Cultivating Wild Data: How Entertainment Intelligence Is Bringing Meaningful Analytics to the Streaming-Age Music Business
It’s fun to talk about data, its ubiquity and importance. It’s not so fun to make sense of a billion lines of data from multiple service providers for an entire label roster.
That is unless you’re Greg Delaney, the founder and driving force behind data analytics platform Entertainment Intelligence (Ei). The service has been quietly helping independent labels and distributors work with data like the majors do, analyzing playlist performance, setting meaningful benchmarks, and tracking spikes that pop out of deep catalog. Clients include Domino, Secretly Distribution, Sub Pop, Epitaph, Naxos, Concord Music Group and Zebralution, among others.
“If the indies all get together, you can have the tools that rival, if not surpass, what the majors can do,” Delaney states. “It’s hard for a big company to pivot quickly, but smaller labels can, with our support”
After founding Crowdsurge, a global fan-club ticketing company that worked directly with artists like Paul McCartney and Foo Fighters, Delaney wanted to address a different “data mess” he saw in the music industry. He teamed up with label and publishing veterans to home in on the metrics that matter, broadening data’s role from short-term blip-like guide in promo campaigns or blunt A&R instrument, to a finely honed strategic tool in the long game of artists career development.
“There are many DSP dashboards out there,” explains Delaney, “and just as many services offering to unite them into a dashboard to rule them all. Most are scraping limited public data and then offering vanity metrics that aren’t very helpful at guiding business decisions. We discovered labels need nuance and actionable benchmarks that only listener-level streaming data can provide.”
Ei gets permission from clients to directly access their data from digital service providers (DSPs like Spotify, Apple, Pandora, and Deezer). It then anonymizes and aggregates this across clients to generate high-quality insights about playlist performance, source of streams, and listening behavior. It pings catalog owners when it detects a “heartbeat,” such as an older track picking up steam. It allows for music professionals, managers, and artists to do something retailers have done for years but the music industry hasn’t: detailed cohort analysis. Ei also gives important context to data and reports, giving a per-capita option for territory statistics, for example, revealing when a market is punching above its weight.
Together, this renders a richer portrait of just how listeners around the world are interacting with music, a view that goes way beyond raw follower counts or skip rates. For example, if a label sees an uptick in streams around the announcement of a GRAMMY nomination, they can determine how many are new listeners, how these listeners interacted with tracks (Did they move on quickly? Did they add it to a personal playlist?), and where they came from. If a touring band needs to know which mid-sized city to pick in a region (Buffalo or Rochester?), Ei can help figure out where a more likely audience lives, right down to the neighborhood. If a label is considering which playlist to pitch a new single to, Ei can give insights into how many total streams are likely to result from a placement, based on the average track streams per day multiplied by the average “lifetime” of other tracks on that playlist.
“Most people don’t unfollow a playlist, which means followers are not a useful metric,” Delaney notes. “We wanted to find something useful, so we took data from a wide range of genres and around 350,000 artists and four million tracks with worldwide audiences. We used it to create our own benchmarks, something only really big volume folks like the majors can usually do.”
This data analysis yields fascinating and actionable results. A large indie label noticed something strange happening in several East Asian markets with a particular song. Listeners in Japan and Korea all seemed to be skipping at the exact same spot. Meanwhile in Central Europe, listeners kept streaming away. The label found that right at the skip spot, a harsher guitar part broke into what was an otherwise chill song. This observation inspired a remix that changed that transition, tailored to those markets.
This kind of data-driven approach is particularly suited for the way indie labels work, to the longer-term investment in artists and repertoire they make. To really see what works, they need to cultivate the wild data pouring out of DSPs.