Creating a hit is largely a matter of random chance
I’ve spent the last 10 years working as a computer programmer, but now that Darren Hoyt and I are trying to launch WP Questions I find myself reading a lot more about marketing.
I very much like this bit in Fast Company, where Duncan Watts argues that which songs emerge as hits is a largely random process:
Watts wanted to find out whether the success of a hot trend was reproducible. For example, we know that Madonna became a breakout star in 1983. But if you rewound the world back to 1982, would Madonna break out again? To find out, Watts built a world populated with real live music fans picking real music, then hit rewind, over and over again. Working with two colleagues, Watts designed an online music-downloading service. They filled it with 48 songs by new, unknown, and unsigned bands. Then they recruited roughly 14,000 people to log in. Some were asked to rank the songs based on their own personal preference, without regard to what other people thought. They were picking songs purely on each song’s merit. But the other participants were put into eight groups that had “social influence”: Each could see how other members of the group were ranking the songs.
Watts predicted that word of mouth would take over. And sure enough, that’s what happened. In the merit group, the songs were ranked mostly equitably, with a small handful of songs drifting slightly lower or higher in popularity. But in the social worlds, as participants reacted to one another’s opinions, huge waves took shape. A small, elite bunch of songs became enormously popular, rising above the pack, while another cluster fell into relative obscurity.
But here’s the thing: In each of the eight social worlds, the top songs–and the bottom ones–were completely different. For example, the song “Lockdown,” by 52metro, was the No. 1 song in one world, yet finished 40 out of 48 in another. Nor did there seem to be any compelling correlation between merit and success. In fact, Watts explains, only about half of a song’s success seemed to be due to merit. “In general, the ‘best’ songs never do very badly, and the ‘worst’ songs never do extremely well, but almost any other result is possible,” he says. Why? Because the first band to snag a few thumbs-ups in the social world tended overwhelmingly to get many more. Yet who received those crucial first votes seemed to be mostly a matter of luck.
Word of mouth and social contagion made big hits bigger. But they also made success more unpredictable. (And it’s worth noting, no one in the social worlds had any more influence than anyone else.) So yes, Watts figures, if you rewound the world to 1982, Madonna would likely remain a total unknown–and someone else would have slipped into her steel-tipped corset. “You cannot predict in advance whether a band gets this huge cascade of popularity, because the social network is liable to throw up almost any result,” he marvels.
Predictably, the music industry received the analysis–”Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market,” published in Science in 2006–with a cocked eyebrow. When Watts presented his findings to executives at a major record label last spring, the younger among them were reasonably receptive. They’re accustomed to the unpredictability of hit-making online, so they can grasp the terrifying randomness of success.
But the older execs?
Watts laughs. “They were all like, ‘I think it’s bullshit. I’m still going to go with my gut,’” he recalls. “And I’m like, Okay, good luck to you. You’re going to need it.”
He is going over ground that Clay Shirky examined in 2003, in such essays as “The FCC, Weblogs, and Inequality“:
Yesterday, the FCC adjusted the restrictions on media ownership, allowing newspapers to own TV stations, and raising the ownership limitations on broadcast TV networks by 10%, to 45% from 35%. It’s not clear whether the effects of the ruling will be catastrophic or relatively unimportant, and there are smart people on both sides of that question. It is also unclear what effect the internet had on the FCC’s ruling, or what role it will play now.
What is clear, however, is a lesson from the weblog world: inequality is a natural component of media. For people arguing about an ideal media landscape, the tradeoffs are clear: Diverse. Free. Equal. Pick two.
He talked about the issue at even greater length in Power Laws, Weblogs, and Inequality:
Freedom of Choice Makes Stars Inevitable #
To see how freedom of choice could create such unequal distributions, consider a hypothetical population of a thousand people, each picking their 10 favorite blogs. One way to model such a system is simply to assume that each person has an equal chance of liking each blog. This distribution would be basically flat – most blogs will have the same number of people listing it as a favorite. A few blogs will be more popular than average and a few less, of course, but that will be statistical noise. The bulk of the blogs will be of average popularity, and the highs and lows will not be too far different from this average. In this model, neither the quality of the writing nor other people’s choices have any effect; there are no shared tastes, no preferred genres, no effects from marketing or recommendations from friends.
But people’s choices do affect one another. If we assume that any blog chosen by one user is more likely, by even a fractional amount, to be chosen by another user, the system changes dramatically. Alice, the first user, chooses her blogs unaffected by anyone else, but Bob has a slightly higher chance of liking Alice’s blogs than the others. When Bob is done, any blog that both he and Alice like has a higher chance of being picked by Carmen, and so on, with a small number of blogs becoming increasingly likely to be chosen in the future because they were chosen in the past.
Think of this positive feedback as a preference premium. The system assumes that later users come into an environment shaped by earlier users; the thousand-and-first user will not be selecting blogs at random, but will rather be affected, even if unconsciously, by the preference premiums built up in the system previously.
Note that this model is absolutely mute as to why one blog might be preferred over another. Perhaps some writing is simply better than average (a preference for quality), perhaps people want the recommendations of others (a preference for marketing), perhaps there is value in reading the same blogs as your friends (a preference for “solidarity goods”, things best enjoyed by a group). It could be all three, or some other effect entirely, and it could be different for different readers and different writers. What matters is that any tendency towards agreement in diverse and free systems, however small and for whatever reason, can create power law distributions.
Because it arises naturally, changing this distribution would mean forcing hundreds of thousands of bloggers to link to certain blogs and to de-link others, which would require both global oversight and the application of force. Reversing the star system would mean destroying the village in order to save it.