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How Does Spotify Get Along Well With Its Listeners?


Spotify stands out from its other music streaming competitors, with 286 Million users logging over 100 Billion data points every day. It utilizes big data and artificial intelligence to sort and prioritize its content to create a more personalized user experience.

Hosting over 50 Million songs and 4 Billion playlists, and storing massive amounts of data related to song preferences, search behaviour, geographic location, and playlist data. All of this is possible through data analysis and machine learning algorithms. Based on the user data above it better understands the musical taste of people, and makes it easier for them to discover new artists, songs, and genres.

Spotify has been one of the biggest Google Cloud Platform customers since February 2016. Back in 2018, being a visionary, it announced to spend around $450 million on its Google Cloud infrastructure in the following three years. Spotify has also acquired several innovative data science companies to remain at the forefront of the music streaming industry.

The predictive recommendation’s engine of Spotify enables to curate playlists such as Release Radar and Discover Weekly.

Discover Weekly

Each Monday, every user is presented with a personalized list of 30 songs. The recommended playlist is a curated list of songs that users might have never heard before but made solely based on the user’s search history pattern and music preference. In this way, not only does Spotify portrays itself as a platform for popular existing musicians, but also one who provides opportunities to budding musicians trying to gain recognition.

Spotify's Discover weekly playlist
Source: TheVerge

This is a brilliant recommendation engine which creates significant value for customer satisfaction. And, it will only get smarter with time as more data is fed to it.

Spotify For Artists

It provides an all-inclusive perspective to artists on their content. Through visualizations to help artists understand user engagement, demographic details, performance metrics and monthly/ daily users.

Spotify has been able to utilize its vast amount of data in various internal business processes too.

In a global ad campaign, they combine user data to create catching, playful titles in their ads highlighting user behaviour. Also, rolling out specific Ads for the region in which it will have more appeal.

Another team that depends on data analytics is the product team. They utilize A/B testing, along with detailed quantitative and qualitative data analysis. For example, while allowing users to skips ads, on a free subscription, they were sure that several Ads that could be skippable should be in sync with the number of skippable songs delivering a consistent UX.

Spotify’s Valuable Acquisitions

Back in 2017, Spotify went on an acquisition spree to improve their technology behind personalization. Niland, a French firm was one such significant acquisition, describing itself as,

“A music technology company that provides music search and discovery engines based on deep learning and machine learning algorithms”.

It was phenomenal for Spotify and led to significant service improvements for its listeners. All possible by leveraging Niland’s machine learning to generate better searches and music recommendations, enabling users to discover the music they like effortlessly.

Blockchain technology in the Music industry is a popular topic. Adhering to which Spotify also acquired a Blockchain company MediaChain labs in 2017.
The music industry has seen various transitions from CD to MP3 downloads to streaming, Making it difficult to keep track of trillions of data points required to make the correct royalty payments. Through Mediachain, Spotify aims to keep the whole process transparent.

So how does Spotify know you so well?

Collaborative Filtering:

It compares a user’s behavioural trends with other users. Netflix uses collab filtering to make recommendations for similar users using star-based movie ratings by viewers. Spotify, on the other hand, uses implicit feedback, times a user played a particular song, saved a song, or clicked on the artist’s page and provides relevant recommendations to the user with similar behavioural trends.

Collaborative filtering in Spotify
Source: TowardsDataScience

Natural Language Processing (NLP):

Spotify’s AI scans the metadata of a song from the internet i.e. blog posts, discussions about the musician, new articles about the artist or new releases from the artist. It keeps track of what people are saying about various artists, the language being used, and other songs or artists being discussed alongside. It then identifies descriptive terms, noun phrases and other texts associated with these songs or artists. These terms are considered as keywords and then categorized into “cultural vectors” and “top terms”.

Each artist and song is associated with many such terms subject to change daily. Also, Each term is assigned a weight, reflecting its relative importance in terms of how many times an individual would attribute that term to a song or musician they like.

use of NLP in Spotify
Source: Universityof Melbourne

Audio models:

Audio models are used to analyze data from raw audio tracks and categorize songs. It helps the platform evaluate songs to create valuable recommendations, regardless of its publicity on the internet. For example, if there is a new song released by a budding artist, NLP might not be able to pick it from the internet if its publicity is low. However, through audio models, the collab filtering model will analyze the track and recommend it to similar users alongside other more popular songs.

Audio models
Source: Plos

Future of Spotify

As users continue to grow the amount of data also expands. Spotify will be leveraging over recording labels and artist, who are desperately relying on the user data to make decisions more than ever. Spotify is also investing a lot more on podcasts space and further collecting data on user preferences to cross-promote new contents and increase engagements.

Spotify business model


The basic idea behind Spotify is to enhance automation of recommendation of songs to its listeners just like Netflix does with shows and movies. Facebook does with friends and Amazon with Books. One of the vital vision of Spotify, one it is continuously trying to improve is to recommend new and unpopular music to its listeners to make it possible for these artists to meet their target audience.

Being Pioneers in the field of music and technology, they are certainly gaining various experiences (learnings or failures). Spotify has been acquiring several innovative data science companies and aims to continue to keep its innovative sprint in the future too.

For technical reference read Brian Whitman and Sander Dielman.

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