The most revealing quote in Sunday’s New York Times came in this Arts & Leisure profile of the young singer Daya.
She’s talking about the decidedly old school decision to launch her campaign by going to radio first. Naturally the program directors took to the Internet to do their diligence and find out whether this Daya was a going concern. “I had maybe 200 followers when I started,” she told Jon Caramanica. “A bunch of radio stations were like, ‘Uhhhhhh, my daughter has more followers than her.’”
The heartwarming part of the story is that Daya got herself heard, evidently built her “likes” to respectable range, and enchanted enough tastemakers to gain a foothold.
The troubling aspect: That the gatekeepers were ready to deny entrance based on the artist’s social media profile, not their own taste or sense of the sea-worthiness of the work.
Maybe the numbers don’t lie, but they sure don’t tell us much. In every field of endeavor touched by the Internet, the primary measurements concern page views and time spent, people who’ve said they “liked” something, who have signed up to be “followers,” who in some way have made an effort — guiding the mouse from one side of the screen to the other — in order to pledge some sort of allegiance. And we accept that. We don’t question it — hey, it’s as close to hard sales figures as we get right now. If a product or podcast or whatever happens to be “rising” on an iTunes chart, that’s somehow a useful indicator — a statistic that can be brandished by marketers as evidence of support or a groundswell or an undeniable new trend.
Those types of indicators aren’t meaningless — they can offer insight into the still-mysterious (and metrics-resistant) process by which unknowns become better-knowns, then household names. But too often, this measurable evidence of “activity” around an artist is conflated with merit. Not everyone can be a great musician, but just about anyone can generate enough “likes” to appear to be great on the Internet.
What if the sum total of all the little maneuvers designed to show “audience” and “reach” and “influence” really amount to a little wizard behind the curtain pulling levers? What if this craven eyeball-grabbing game has nothing to do with art at all, and just involves hiring robots (or SEO firms) to “like” stuff?
Below the superstar level, anyway, that’s about the size of it. In most stories about creative people launching new work, you can find some metric proffered as evidence of “hotness.” Page views. Streaming numbers. Retweets. Each might tell us a little something about the cleverness of the campaign — and very little about the work in question. At times it seems like the creativity doesn’t matter as long as somebody can prove that there’s a digital crowd gathered around it.
We’re reaching a point where everyone’s allotted fame nanosecond depends on the manipulation of some tracking data. The herd looks in one direction and gazes at a rising star — bingo! we have a winner! The press agents step in with breathless copy about this “undeniable” new sensation who has amassed this “grass roots” support, and this kicks the marketing effort into a new gear. Then, a short time later, the herd looks in the other direction and finds another, equally adorable rising phenom, and the game starts anew.
Surely this is part of what’s broken about the music business. Not just the demand for metrics, but the use of said metrics in the launching and selling of new work. And the way those metrics are twisted to advance an artist’s cause, as proof of an artist’s aesthetic “aptness” in this particular moment. The beancounting types, hardwired to make decisions based on data, will evidently take whatever data they can get. If lots of people clicked, followed or liked, well, gee, that’s almost like hard sales… it must be good.
Just like those wary radio programmers considering Daya, we can’t and increasingly won’t pay attention unless the artists have cleared certain thresholds — even if we know the data has been ginned up by some marketers in the back room. Even if the metrics are squishy. Or meaningless.
What does that say about us? Do we need whole new measurements, or new ways to “read” the data? Or both?
