AI is revolutionizing insights and business strategies and fine-tuning the way we work, connect, learn, and play around the world.

1. AI-driven data could
be the music
industry’s best
marketing
instrument
E N R I Q U E C A D E N A M A R I N

2. E N R I Q U E C A D E N A M A R I N . C O
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.

3. E N R I Q U E C A D E N A M A R I N . C O
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.

4. E N R I Q U E C A D E N A M A R I N . C O
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.

5. E N R I Q U E C A D E N A M A R I N . C O
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.

6. E N R I Q U E C A D E N A M A R I N . C O
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.

7. E N R I Q U E C A D E N A M A R I N . C O
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.

8. E N R I Q U E C A D E N A M A R I N . C O
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.

9. E N R I Q U E C A D E N A M A R I N . C O
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.

10. E N R I Q U E C A D E N A M A R I N . C O
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.

11. E N R I Q U E C A D E N A M A R I N . C O
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.

12. Thank You
E N R I Q U E C A D E N A M A R I N . C O