Everyone’s talking about super fans, super listeners, and fandoms. This is how you identify artists that have them.
As streaming platforms grow super users, these are the strategies you can use to identify artists with super fans, super listeners, and fandoms.
The second stage of streaming penetration means making super users.
The first stage of streaming penetration is breadth, i.e., volume and scale. The tech culture-fueled mandate for any streaming platform (DSP) is to subscribe as many people as possible and worry about profits later. This stage of penetration is largely characterized by user passivity (e.g., playlists and algorithmic programming), because DSPs’ priority is to keep users just busy enough to stay subscribed. The second stage of streaming penetration is depth. As investors press for profit, churn increases, market share decreases, and/or markets approach saturation, the priority shifts to profit through a diversification of features that increase engagement and on-platform time. This stage of penetration is largely characterized by user activity (e.g., social media functionality, advertising, e-commerce integrations, communities, and fandoms), because DSPs are focused on creating super users, not just subscribers.
As a result, when you’re assessing the value of an artist’s catalog to determine whether or not their copyrights will be a worthwhile investment over the next 30 years—and whether you should really be offering that 15x multiple—you don’t just want to look at the 5-15 percent annual growth rate in streams but how much depth the artist’s catalog has with fans. If an artist’s music was removed from big editorial playlists, would fan communities still be there playing their music on repeat?
I gave something akin to this analysis in 2022 when I was invited to speak on a music valuation panel at a well-known music industry conference. I’m not a high-powered attorney or executive, but I was a bit surprised when one panel participant said something to the effect of, “Anyone who knows what they’re talking about knows we’re not anywhere close to saturation.”
Saturation wasn’t really the point of my response, but being underestimated and proving those underestimations wrong is something I’ve been doing all my life; so, fast forward a year, and what is everyone in the music industry talking about? The depth of streaming penetration: super users, i.e., super fans, super listeners, and fandoms.
Consider data relationships, not datapoints, for a fair analysis.
There are plenty of data that can help you determine how significant super users are to an artist’s audience, but many are private and only accessible to the DSP, artist, or artist’s team. However, you can also use public metrics like monthly listeners, followers, and social presence as proxy variables to estimate that super usage. The important thing to remember is the relationship between these data, regardless of whether they’re public, private, or proxy. Overestimating a single—especially qualitative—factor and ignoring the way that multiple factors relate to one another can, in the case of music, inflate the value of a highly visible artist’s catalog, or, in the case of education, inflate admissions rates for students who come from wealthy backgrounds.
Standardized tests, as flawed as they may be, will give you some sense of how well a student takes a test, and GPA will give you an idea of how well a student works in a class setting. To round out the picture, it’s important to understand certain life experiences or hardships a student has had, which can speak to their ability to overcome adversity and still achieve high marks. If two students have similarly high test scores and GPA but one student managed to achieve that without access to expensive tutors and test prep, I think you’d be hard-pressed to find someone who claimed that student wasn’t deserving of admission into an elite university. Unfortunately, that’s not the way elite university admissions work, because these schools are overly reliant on a single factor: wealth.
According to a recent study by Opportunity Insights, students from top-earning households are 2x more likely to be admitted to elite universities than students from low-earning households. Middle-class students, meanwhile, are the least likely to be admitted, all other things equal. The authors of the study explain that these universities appear to take some adversity into account, which gives students from low-earning households a better chance of getting in than students from middle-earning households. However, the benefit is nothing compared to what it is for students from top-earning households.
In effect, instead of weighing the relationship between test scores, GPA, and adversity, elite universities are admitting the highest earners at the highest rate, rewarding wealth more than hard work. That’s probably why, coming from a 40-60 percentile in parent income, I didn’t feel too comfortable at an institution like Stanford, and some of the smartest, hardest working, and most inspiring people I’ve met in my life didn’t go to elite universities or even attend college at all—and a lot of those people are artists.
Applying an elite university-approach to music could get you into a position where two artists have very similar stream counts, but one artist’s catalog is valued higher because of name recognition alone. There is certainly an argument to be made for this approach, because name recognition can get an artist huge brand deals and collaborations. However, brand value doesn’t necessarily get you those super user listeners that will be all-important heading into the next decade of streaming.
Identifying artists with high fan conversion rates within their popularity cohort.
A couple of years ago, I published a study by Andrew Thompson on the Chartmetric blog that looked at the relationship between followers and monthly listeners for artists at various levels of Spotify popularity.1 The distribution of Spotify data was similar to the distribution of university applicant data according to parent income, though much more pronounced at the lower end of the distribution.
To keep things simple and fresh, I’ve bucketed a random set of artists from 10 Spotify popularity ranges and charted the median follower-to-listener ratio for each range. In theory, the higher the ratio, the more effective the artist is at converting listeners into followers, which we can think of as a proxy for long-term fans (and potentially super fans).
You can check out Andrew’s original analysis to better understand why the distribution might share some similarities with that university applicant graph, but suffice it to say, artists with a Spotify popularity greater than 60-69 trend toward 100 percent conversion while artists with a Spotify popularity less than 60-69 trend toward 0 percent conversion.2 Understanding this baseline should give you an idea of what kind of ratio to look for when evaluating an artist or their catalog.
If an artist is in the 80-89 popularity range, but their follower-to-listener ratio, i.e., fan conversion rate, is only 40 percent, then super fans and super listeners probably aren’t a big part of their audience (though you would really need to check their Spotify for Artists to confirm this). The important takeaway here is that a multidimensional analysis that considers data relationships and not just singular datapoints or inflated qualitative factors will result in the fairest representation of music valuations, A&R assessments, and student populations at elite universities.
Information is abundant, and time is not—but that doesn’t mean we should only see the trees. The proliferation of information should help us see the forest.
For those unfamiliar with the Spotify Popularity Index, it’s a proprietary system that Spotify uses to score how popular an artist, album, or track is on a scale from 0-100. No one knows exactly what goes into it, but it probably takes into account some combination of recent streaming growth, playlist adds, and maybe even save rates.
Artists in the 30-39 Spotify popularity range are the exception here, and Andrew’s piece could give you some good clues as to why.