to grow. Spotify will be able to use their data to create an even more personalized experience and lock users into the service. Additionally, as the amount of data grows, Spotify will gain more leverage over recording studios and artists who desperately need access to the data to make business decisions. Spotify is also starting to invest heavily in the podcast space and can leverage their knowledge on users’ preferences to cross promote this new content and increase engagement. They will also be able to repurpose their existing recommendation engine to create similar features for podcasts.
However, as they continue to invest in the data space, Spotify must be careful not to alienate the fans or the artists using the service. Spotify has developed the capability to create music with AI tools that completely remove artists from the process. It will be important for the company to correctly articulate the purpose of this type of technology, so the creative talent is stays on the platform. From a user perspective, Spotify needs to be cognizant of user privacy and data protection laws as they increasingly depend on user data in all facets of their business.
To overcome these challenges and capitalize on the opportunities, Spotify must create AI tools that empower the artists and improve the user experience. It will be important for them to be transparent about the methods data is used so that both parties feel in control of their own data.
Spotify has been known to push the bounds of technology in a traditionally creative industry. It will be interesting to see how Spotify continues to balance technology and art to curate a positive experience for both artists and fans.