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it can allocate its budget for sponsored tweets. Actually running such an experiment is still extremely difficult in practice, so we instead did our best to approximate it using the data we had already collected. Specifically, we divided our data in two, artificially setting the first month of our time period as our “history” and the second half as the “future.” We then fed all our “historical” data into a statistical model, including how many followers each user had, how many others they were following, how frequently they tweeted, when they had joined, and how successful they had been at triggering cascades during this period. Finally, we used the model to “predict” how influential each user would be in our “future” data and checked the model’s performance against what actually transpired.

In a nutshell, what we found was that individual-level predictions are extremely noisy. Even though it was the case that on average, individuals with many followers who had been successful at triggering cascades of retweets in the past were more likely to be successful in the future, individual cases fluctuated wildly at random. Just as with the Mona Lisa, for every individual who exhibited the attributes of a successful influencer, there were many other users with indistinguishable attributes who were not successful. Nor did this uncertainty arise simply because we weren’t able to measure the right attributes—in reality we had more data than any marketer would normally have—or to measure them accurately. Rather, the problem was that, like the simulations above, much of what drives successful diffusion depends on factors outside the control of the individual seeds. What this result suggests, in other words, is that marketing strategies that focus on targeting a few “special” individuals are bound to be unreliable. Like responsible financial managers, therefore, marketers should adopt a “portfolio” approach, targeting a large number of potential influencers and harnessing their average effect, thereby effectively reducing the individual-level randomness.24

Although promising in theory, a portfolio approach also raises a new issue, of cost effectiveness. To illustrate the point, consider a recent story in the New York Times that claimed that Kim Kardashian, the reality TV actress, was getting paid $10,000 per tweet by various sponsors who wanted her to mention their products. Kardashian at the time had well over a million followers, so it seems plausible that paying someone like her would generate more attention than paying some ordinary person with only a few hundred followers. But how did they come up with that particular figure? Ordinary people, that is, might be prepared to tweet about their products for much less than $10,000. Assuming, therefore, that more visible individuals “cost” more than less visible ones, should marketers be targeting a relatively small number of more influential, more expensive, individuals or a larger number of less influential, less expensive individuals? Better yet, how should one strike the optimal balance?25

Ultimately, the answer to this question will depend on the specifics of how much different Twitter users would charge prospective marketers to sponsor their tweets—if indeed, they would agree to such an arrangement at all. Nevertheless, as a speculative exercise, we tested a range of plausible assumptions, each corresponding to a different hypothetical “influencer-based” marketing campaign, and measured their return on investment using the same statistical model as before. What we found was surprising even to us: Even though the Kim Kardashians of the world were indeed more influential than average, they were so much more expensive that they did not provide the best value for the money. Rather, it was what we called ordinary influencers, meaning individuals who exhibit average or even less-than-average influence, who often proved to be the most cost-effective means to disseminate information.

CIRCULAR REASONING AGAIN

Before you rush out to short stock in Kim Kardashian, I should emphasize that