Not directly related to books, but an interesting discoverability story.
In 2000, a Stanford Ph.D. named Avery Wang co-founded, with a couple of business-school graduates, a tech start-up called Shazam. Their idea was to develop a service that could identify any song within a few seconds, using only a cellphone, even in a crowded bar or coffee shop.
At first, Wang, who had studied audio analysis and was responsible for building the software, feared it might be an impossible task. No technology existed that could distinguish music from background noise, and cataloging songs note for note would require authorization from the labels. But then he made a breakthrough: rather than trying to capture whole songs, he built an algorithm that would create a unique acoustic fingerprint for each track. The trick, he discovered, was to turn a song into a piece of data.
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While most users think of Shazam as a handy tool for identifying unfamiliar songs, it offers music executives something far more valuable: an early-detection system for hits.
By studying 20 million searches every day, Shazam can identify which songs are catching on, and where, before just about anybody else. “Sometimes we can see when a song is going to break out months before most people have even heard of it,” Jason Titus, Shazam’s former chief technologist, told me. (Titus is now a senior director at Google.) Last year, Shazam released an interactive map overlaid with its search data, allowing users to zoom in on cities around the world and look up the most Shazam’d songs in São Paulo, Mumbai, or New York. The map amounts to a real-time seismograph of the world’s most popular new music, helping scouts discover unsigned artists just as they’re starting to set off tremors. (The company has a team of people who update its vast music library with the newest recorded music—including self-produced songs—from all over the world, and artists can submit their work to Shazam.)
“We know where a song’s popularity starts, and we can watch it spread,” Titus told me. Take, for example, Lorde, the out-of-nowhere sensation of 2013. Shazam’s engineers can rewind time to trace the international contagion of her first single, “Royals,” watching the pings of Shazam searches spread from New Zealand, her home country, to Nashville (a major music hub, even for noncountry songs), to the American coasts, pinpointing the exact day it peaked in each of nearly 3,000 U.S. cities.
Shazam has become a favorite app of music agents around the country, and in February, the company announced that it would get into the music-making business itself, launching a new imprint under Warner Music Group for artists discovered through the app.
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What do people want to hear next?
It’s a question that label executives once answered largely by trusting their gut. But data about our preferences have shifted the balance of power, replacing experts’ instincts with the wisdom of the crowd. As a result, labels have gotten much better at understanding what we want to listen to. This is the one silver lining the music industry has found in the digital revolution, which has steadily cut into profits. So it’s clearly good for business—but whether it’s good for music is a lot less certain.
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Pop music is a sentimental business, and predicting the next big thing has often meant being inside that crowded bar, watching a young band connect with the besotted, swaying throng. But now that new artists are more likely to make a name for themselves on Twitter than in a Nashville club, Culbertson is finding that the chair in front of his computer might be the best seat in the house.
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.”