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The music industry is learning a new rhythm through the instrument of artificial intelligence. AI is revolutionizing insights and business strategies and fine-tuning the way we work, connect, learn, and play around the world. Expected to become a $70 billion market by 2020, AI is shifting traditional practices to more sustainable digital spheres.

In the music industry, emerging AI tools are helping reorchestrate the way audiences consume music content. One of the most effective marketing tools industry pros can utilize is the consumer data mined through AI’s machine learning.

In the future, AI-driven data can help the music industry fine-tune its marketing strategies, offering improved insights to maintain harmony between artists, the industry, and fans — all while maximizing profits.

AI is no stranger to the music industry. Since their apps launched, audio and music-facing tech companies like Shazam and SoundHound have utilized AI technologies that analyze a large catalog of songs using spectrograms to measure the various frequencies. But the access to AI-enabled data is starting to shift the music industry into more sophisticated arenas.

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Major recording companies like Sony Music and Universal Music Group own most of the content, along with shares of consumer platforms such as streaming services and apps. While major recording companies are granted access to consumer data, it’s the streaming services, such as Spotify and YouTube, that control how people consume music and, thus, who has access to AI-driven data.

Independent artists own a small portion of all the music content available, but they gain data from direct-to-fan platforms like Hive or Pledge Music. Yet many recording industry professionals are just learning how to access and analyze emerging data tools to help maximize their profits.

Here are four machine learning metrics that music industry professionals should use.

Audience engagement metrics

Engagement data offers insight into how audiences respond to new music genres, trends, artists, and songs. It can show the number of collections, changes in followers, and the number of plays per payer, all calibrated by the number of saves or collections that include a specific song. Professionals from across the music industry can use this actionable engagement data to attract increased visibility for their signed artists, thereby reaching more fans. Music labels can target audiences and track patterns to make improved business decisions, all while stimulating revenue.

By 2030, Goldman Sachs reports, streaming services will create $34 billion in revenue for the music business. These services will simultaneously generate a consistent and credible source of data that improves insight and outreach to various audience demographics.

Data filters

Each niche of the music industry has a specific need for data. Streaming services like Spotify use filtered data to transition non-paying listeners into paying subscribers. A major label, on the other hand, operates differently.

A label’s goal is to create filtered data that can help them market songs and turn mediocre fans into dedicated superfans. Spotify tapped into this data by creating Found Them First, a microsite that allows users to see which musicians they listened to on Spotify before they became popular. For labels, this monetizes the idea of early fandom.

Ultimately, these insights are used to motivate subscriber growth, driving fans’ desire to explore artists earlier in their careers. Industry players can use filters to better design their outreach strategies and content to drive competition. Data filtering advancements in other sectors may help the music industry advance the analysis of its own data.

YouTube and recommendation engines

Along with other entertainment media, YouTube and recommendation engines improve matches between listeners and artists. Music industry pros are already grasping the AI technology that allows YouTube and other recommendation engines to promote artists through raw engagement data from streaming platforms in the form of “rate of collections” and the “rate of replays per user,” even segmented by ZIP code.

Fueled by Google Brain’s AI division, YouTube improved its recommendation capabilities with a series of micro targets. The company created an algorithm using the number of times users spent watching videos and the number of video clicks per person. Soon, higher quality videos that correlated with long watch times appeared first in search queues. For three years, viewership grew by 50 percent on YouTube each year.

Google Brain learns by picking up on subtle patterns at accelerated rates. The technique, called unsupervised learning, allows for more detailed, significant insights into viewership. As the technology identified varying video lengths for specific platforms, it helped spur higher watch times.

In the music industry, experts can utilize this tool to target advertising lengths based on different platforms. Hundreds of micro changes allowed YouTube to increase time spent on the site by 70 percent. This deep reinforcement learning technology will likely propel the music industry forward as companies learn how to advertise and market to high-potential superfans based on the learned streaming data.

Automated marketing tools

AI algorithms can also help music industry professionals assess the competition by examining the social and streaming patterns of artists and competing labels. By targeting the regular streaming habits of listeners, experts identify superfans who are guaranteed to spend money on an artist each year.

For example, music marketers can break down the demographics, age ranges, and typical search patterns of Rihanna superfans. By targeting the typical buying power of each artist’s listening demographic, they can correlate how, when, and who to market to during ad breaks.

An example of this is the technology created by Syncspot. This company has created an automated, cross-promotional tool where two brands join forces. The music festival Coachella has already leveraged partnerships with brands to stimulate cash flow. Music industry marketers can rank each user in a point system that identifies people who are most engaged with an artist. More personalized content, like VIP concert ticket promotions, are offered to the most valuable fans.

Automated marketing proves beneficial in identifying true fans. However, marketing strategies will differ by a label’s artist or genre. Since fan activity and patterns vary by genre and engagement, music labels will have to identify how to cater to each artist’s fan base.

AI’s data-driven insights can enhance how the music industry connects with audiences. As the shift from traditional to digital continues, Al can power the production, search, delivery, and profitability in the digital music value chain.

As AI takes center stage, music industry professionals will need to evolve like tech companies to maximize profits that insiders enjoyed during music’s physical distribution era. In the near future, AI-enabled data will streamline our access to music and keep us dancing.

Enrique Cadena Marin (DJ ECM) is one of Latin America’s fastest rising EDM artists and producers.

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