A clear and convincing take-down of the notion that (a) influencers exist, and (b) marketing at them is worth doing.

To summarise the key points:

1. Six degrees of separation does in fact exist - but it doesn’t work the way most people think it does. When we imagine these chains we assume they must work through celebrities, leaders, & other social connectors - whereas in fact messages travel horizontally, not hierarchically.

The overall message here is that real social networks are connected in more complex and more egalitarian ways than Jacobs or even Milgram imagined— a result that has now been confirmed with many experiments, empirical studies, and theoretical models. In spite of all this evidence, however, when we think about how social networks work, we continue to be drawn to the idea that certain “special people,” whether famous wives of presidents or gregarious local businessmen, are disproportionately responsible for connecting the rest of us. Evidence, in fact, seems to have very little to do with why we think this way

2. In The Tipping Point, Malcolm Gladwell talked about “social epidemics” set off by a small number of “superspreaders” - what he calls “the law of the few”. This is obviously catnip for marketers - it makes manipulating a large audience seem straightforward - and is the model behind influencer marketing. Unfortunately it’s not true.

3. Influence may be subtle - e.g. from observing behaviour rather than explicit verbal recommendations. It may also happen in ways we don’t acknowledge because they don’t fit our model of how things “should” work - e.g. employees influencing their boss.
[NB This isn’t necessarily problematic for influencer marketing: it’s likely to be fine if it “works” by people copying the influencer’s actions rather than doing what they say/blog about.]

4. There are different types of influence - friends are influential in different ways to Oprah. People may also be influential only on their very specific topics of expertise.
[NB Again not necessarily a problem for influencer marketing.]

5. Measuring influence is very difficult, so usually we’re not measuring it directly at all:

For example, to demonstrate just one incident of infl uence between two friends, Anna and Bill, you need to demonstrate that whenever Anna adopts a certain idea or product, Bill is more likely to adopt the same idea or product as well. Even keeping track of just one such relationship would not be easy. And as researchers quickly discovered, doing it for many people simultaneously is prohibitively difficult. In place of observing infl uence directly, therefore, researchers have proposed numerous proxies for infl uence, such as how many friends an individual has, or how many opinions they voice, or how expert or passionate they are about a topic, or how highly they score on some personality test— things that are easier to measure than infl uence itself. Unfortunately, while all these measures are plausible substitutes for infl uence, they all derive from assumptions about how people are infl uenced, and no one has ever tested these assumptions. In practice, therefore, nobody really knows who is an influencer and who isn’t.

6. How much more influential is an influencer than a regular person? Let’s say fully 3x more. That doesn’t get you very far in reaching millions of people.

7. Doing so requires adding a second idea from network theory, that of social contagion. The hope is that this will multiply the “law of the few” so that the influencer’s choices reach millions of people. The problem is that networks don’t work like that.

8. Duncan Watts and Peter Dodds tested this using computer simulations, they found that the initial “influencer effect” didn’t hold across the whole network. An individual with 3x influence didn’t result in three times more people overall being influenced - in fact, often their impact was negligible.

The reason is simply that when influence is spread via some contagious process, the outcome depends far more on the overall structure of the network than on the properties of the individuals who trigger it. Just as forest fires require a conspiracy of wind, temperature, low humidity, and combustible fuel to rage out of control over large tracts of land, social epidemics require just the right conditions to be satisfied by the network of influence. And as it turned out, the most important condition had nothing to do with a few highly influential individuals at all. Rather, it depended on the existence of a critical mass of easily influenced people who influence other easy- to- influence people

9. Yeah yeah, but that’s a computer simulation. So Watts,Jake Hofman, Winter Mason and Eytan Baksh tested this on Twitter data, looking at link-sharing:

The nice thing about these shortened URLs is that they effectively assign a unique code to every piece of content broadcast on Twitter. Thus when a user wishes to “retweet” something, it’s possible to see whom it came from originally, and thereby trace chains of diffusion across the follower graph.
In total, we tracked more than 74 million of these diffusion chains initiated by more than 1.6 million users, over a two- month interval in late 2009. For each event, we counted how many times the URL in question was retweeted— fi rst by the original “seed” user’s immediate followers, then by their followers, and their followers’ followers, and so on— thereby tracing out the full “cascade” of retweets triggered by each original tweet.
As the figure on page 102 shows, some of these cascades were broad and shallow, while others were narrow and deep. Others still were very large, with complex structure, starting out small and trickling along before gaining momentum somewhere else in the network. Most of all, however, we found that the vast majority of attempted cascades— roughly 98 percent of the total— didn’t actually spread at all

10. Comparing half the dataset against the other - as a proxy for a past/future predictive split - they found that individual-level predictions (as a marketer would do, in identifying influencers to target) were very 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”

Conclusion: The most effective way to influence a network is through a lot of ordinary-level people, not an elite of “special influencers”.

Marketing insight: You *might* do that with freebies and promotions, but it’s pretty expensive to give even something small to a mass group (e.g. 5% of customer base). What you actually have to do is:
(a) Not Be A Shit - because if you fall down on the hygiene factors they’re not going to recommend your brand; then
(b) Deliver gold-standard customer service. Making a problem into not-a-problem quickly and painlessly can delight people, and that’s how you create advocates.

Notes on the semantics of the internet

America’s Great I like Freedom Read My Book

Sarah Palin’s Twitter feed, Wordle-ised.

Who needs to read more than that?

(Devised & coded by Eric Stiens)