New tools may soon further diminish the importance of actually hearing artists perform. Next Big Sound, a five-year-old music-analytics company based in New York, scours the Web for Spotify listens, Instagram mentions, and other traces of digital fandom to forecast breakouts. It funnels half a million new acts through an algorithm to create a list of 100 stars likely to break out within the next year. “If you signed our top 100 artists, 20 of them would make the Billboard 200,” Victor Hu, a data scientist with Next Big Sound, told me. A 20 percent success rate might sound low, until you gaze out at the vast universe of new music and try to pick the next Beyoncé.

Last year, the company unveiled a customizable search tool called Find, which, for a six-figure annual subscription, helps scouts mine social media to spot artists who show signs of nascent stardom. If, for example, you wanted to search for obscure bands with the fastest-growing followings on Twitter, Find could produce a list within seconds.

The company has discovered that some metrics, such as Facebook likes, are unreliable indicators of a band’s trajectory, while others have uncanny forecasting power. “Radio exposure, unsurprisingly, is the most important thing,” Hu says. It remains the best way to introduce listeners to a new song; once they’ve heard it a few times on the radio, they tend to like it more. “But we discovered that hits to a band’s Wikipedia page are the second-best predictor.” Wikipedia searches are revealing for the same reason Shazam searches are. While getting a song on the radio ensures that people have heard it, Culbertson says, “Shazam tells you that people wanted to know more.”

To get a song on the radio in the first place, music labels confront a paradox: How do you prove that it will be a hit before anyone has heard it? DJs consider unfamiliar songs “tune-outs,” because audiences tend to spurn new music. In the past, labels sometimes pressured or outright bribed stations to promote their music. Songs became hits because executives decided they should be hits.

But radio, too, has come to rely more on data, and now when label executives pitch a station, they’re likely to come armed with spreadsheets. The search for evidence of a song’s potential has become exhaustive: you can’t just track radio data, or sales, or YouTube hits, or Facebook interactions, or even proprietary surveys and focus groups. To persuade a major radio station to play a new song, labels have to connect all these dots.

“The idea that DJs are just picking songs because they like them is so antiquated,” says Radha Subramanyam, the executive vice president of insights, research, and analytics at iHeartMedia (formerly Clear Channel), the nation’s largest owner of FM stations. iHeartMedia consults companies like Shazam to figure out which songs are going viral. Nielsen Audio, another data firm that has partnered with the company, offers thousands of listeners cash or gift cards to wear devices called Portable People Meters that track which radio stations people are tuning in to. To know when listeners are growing tired of a song, iHeartMedia conducts weekly surveys using a database of 1.5 million people.

Perhaps iHeartMedia’s most interesting partner in the search for pop music’s next big thing is a 12-year-old subsidiary called HitPredictor, which, true to its name, predicted 48 of the top 50 radio hits last year. Before a song debuts on a major chart—Top 40, urban, country, or alternative—HitPredictor plays key sections for its online database of listeners and rates their responses. Any song that scores above a 65 is considered a possible breakout, though above that threshold, the highest-scoring songs don’t always do best. (Meghan Trainor’s debut single, “All About That Bass,” scraped by with a 68.97 rating but went on to become the top song in the country this fall.)