Historically, A&R has been a gut-based endeavor that’s rested on a single question: Does the artist have that ostensibly unquantifiable “it” factor? The problem today for gut-based A&R is the sheer volume of music that industry executives, aspiring scouts, and hit-making curators have to wade through to allow that experience-based gut power to function effectively.
To help reduce that volume, we followed the trajectories of a handful of artists that our Predictive A&R model first discovered in September in an effort to identify some early patterns of potential success by analyzing the relationship between Spotify Monthly Listeners (MLs), Spotify Followers, and Cross-Platform Performance (CPP).
As any good A&R team knows, scouting talent has never been about catching up; it’s always been about being there first. Today, that means identifying what early patterns in an artist’s streaming trajectory indicate the potential for sustained growth and a fruitful career, ideally at scale.
While there are a number of traditional filters to which A&R experts can, and should, still turn — from chance to word of mouth to demo submissions and sales thresholds — supplementing those filters with a data-based approach can prove essential for spotting that “it” factor early and maintaining a competitive edge in today’s algorithmic listening landscape.
One of the metrics of success that everyday listeners, music industry experts, and artists themselves gravitate toward is a consistently inflated Spotify MLs count, but by the time that number has reached an eye-grabbing amount, that artist has likely already achieved critical mass and courted viable suitors. In other words, it’s either entirely too late to make an offer, or the bargaining power of your A&R team has diminished significantly.
A Scalable Approach Made for Today’s Music Environment
While our predictions numbered in the thousands, we wanted to examine just a sample of these artists exhibiting promising streaming growth, so we initially sorted by the largest differences in Spotify MLs for each artist. We also sorted, however, by the largest differences in Spotify Followers to account for the possibilities of “one hit wonder” and/or stream farm scenarios and to determine what, if any, relationship there is between MLs, Followers, and other indicators of a variety of artist growth patterns.
In order to get a better understanding of how artists succeed (or don’t) on streaming platforms, we tracked the artists identified by our predictive A&R models over the course of weeks and months. We were particularly interested in the artists that had: