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Rolling Stone PODIUM: How Big Data Is The Music Industry’s Goldmine

Goa-based music business professional and DJ Rufy Anam Ghazi analyzes the pivotal role of data for artists, labels and even streaming services

Mar 05, 2023

(The views and opinions expressed in this blog belong solely to the aforementioned blog writer, and do not represent those of Rolling Stone India as a magazine)

“As much of the music industry remains online; data literacy has become more important than ever across all sectors

Cherie Hu (Founder & Publisher of “Water & Music”)

Let’s begin by putting things into perspective: The unique set of traceable digital activities, actions, contributions and communications you manifest over the internet becomes your digital footprint. Now visualize this for millions of users across the globe that have internet access. Each touch-point from all these users or their digital footprint contributes to data, and this large volume of data is known as Big Data. Big data is a term that reflects the amount of information people generate – and like you just visualized, it’s a LOT!

Now let’s go back into history, before the prevalence of the Internet. Some of the most prominent artists, such as The Beatles, Queen, and David Bowie, rose to fame through playing grand live shows, investing in on-ground promotional campaigns, hiring PR agents and signing radio/ad deals. And back then, this was all accomplished through the efforts of a record label endorsing these artists.

The current scenario is very much different. The digital revolution has completely transformed the music industry, giving artists more power than ever before. Every time a fan interacts with music through streaming services or social media, the touch points are recorded, giving artists and their teams a more comprehensive understanding of their listenership.

This abundance creates opportunities for artists and their teams, allowing them to assess, compare and optimize their efforts to reach and engage with the right audience. By leveraging Big Data and analytics, one can gain near real-time insights into fan engagement across all platforms, such as audience demographics, the popularity of a song/artist/genre in a particular location, musical preferences, online trends and much more.

Let’s begin with understanding data: Data are individual facts, statistics, or items of information, often numeric, that are collected through observation (Cheers, Wiki!). A quick look at the Data Pyramid will tell us how these numbers convert into valuable information:

Level 1: Data
Unstructured, raw data points such as the artist’s Twitter handle and Facebook username; album release dates or concert dates; Metadata that defines the artist’s specifications: Male/Female; Genre; Label; etc.

Level 2: Information
This is where we start structuring our data, perhaps visualizing it graphically to find clues about what questions it may answer.

Level 3: Knowledge
The industry-specific context will help us convert our information into knowledge, for example, setting benchmarks for genre consumption or marking a level of an artist as Upcoming/Mainstream.

Level 4: Intelligence
And finally, here, we predict outcomes and advise actions with a high level of confidence, such as when to release music for maximum impact or plan a tour in a particular market. For artists, the intelligence level also gives insight into who your fans are, where they live, and what content they consume.

Data or metrics applicable to the music industry include music streams, digital album sales, music video streams from YouTube, concert sales, etc., which can all be gathered online. Let us now examine how it is utilized within the industry:

I. Streaming Services: Spotify, Apple Music and other such services use data for music recommendation and a personalized user experience.

Spotify uses the following machine-learning models for their unique and popular offerings like Discover Weekly, Spotify Wrapped, etc.:

  1. Collaborative Filtering: This model analyzes and compares user profiles’ listening patterns and identifies similarities to make recommendations for similar songs.   
  2. Natural Language Processing (N.L.P.): Spotify A.I. scans text and speech on the web around the music to make a song profile and tag it as per the identified theme, language or any other keyword.
  3. Convolutional Neural Networks (C.N.N.): This model ensures that the less popular songs are included in the mix. It analyzes raw audio files based on parameters such as BPM, Loudness, Time Signature and Key and matches them with songs of similar patterns.

Image Credit: Giuliano Giacaglia (Source – Medium)

Apple Music’s “For You” personalizes the experience by recommending songs based on how the user likes/saves their music. It also tracks performances of songs and artists within the app, iTunes Store and elsewhere on the web to produce their Charts tab.    

II. Music business establishments like labels and artist agencies: It goes without saying that creating music is the primary focus; however, there is no point in creating music that will not find an audience. Hence music business establishments such as labels and agencies pay heed to data science to understand and keep up with the listener’s changing tastes; they can analyze the prevailing trends to predict what the listeners are looking for.

With the help of analytical tools like Chartmetric and Soundcharts, they can collect and analyze data from streaming platforms, social media, and airplay to gain insight into artists and their music, audience listening patterns, and platform behavior. This information helps artist managers understand where their music is succeeding and where it’s lacking, so they can make improvements and create strategies that lead to greater success. Big Data has even made it possible to predict future trends in the music industry and identify the “next big hit.

Image: Chartmetric

Big Data assists marketing and promotion teams in ensuring that the music reaches the right audience and creates the required buzz. It predicts ticket sales, offers creative ways of providing musical experiences to listeners, develops fan-interactive tactics for higher engagement, and much more.

III. Artists: For artists, data can be a powerful tool to create efficient release and marketing strategies, understand the consumption patterns of their fans, tap into trending opportunities to grow their fanbase, and stay up to date with the current musical preferences to experiment with their music.

Data can also help artists review their popularity per location, market, and demographics. This information can be used to plan a tour and set concert dates, as well as gauge the profitability of the tour based on the fanbase and ticket sales. Presenting promoters and agents with localized data acts as proof and eases negotiation.

Data can also be used to conduct psychographic analysis and collaborate with brands and influencers for marketing purposes. This offers artists the opportunity to reach a newer fanbase, as well as generate revenue through these partnerships. Companies like SoundOut and Hoopr actively facilitate this collaboration to bridge the gap between content and music creators.

Platform-owned analytical tools such as Spotify For Artists and Instagram Analytics furnish details on audience engagement and consumption behaviors which can let the artist plan which songs to promote and where, the optimal release time for a song, etc. (P.S. Refer to my Chartmetric article wherein I have explained how I use music data analytics for my works with artists: “Why Music Data Analytics Tools Are an Artist’s Best Friend”)

Data collection and analysis can help artists and labels identify when and where their music is being used, enabling them to receive the royalties they are due. Advanced technology based on web scanning and data processing can also reduce unauthorized usage of music and copyright infringements. This helps ensure artists are fairly compensated for their work.

Conclusion:

Big Data has revolutionized the music industry, and its benefits are clear: improved marketing, better audience engagement, and increased revenue. With the right tools, anyone can use data to their advantage, regardless of their expertise. Platform-owned and cross-platform tools provide access to valuable data, allowing artists and establishments to make smart decisions and maximize their profits. Going forward, Big Data will continue to shape the music industry and provide more opportunities.

If you are an artist or a music business professional interested in the topic or would like a detailed analysis based on your music/artists, please write to me at rufy.ghazi@gmail.com / hello@rufyghazi.com and visit my website to find out more about what I do. Good luck!

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Rufy Anam Ghazi is an experienced music business professional specializing in music data, strategy, and curation. She is also a DJ, performing under the moniker Lady Ruffelin.

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