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The Algorithm behind the Beat


By Melissa Goertzen, Staff Writer

 

Move over, American Idol. The next big success story in the music industry won’t be discovered in high profile talent competitions. Instead, it will be identified in data sets by complex algorithms designed to uncover usage and business trends. On the surface, this method sounds dry and more devoid of emotion than Simon Cowell’s critiques, but it’s actually the ultimate way the public selects “the next big thing.”

Every time the public clicks on YouTube links, posts concert photos on Twitter, or chats about bands on Facebook, they contribute to a body of information called big data. The term refers to a collection of data sets that are large and contain complex interrelationships. Think about the structure of social media networks. They contain millions of individual user profiles that are linked together by friendships, ‘likes’, group memberships, and so on. Essentially, big data mirrors the structure of these platforms.

In the music industry, big data is generated by activities like online sales, downloads, and communication conducted through apps or social media environments. Metrics measured include “the amount of times songs are played or skipped, as well as the level of traction they receive on social media based on actions such as Facebook likes and tweets.” Analytic tools determine the overall popularity of fan pages and register positive or negative comments about artists. Together, this information identifies current trends, assesses the digital pulse of artists, and leads to sales through singles, merchandise, concert tickets, and even subscriptions to music streaming services.

In terms of discovering new talent, big data plays an important role in generating interest at major record labels. In many cases, companies tally an artist’s page views, ‘likes’, and followers. Then, numbers can easily be compared against other artists in the same genre. Once an act has generated a hundred thousand plus Facebook or Twitter followers, talent managers take notice and start drumming up interest within the music industry itself.

The ability to identify current trends and predict the next megastar comes with large financial rewards for everyone involved. For instance, data scientists studied the impact of social media on iTunes album and track sales by comparing one’s metrics with the other’s revenue. They concluded that social media activity correlates to an increase in album and track sales. More specifically, YouTube views have the largest impact on sales; a finding that prompted many record labels to upload large budget music videos onto the platform to promote singles.

Before spending millions on video production, analysis is used to identify which songs are likely to become hits based on the online activities of targeted audiences. The accuracy of these predictions is correlated to the quality of big data analysis. Entrepreneurs within the music industry are now experimenting with new methods to develop algorithms that harvest information with greater efficiency and accuracy.

One of the most notable examples is a joint venture between EMI Music and Data Science London called The EMI Million Interview Dataset. It is described as “one of the richest and largest music appreciation datasets ever made available – a massive, unique, rich, high-quality dataset compiled from global research that contains interests, attitudes, behaviours, familiarity, and appreciation of music as expressed by music fans.”

David Boyle, Senior Vice President for Insight at EMI Music, explains that it’s “comprised of a million interviews broaching topics like level of passion for a particular music genre and sub-genre, preferred methods for music discovery, favorite music artists, thoughts on music piracy, music streaming, music formats, and fan demographics.” The goal of the project is to release this collection of information to the public and improve the quality of business within the music industry.

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