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Data Science in the Music and Entertainment Industry

Picture of Mahzabin Khan Mahzabin Khan Sep 27, 2021

Data Science Helpful Resources DataDrive Music Entertainment

Data Science in the Music and Entertainment Industry

As an aspiring musician and data scientist, I have always wanted to see how the two worlds collide. As technology has made data increasingly accessible, data science continues to transform and shape how we think about problems across various industries. The music and entertainment industry has a vast number of applications for predictive and prescriptive analytics which include finding the top trending song, creating the next big hit, and even providing analytics in various streaming platforms like Spotify, Netflix, and YouTube. Today we will be taking a look at a few use cases for data science in the music and entertainment industry. Let’s begin!

Audience Engagement Analytics

With the rise of online music streaming services, there is now an abundance of music consumer data. With all this new data, distributors like Spotify and Apple Music can provide audience demographics and location information to help artists understand the details of their core audience. YouTube also creates dashboards that demonstrate viewer demographics, watch time, and location. These statistics can be used to answer critical questions such as:

How long did my audience spend listening to my song?
Where do my listeners play my song?
How many times was my song skipped or saved?

These insights can then be used to decide which songs to promote and where, optimal release time for a song, etc. Along with being musically talented, the industry now depends on data science to help determine which songs, genres, or artists will cater to a larger audience.

Designing the Next Big Hit

You may have noticed in your listening that many music producers have been creating similar-sounding songs for various artists in order to recreate a big hit. Similar-sounding music could have the same genre, lyrics, or even the same beat. The goal of creating similar songs is to familiarize the audience with the same type of music and to increase the chance that they will listen or buy it. This type of music is generated by analyzing years of data and identifying popular trends using machine learning techniques. A huge amount of research goes into producing the next big hit using data science techniques such as logistic regression, random forest, and support vector machine. Sony famously released a song built from their Flow Machine's artificial intelligence project (with a little human help too), and another project used artificial intelligence to analyze MIDI music to create brand new tracks from artists or bands with members that have since passed away (using tribute bands to then perform and record the new creations), such as Nirvana and Amy Winehouse.

people dancing in club

Spotify: Discover Weekly

Discover Weekly is a super cool feature offered by Spotify where listeners receive a personalized playlist based on their taste and listening history. Spotify uses machine learning along with various data aggregation and sorting methods to create a recommendation model. Spotify primarily uses three forms of recommendation models.

Collaborative Filtering: This process includes comparing multiple user-created playlists that have similar songs. The algorithm goes through each playlist and identifies other songs that appear to be alike and recommends those songs.
Natural Language Processing (NLP): Spotify uses NLP to analyze speech and text in real-time. This algorithm scrapes the web to find any text related to the music in order to create a profile for each song. Based on the data found, the algorithm classifies each song according to the type of language, theme, and keywords.
Convolutional Neural Networks (CNN): Lastly, Spotify uses CNN to increase accuracy and ensure less-popular songs are not neglected in the model. CNN converts audio files to waveform, which are assigned key parameters such as beats per minute, volume, major/minor keys, etc. Using these key parameters, the model tries to match others songs with similar patterns.

Using these awesome machine learning models, Spotify creates beautifully tailored playlists for each listener every week!

eating popcorn watching netflix

Netflix: Recommendation Builder

When Netflix told me that the show “Ozark” was a 98% match, I was skeptical. I didn’t think a show about money laundering and drug lords was my type, but turns out Netflix knew me more than I did! Ozark was one of the finest shows I’ve watched, thank you data science team at Netflix! So how does it work?

Netflix collects customer information such as when a user watched a show, how many times did the user pause, did the user resume watching, what did the user search, what type of shows did the user watch previously, etc. Using this data, Netflix has built a profile for each user. Then, like Spotify, it uses machine learning techniques such as Collaborative Filtering and Neural Networks to recommend content that falls within that criteria!

YouTube: Fake News and Trash Content

Oftentimes, YouTube, as well as social media platforms like Facebook and Instagram, are filled with fake news. In an attempt to flag fake and misleading information, YouTube adopted artificial intelligence. The first stage of building an AI tool with these capabilities involves it learning how to read and identify bias, which is a very complex method. Using AI, YouTube was able to remove around 11 million videos during the initial days of COVID-19.

Furthermore, YouTube also uses machine learning algorithms to identify deceptive or inappropriate videos. NLP can be used to analyze video titles and decipher whether the content is appropriate or not. The algorithm also examines feedback received from users who report the videos that have misleading thumbnails or click-baits.

people on devices

Conclusion

Although this blog has reviewed a few high-level concepts and applications of data science in the music and entertainment industry, we're just starting to scratch the surface of what's possible with applying data science to this space. The foundations of a good model is almost always grounded by a question. If you have a question (and some data!), there’s a high possibility that data science can be used to help enrich your consumer's experience, and even your business.

And there you have it! I hope you enjoyed reading this blog as much as I enjoyed writing it. If you'd like to talk more about applying data science in the music industry, use the links below to find me on social media or the "Let's Work Together" button above to reach out to our team!

Let's Talk Data Science!