How “The Shazam Effect” changed music- and could change book publishing

There was an interesting article in The Atlantic earlier this week called “The Shazam Effect.” For those of you unfamiliar with the term, Shazam is a tech start up founded in 2000 by a Standford Ph.D. named Avery Wang who wanted to develop a service which could use a cellphone to identify any song within the phone’s range using an algorithm which created a unique acoustic fingerprint for each track, turning each song into a piece of data which could be read by the Shazam program. 500 million downloads later the program is used by music industry executives to determine not merely what songs are popular, but which songs will be hits with the right marketing effort in the future based on early-detection. Read the following (edited for length) and for those of who who like reading substitute “music” for “books”, “songs” for “self-published novelist” “artists” with “authors”, “hear” and “listen(er)” with “read(er)”, and “labels” or “music executives ” with “traditional publishers”:

“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.  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.

Shazam searches are just one of several new types of data guiding the pop-music business. Concert promoters study Spotify listens to route tours through towns with the most fans, and some artists look for patterns in Pandora streaming to figure out which songs to play at each stop on a tour. In fact, all of our searching, streaming, downloading, and sharing is being used to answer the question the music industry has been asking for a century: 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.

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.

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.

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 Hot 100 matters because it doesn’t just reflect listener preferences, it also shapes them. In a groundbreaking 2006 study on the influence of song rankings, three researchers at Columbia University showed that popularity can be a self-fulfilling prophecy. The researchers sent participants to different music Web sites where they could listen to dozens of tracks and download their favorites. Some sites displayed a ranking of the most-downloaded songs; others did not. Participants who saw rankings were more likely to listen to the most-popular tracks.

The researchers then wondered what would happen if they manipulated the rankings. In a follow-up experiment, some sites displayed the true download counts and others showed inverted rankings, where the least-popular song was listed in the No. 1 spot. The inverted rankings changed everything: previously ignored songs soared in popularity, and previously popular songs were ignored. Simply believing, even wrongly, that a song was popular made participants more likely to download it.

Everyone I spoke with about the Hot 100—label and radio executives, industry analysts, and other journalists—agreed with Jay Frank’s assessment that consumers have more say than they did decades ago, when their tastes were shaped by the hit makers at labels. But here’s the catch: if you give people too much say, they will ask for the same familiar sounds on an endless loop, entrenching music that is repetitive, derivative, and relentlessly played out.

Because the most-popular songs now stay on the charts for months, the relative value of a hit has exploded. The top 1 percent of bands and solo artists now earn 77 percent of all revenue from recorded music, media researchers report. And even though the amount of digital music sold has surged, the 10 best-selling tracks command 82 percent more of the market than they did a decade ago. 

And not only are we hearing the same hits with greater frequency, but the hits themselves sound increasingly alike. As labels have gotten more adept at recognizing what’s selling, they’ve been quicker than ever to invest in copycats. People I spoke with in the music industry told me they worried that the reliance on data was leading to a “clustering” of styles and genres, promoting a dispiriting sameness in pop music.

In 2012, the Spanish National Research Council released a report that delighted music cranks around the world. Pop, it seemed, was growing increasingly bland, loud, and predictable, recycling the same few chord progressions over and over. The study, which looked at 464,411 popular recordings around the world between 1955 and 2010, found that the most-played music of the new millennium demonstrates “less variety in pitch transitions” than that of any preceding decade.

The problem is not our pop stars. Our brains are wired to prefer melodies we already know. (David Huron, a musicologist at Ohio State University, estimates that at least 90 percent of the time we spend listening to music, we seek out songs we’ve heard before.) That’s because familiar songs are easier to process, and the less effort needed to think through something—whether a song, a painting, or an idea—the more we tend to like it. In psychology, this idea is known as fluency: when a piece of information is consumed fluently, it neatly slides into our patterns of expectation, filling us with satisfaction and confidence.”

You can see what data analytics can do for music, you can imagine what they can do for books.

Imagine major publishing companies using data algorithms to predict what self-published author or book might be the next big hit. Rather than let the market decide, they take someone with potential and make sure he/she is shot up to the top based on data and the assumption people want more of the same. Since most people prefer things they already know, they will support whatever is considered “popular”. So if the major publishers decided a particular book should be popular, they can simply bump it to the top, knowing the book-buying public will buy a print or e-book copy because they think everyone else is. The power of peer pressure, combined with people’s comfort in seeking out things we are familiar with and enjoy, could continue moving the literacy world in the same direction as the music industry: Authors will be chosen based on potential popularity and fitting their books into a formula for what people want, which means make sure your books look like everyone else’s with only minor differences. Those who are “chosen” will earn even more of the take on book revenue because they will perpetually be near the top. Only now people will be chosen by data analytics rather than someone reading the slush pile.

This could be a boon to self-publishers, who with a little marketing, social media presence, and luck, could be plucked from relative obscurity and made into the next big thing. Agents will have an important, but diminished, role in finding new talent because the publishing companies will just pay a tech company for this service. In this system agents would focus more on the contract and business side and less on presenting an author to the editors and publishers.

However, this system would further increase the disparity between the top and the bottom, as anyone showing even a modicum of talent will be whisked to the top just as the music industry has been successful at doing. And we all know there is a reason authors on a major bestsellers list stick that achievement on their books.

What do you think about this article? Could reading become like listening? Books treated like the music industry treats authors? the gap between the wealthy few mega best-sellers and everyone else continue to grow? Or are reading and listening too separate for this ever to happen?

The image is owned and copyrighted by The Atlantic.

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