Spotify will be able to use their data to create an even more personalized experience and lock users into the service. 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.Īs 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. 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. Another internal department that relies heavily on data analytics is the product team. This is a creative way to humanize the vast amounts of data that they have and encourage new users to check out the service. Spotify was able to roll out specific ads to the region in which it would resonate the most. 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. Lastly, Spotify has been able to utilize the vast amount of data in various internal business processes. 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. Another effort dedicated to the artist aspect of the platform is the Creator Technology Research Lab, led by Francois Pachet. Managers have used these analytics to inform tour dates, locations and timing of new albums/singles. This gives artists more control over their product and helps them understand their audience better. 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. 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. Pathways to a Just Digital Future Watch this tech inequality series featuring scholars, practitioners, & activists 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. The recommendation will only become smarter over time as more and more data is fed into the ecosystem. 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. 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. 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’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. Utilizing user data and proprietary algorithms, Spotify has been able to sort and prioritize the content to create a superior, personalized user experience. As of December 2019, Spotify has 217M users that log over 100 billion data points per day. Spotify is an on-demand music service that utilizes big data and artificial intelligence to stand out in a crowded music streaming space.
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