Data Spotlight: Spotify Wrapped Music Data

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Data Spotlight


The listeners of Spotify are familiar with the annual hum known as

Spotify Wrapped but not with the science used for music data. At the end of every year, Spotify provides music dat to its users with a summary of not only their music history, top artists, and favourite genres, but also the total minutes of music, and more. In addition to that, they provide it all wrapped up in a very beautiful, colourful display. What excites us is to know that all of this is done by using big data. 

In this blog, we aim to help you understand the working procedure of Spotify Wrapped and the reason for its effectiveness. Spotify is the biggest example of someone that leads in creating an emotional connection with its consumers by using data especially music data.

How Spotify is using data to narrate the story?

There is no way that Spotify Wrapped involves the use of basic analytics. When the year-end arrives, users get a report. This report, mentions that those users are in the top 1% of a band’s most loyal followers. In addition to that, they mention if they are among the bold, non-mainstream listeners. However, the presentation of the data is something that excites people. It makes them feel like it’s a personality test. In simpler words, Spotify provides its music data back to its users. Spotify users love this feature. For them, it could be like sharing the songs that they like or it could be that Spotify acknowledges favourite artists. That is the kind of emotional validation that makes users feel personable. Not only is their music data neatly packaged, but also is very well presented. Haley Weiss mentions in The Atlantic, seeing top songs on Spotify Wrapped is like seeing an old best friend that you lost touch with”. This is how Spotify attaches its users to itself. The listening platform of Spotify is designed to include built-in ego-boosters for its listeners.
Behind making Spotify Wrapped as one of its, important marketing models, Spotify has geared up for such kind of organic engagement. The Spotify listeners take screenshots and put them on social media platforms of their profile. In addition to that, they share the links to their playlists, that show their friends and families the kind of music they have been listening to. This way, Spotify becomes self-marketable as users organically publicize their engagement, using music for the biggest social experience.

How does Spotify Wrapped perform?

Generally, Spotify relies on three recommendation models:
  • Filtering Collaboration: This uses information about other listeners which contributes to the algorithm’s understanding of the users’ listening habits. The users who have similar habits are more likely to have similar taste profiles. Spotify then analyzes the duration of the music to map out where the trends of listening fall.
  • Natural Language Processing (NLP): This technique allows Spotify engineers to turn playlists into text documents. Hence they can use it to identify and analyze relationships between lyrical patterns. NLP is also useful for navigating music without lyrics. This is because it goes across genres and brings users new alternatives to what they’re used to.
  • Audio model Feature: These are key to Spotify’s ability. This feature matches the listeners with newly released music based on their past listening trends and preferences. For instance, Spotify uses audio models and neural networks to “process raw audio to produce a range of characteristics, including key, tempo, and even loudness.” This feature is vital for emerging artists on their way to being discovered given all the music data available.

The biggest wonder that design and data could do together

Creating insights is always more intuitive than just the raw data. If Spotify would have just shown a list of songs, the users would not have been more interested. Hence, that would have been a roadblock in the original motive of the people in Spotify—to enhance their marketing. 
Making insights not just useful but also attractive. It is one of the finest work of data and design together.  Spotify Wrapped is a brilliant example of how powerful UX design can create intrigue engage users. 
The UX design is the only thing behind creating this special connection. There are a number of reasons why it works:
  • Interactive Visuals: The visuals used in the report are attractive because Spotify Wrapped uses specific palettes according to color psychology to get positive reactions. Bright, neon colors trigger personal happiness and excitement, however, here Spotify modifies them into uncommon hues. It naturally grabs attention when shared on social media. 
  • Simplified sharing on social media: Spotify Wrapped loves when its users share their content on social media. Hence, it creates organic marketing for not only the music but for the platform itself.
  • Beautiful presentation: When engaging with the report, users will notice that pictures move alongside the mouse or smoothly glide across the screen. It’s easy to navigate and extremely fluid, and there’s even a degree of customization a user can do before sharing it, like changing the background or font colors for ideal personal branding. This allows users to spend more time and gradually develop a sense of liking for the product. 

Making content sharable

The whole point is about making the content sharable. That’s when it gains audiences and attracts eyeballs. As a result that resulted in the success of Spotify Wrapped. Humans like it when something comes to them in an insightful and interactive manner. Giving us a taste of our own music backed with data brings the thrill of discovering something new about ourselves. Such a type of self-quantification has become more popular recently. Not only is it a matter of creating joy or discovery but also about improving the personal experience. 

Data Science involved in Spotify

Spotify’s head of creator product insights, Sara Belt describes Spotify’s data science methods to be a cross-disciplinary approach. Spotify turns to data science strategies not only to understand their user’s music data but also their users. Spotify has a brilliant product insights team, which is basically a combination of its user research and data science teams. This helps to enhance the research methods to “drive evidence-based decision making”. For both consumer data and the success of the brand, one needs to focus on cultural literacy as well. Then the brand gains a comprehensive understanding of their market. 

Spotify’s ‘ANNOY’ Music Data Library

Much contradicting the literal meaning of Annoy, one of the most amazing ways Spotify uses data is by creating a library that accurately predicts the preferences of the listeners’. However, these recommendations are not just something out of the blue. Spotify created a library called ANNOY. Github explains ANNOY as: “ANNOY (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that use mmap into memory. Hence, many processes may share the same data.”
In simpler words, Annoy searches for points that are close to a particular query point. In Spotify’s case, the users’ songs recommendation is based on their listening preferences and patterns. Annoy also can allow multiple processes to share the same data. This is done by creating large, read-only file-based data structures that are mmapped into memory. 
Spotify, made the library open-sourced, allowing millions of data scientists and developers access to use the technology. By making their data available publicly, Spotify joins a growing list of companies who attempt to use the data to drive positive change and create a better environment for aspiring data scientists.  
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