A theory of popularity

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Out of the many deceptively simple, aesthetically comical, casually addictive games out there in the app store, Flappy Bird made it big. The question is, why did it succeed, among the many games that possess the same qualities?

This same question can be asked of the many goods in the cultural market – why did this book make the bestsellers list, and not that one? Why did Justin Bieber make it big, instead of another blonde Canadian? We are inclined to believe that there is some difference in quality – Bieber may be objectively a better singer, or subjectively more good looking than other Canadian blondes. If that were the case, we should be able to replicate the success quite easily, or at least reliably predict which book or song or game will be the next runaway hit. Instead, The Blair Witch Project wins while John Carter sinks.

Kieran Healy adds to the discussion by bringing up one of my favorite academic papers, by sociologists Duncan Watts, Matt Salganik and Peter Dodds that examines this phenomenon, titled, “Experimental study of inequality and unpredictability in an artificial cultural market“. The experiment set up was clever – A music sharing website was created in which over 10,000 participants download previously unknown songs. One group of users were presented with a list of songs without any indication on how many times the songs have been downloaded. Another group of users were presented with the same songs, but ordered by the number of previous downloads.

Results showed that just that little bit of information on the number of downloads was enough to make songs perform disproportionately. Songs that were shown to be downloaded just a little bit more became extremely popular. Even then, rerunning the same experiments again lead to a slightly different result – the most popular song still became disproportionately so, but was a different one. On the upside, however, the best songs rarely did poorly, and the worst rarely did well. But everything in between is up for grabs.

Flappy Bird had the qualities for success, but it was also the beneficiary of some rapid word-of-mouth and random luck that drove the game up the download charts. This paper helps blast aside some of our grasping for the post rationalization on why success occurs – we want to believe something has succeeded because of certain features, that the product is fundamentally better, when most of the time they can be quite irrelevant.

There are probably a range of reasons for people’s reluctance to agree that true unpredictability is a real feature of these markets. Psychologically, people are often predisposed to believe in some version of a Just World Hypothesis where people fundamentally get what they deserve. A little more sociologically—as noted by Salganik et al—the sheer fact of a winner’s huge success focuses attention on the particular features of the good, and encourages people to tell a story about why those features drove it to success. This story is of course very plausible ex post precisely because the good succeeded so well. The commentariat in the business press, and sadly a great deal of research on management and leadership in business, is built on some version of this error. Seeking an explanation for success, you look only at the successful cases and thus ignore the possibility that the predictors of success you discover are also present in many of the failed cases. In research design this mistake is called sampling on the dependent variable. As John said repeatedly during the show, this will lead you to confuse necessary and sufficient causes.

What does this mean for brands? It does not mean that quality is not relevant. It is, and it’ll be nice if quality is enough to win, and that was the case when there weren’t that many companies around. A unique value proposition is great, but difficult to achieve. This leaves many companies trying to win what is really a game of chance (even if it’s one you can approximate), to be the Flappy Bird that keeps on flapping. When we say we don’t know which half of the money spent on advertising/marketing is wasted – all the data in the world still cannot give you an ex ante explanation.

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