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UK-Based Company’s New Approach to Music Data Analytics Goes Beyond Charts

Uncharted Territory: Entertainment Intelligence’s New Indie Benchmarks Create Guideposts in a Data Wilderness

Charts are nice if you’re part of music’s top 10%, but what about the other 90%?

Public data analytics dashboards look cool, but what do they really tell us?

Music experts Entertainment Intelligence (Ei) have established a whole new approach to help artists and labels see where they stand with fans–and to guide them to better marketing and creative decisions in a global music market. Using a large body of granular data, it has generated a powerful set of Indie Benchmarks (iB) that offer actionable insights to independent labels, artists, agents and marketeers on par with the internal tools used by the majors.

“Charts are an artefact of the physical retail era, when we only really cared about how many units were moving out of the store,” notes Ei founder Greg Delaney. “Data like total overall sales or follower counts are of extremely limited use; how many people go and unfollow an artist or playlist for instance?”

Ei uses listener-level data direct from DSPs like Apple and Spotify, anonymizes them across its client base which includes labels like Concord, Domino, and the Secretly Group. They then apply machine learning to uncover relevant audience patterns and peer artists, creating truly useful benchmarks for independent music professionals.

As in all data analytics, these benchmarks are built on a clear understanding of what billions of lines of data say. These benchmarks answer questions like how many engaged fans does an artist need to meet a release goal or to catch the ear of a key curator; how is marketing spend actually impacting listening and engagement; and what playlists will help an act get where it needs to go. Benchmarks identify similar artists to compare performance with. They add nuance and real insights to basic numbers like followers, streams, and skips.

Skip rates may seem straightforward, for example, but the real question is who is skipping and when. Ei can tell you. “Because we store every single stream, including the listeners’ identifier and their streaming history, we can tell how many times fans have heard a track already and if they have saved it,” Delaney says. “So the next time they skip that same track, we can mark it as neutral, which is very different from a first-time listener skipping, especially the sub-30 seconds skips, which we also track.”

Ei already generated an average track streams per day (ATSPD) benchmark for all playlists, but it can now report the expected lifespan of a track. With this, users can estimate the true value of a particular playlist, versus others that have a large number of followers but lower listenership, or high volumes but an even higher churn rate, the speed at which tracks are moved off a playlist.

The world suggested by benchmarks, beyond the blunt instruments of charts, social listening, and basic counts, is full of potential. “There is a massive audience out there for the music artists have put their heart and soul into creating,”

“If you’re a pop diva, stadium rocker, or DJ extraordinaire, you may well appear on top international charts, but why would those have any relevance to a psychedelic goth rock ensemble with a small but fiercely loyal fan base in Chile? Using benchmarked data, an artist could know if they are position 901 and what of relevance is happening 10 places above and below them,” Delaney explains. “A smart system would then provide tips and feedback on how to improve their position, find a larger audience, book better tours, and so on, based on what worked for others in their position. We need to work together, pooling data and experience to create something worthy of the artists and fans we serve.”

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