The cools ways in which Spotify recommends music to you.
Today, Spotify is the world’s leading music streaming company with over 150 million years. When it was launched in 2008, streaming music via the internet was already there. But what made Spotify unique was its use of data analytics to curate playlist.
Spotify generates an astonishing amount of data each day. It uses these data effectively to generate insights. With these insights, it creates a personalized playlist for each user based on their interest. This efficient use of data made Spotify different from iTunes, Pandora, and other services present at that time.
How does Spotify use analytics? What kind of analytics does it use on what type of data? Let’s see.
Natural Language Processing to identify popular music
If you’ve been using Spotify, then you know it creates a new playlist every week for you called Discover weekly. The playlist has songs that you like but you’ve never heard of. How does Spotify find new songs that you like?
One way is it uses Natural Language Processing. We know NLP is how the computer understands human language. Spotify uses NLP to crawl through the web and blogpost to understand what songs or artists do users talk about. The exact mechanism of how Spotify uses NLP is beyond the scope of this post.
After identifying new songs or artists, Spotify then assigns weight to it based on the popularity that changes each day. Then, it uses other techniques like collaborative filtering or audio analysis to recommend the new songs to the respective users.
Audio Analysis to find similar songs
Spotify finds new songs using NLP but how does it actually recommend to the users?
For every song present, Spotify uses neural network analysis to find different characteristics about it. Data such as tempo, loudness, time, etc about every song are present in the Spotify database.
For every new song that comes into Spotify, it uses the same mechanism to find the characteristics of the song. If the characteristic of the new song matches the characteristic of the song that you liked or saved, then Spotify recommends the song to you.
For this process to work, Spotify needs to collect data about you. Data such as the songs you’ve liked, saved, listened to repeatedly, artists you’ve visited are collected and used to find similar songs.
In this process convolutional neural network plays a big role. It analyses the raw audio data to find various characteristics about it. Convolutional Neural Network is a type of deep learning algorithm that is primarily used to analyze the visual data. Here, Spotify modified the algorithm to work with audio data.
Netflix is another big player that uses this kind of analytics technique to recommend similar movies.
Analyzing raw data using neural networks is just one of the processes to find similar songs. Collaborative filtering is another mechanism to find the songs that the user may like.
It is a cool technique that finds the common songs that different users like. Say you like songs a, b, c, and your friend likes songs c, b, e. Then, it is highly likely that you like the song e cause there is a high similarity with your friend. This is how collaborative filtering works. But Spotify uses this mechanism on all the songs and users present.
This is a highly complex task on a large scale. Spotify uses various data mining tools and algorithms to find similar songs between different users and recommend to them.
These are the main ways in which Spotify uses data analytics in recommending new songs. There is also another important way Spotify uses analytics to provide value. It is in the Spotify artist app.
Spotify artist application
Spotify has a different app just for the artist creating music. The app gives real-time statistics about how their music is performing. Statistics like the number of downloads, number of users listening, likes, etc about their songs are analyzed and displayed to the artists.
The application is like Google Analytics but for Spotify and songs.
With yearly roundup music, variety of curated playlist, songs based on weather, mood, and condition developed using analytics, Spotify is purely a data-driven company. With more users joining Spotify, and new songs created each day, the use of data analytics is only going to grow in the future for Spotify.
Spotify has won the battle in the music streaming thanks to data analytics. People think that Spotify knows more about their taste that they do. Now it is time to win the battle of Podcast for Spotify. The popularity of podcasts has grown significantly over the last five years. Knowing this, Spotify has invested hugely in the podcast industry. Spotify has recently started to recommend the podcasts for its user’s using analytics. They’ve even created podcasts for your pets. Yes! you’ve heard me right. Podcast for your pets, and music for you.
The future is bright for Spotify. It all happened because Spotify was one of the first to understand the value of analytics and use it on their platform.