The music industry’s relationship with Big Data is rather mutually exclusive. With the introduction of the internet came the rise of iPods, smartphones, streaming services, online fan clubs, subreddits, and all that galore; the direction of digital marketing for the music industry has relied heavily on data efforts to equate to possible record/merchandise/ticket sales.
With CD sales plummeting in the late-2000s and music piracy at an all time high, the creation of streaming services provided a steady revenue base without piracy interference. As users on Spotify listen to an album, multiple recommendations are created in the algorithm and inserted into personalized user play listing and radio. Thus, it became a game for digital marketers: how can we boost the algorithm?
As an intern at Atlantic Records I had my first glimpse of how far Big Data has taken ahold of the industry. I had found my best friend from high school on an excel spreadsheet for Dear Evan Hansen fans – with her name, birthday, address, email, social media profiles, etc. and the connections between her and other fans. We had information on who has liked, shared, tweeted the most for our campaigns. Who engaged on our special landing page and who are the top Kelly Clarkson fans on Songkick.
The marketing opportunities with the data we collected allowed us to fuel and leverage our campaigns to the RIGHT people at the RIGHT time in the RIGHT moment. It was scary to wrap my head around the thought that as an intern, I was researching and adding to those excel sheets to gather as much data the digital marketers needed to ensure what the music industry relied to most: ROI.
Besides data mining being of uber importance to digital marketers, it also replaces traditional A&R altogether. Labels are becoming less willing to take chances on artists whose music isn’t engineered to maximize profit.
To think that based on consumer data – the likes, the needs and wants: labels are able to create the next pop star to fulfill a consumer demand.
Chartmetric says it can filter which of the 1.7 million artists it tracks will have a big career break within the next week.  And for analytics platform Next Big Sound, it reports its algorithm can predict which of the nearly 1 million artists it tracks are most likely to hit the Billboard 200 chart for the first time within the next year. 
Even so – through consumer analytics on Spotify the music industry is able to gain insight on where to invest marketing dollars. For example, based on UMG’s realization that a much larger than normal percentage of rapper Logic’s “1-800-273-8255” listeners were adding the song to personal playlists – more marketing dollars were invested into the track and eventually the song landed at No. 3 on Billboard’s Hot 100 and went quintuple platinum. 
The age of streaming services has only fueled a fire – it has saved plenty of labels from the backfire of Napster, and allowed the major players such as Warner, Universal and Sony to make informed investment decisions to continue funding the (now) 11.9 billion dollar industry. 
Big Data and music is more than just a friendship. It’s a dependence.
Hayes, Josh. “How Chartmetric Predictive A&R Finds Artists With Machine Learning.” How Music Charts, How Music Charts, 22 June 2020, blog.chartmetric.com/the-next-era-of-a-r-tools/. 
Hajari, Adam. “Predicting Artist Success at Next Big Sound.” Medium, Next Big Sound, 20 Apr. 2018, blog.nextbigsound.com/predicting-artist-success-at-next-big-sound-797cc5f09185?gi=b2c59ddb8b01. 
Setaro, Shawn. “How Data Is Making Hits and Changing the Music Industry.” Complex, Complex, 30 Jan. 2020, www.complex.com/music/2019/09/data-changing-music-industry.