The Thing About the Influencers

These are the slides and the (highly idealised, improved and much more eloquent than the real presentation) words that went with them from a lightning talk I gave at the Digital Shoreditch conference. In reality, there’s a lot more thought and attention in this article than there was in the talk. 10 minutes isn’t nearly enough time to touch on my somewhat confused thoughts.

I’ve been doing an awful lot of thinking about “influencers” recently as part of a big project. It’s been almost a decade since I last looked at the problem in any depth; back then I was fascinated by the Social Network Analysis opportunities: these days, as you’ll see, much less so.

I’m not sure that I have a lot of answers; but at least the questions I’m asking may be more interesting.

There’s a lot of interest in “influencers” these days. Search interest has trebled in volume.

And the number of articles in the marketing press to meet that search interest (here represented by the BrandRepublic archive) has also increased.

I think it’s worthwhile pointing out that Malcolm Gladwell (we’ll come on to talk about him a bit more) published “The Tipping Point” just around here.

What he couldn’t have predicted, I think, was the explosion in social media marketing that was to emerge a few years later. There were a few blogs around, sure; but MySpace still lay in the future when he wrote the book.

The book more or less set the scene for the rise of blogging, the democratisation of web publishing and social networking.

PR agencies were already beginning to characterise their discipline as the “business of influence”, and the first attempts were being made to industrialise “viral marketing.”

So I’d suggest that Gladwell probably had a profound influence on the way we talk and think about these things. He posited a “Law of the few”, whereby:

The success of any kind of social epidemic is heavily dependent on the involvement of people with a particular and rare set of social gifts.

This is a very attractive narrative, for all sorts of reasons.

But it’s probably wrong. Duncan Watts is a sociologist who’s been studying and publishing papers about social dynamics, advertising and trends since the late 90s. He’s also a bit of a personal nerd crush. He says,

Gladwell’s law of the few is catnip to marketers and businessmen and community organizers and just about anyone else in the business of shaping or manipulating people. And it’s easy to see why. If you can just find these special people and influence them, their connections and energy and enthusiasm and personality would be put to work for you.

Sadly, this isn’t the case: viral spread has much more to do with the composition of the network than it does with the special powers of any of the people within that network.

…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. When this critical mass existed, even an average individual was capable of triggering a large cascade — just as any spark will suffice to trigger a large forest fire when the conditions are primed for it. Conversely, when the critical mass did not exist, not even the most influential individual could trigger any more than a small cascade.

Sorry, Malcolm.

On the other hand, advertising people have never let the truth stand in the way of a good story, so let’s follow the money.

Thanks to social media platforms, celebrities are becoming channels, controlling and commercialising their audiences. They no longer have to rely on chat shows and press conferences to retail their messages.

And what used to be ‘narrowcast’ platforms (blogs/vodcasts/podcasts) have grown, consolidated, and commercialised. Some of the talent from these are becoming celebrities in their turn.

Advertisers are more likely to want “integrated programmes” that make the most of the talent they employ. That means looking at how they’ll play in social; what additional value they might bring to the table.

And this means that spends are going up. Anecdata suggests that in the US, “influencer marketing” for some clients is a budget line up to 3x media spend.

Increased spend mean that existing talent representation is taking advantage of these trends, and new kinds of talent representation are emerging.

This kind of consolidation and professionalisation makes it easier for busy advertisers to spend money.

Which in turn increases the flow of money.

The only problem is that no-one knows how the hell to value this stuff. There’s no clear, objective way to select talent. We’re all relying on personal relationships, gut feel, and our contacts at talent/artist management companies. It’s all a bit Ari Gold.

If we’re to invest in influencer marketing, we need to know who might we work with if we can’t afford Phillip Schofield. Who could help us reach Asian teens in the North? Who’s hot with Midlands Mum?

It’s pretty clear we’re only going to be valuing influencers in a couple of ways. As channel or as content.

Oprah is channel: she’s the gatekeeper to huge audiences.

George is content. He hasn’t even got a Twitter account. But he commands huge media interest: if we’ve got George on our side, we can parlay that into news coverage, social sharing, organic search traffic and other earned media. He’ll decrease our cost-per-view, and generally improve our media efficiency.

There are all sorts of data streams and cues we might use to assess audience interest in an influencer. Search is a pretty straightforward one: where are we in the influencer’s narrative cycle?

And what’s the relative search demand for each of our influencers?

And of course, what — and where — is the social interest? There are plenty of data to play with here.

So now let’s look at the channel side of the story.

It’s frequently remarked that there are things that are worth measuring and things that can be measured; but that not everything that can be measured is worth measuring and not everything that’s worth measuring can be measured.

Nowhere is this more true than when it comes to social media. We spend an awful lot of time measuring what can be measured, without ever asking whether we should be.

Twitter’s sheer volume of data, and the relative ease with which those data may be collected often mean that we don’t look any further than that. Most of the tools for identifying and evaluating “social media influence” simply use Twitter data.

I’m a big one for tradition, so I’d like to start with Twitter.

Stephen Fry is a great example. He’s pretty big. So big, in fact, that when he tweets, he can send two or three thousand clicks a second and bring websites to their knees.

Which is pretty awesome, really. If this is true, he’s a one-man DoS attack.

Can we validate these numbers?

Every so often, Mr Fry tweets a link that’s been shortened with Bitly. You may be aware that Bitly provides public stats for those links.

So, I went and collected his last few thousand tweets, found that about 10% of them contained bitly links, then counted the clicks on each of those links. This is a histogram of that data. I do enjoy histograms.

From this analysis, I can tell Stephen Fry gets a median 1.6K clicks-per-tweet.

This number is impressive, but it’s hardly the 3,000–4,000 per second that were reported.

Now, I’d like to be fair to Stephen Fry, and point out that he probably only has website owners’ words for the magnitude of the Fry Effect. I mean, how would he know how many clicks he’s sending?

The lesson here is, don’t trust other people’s data. You don’t know where they found them.

So let’s hypothesise that traffic isn’t a great proxy for influence. Surely Fry’s 9.5 million followers mean something in terms of exposure? That’s an audience that competes favourably with X Factor or Britain’s Got Talent.

Only we don’t know how many of Fry’s 9.5m followers see each tweet. It would be naive to believe that each tweet reaches all of them.

Twitter doesn’t publish reach figures (although individual accounts can see their own impression data.) How might I begin to investigate?

Fortunately, a generous friend of mine shared his Twitter stats with me. He has around 100,000 followers; but on average he nets about 5,000 impressions with each tweet.

His impressions set a top limit on his potential reach, so let’s be optimistically sloppy, and say he’s reaching an audience equivalent to about 5% of his followers.

I’m also interested in retweets-per-tweet as a metric. It strikes me that this is a good way to tell how effective a given Twitter user is at activating their audience.

We also have lots of convincing evidence that reach (or at least the impressions that we’re using as a good proxy for reach) increase with retweets.

My friend receives a median 4 retweets-per-tweet1 (or 1 retweet for every 25,000 followers.

Fry gets a massive 113 retweets per follower. But that only works out at 1 retweet for every 85,000 followers; suggesting that my friend is more than three times as good at activating his audience.

There’s a reason for this. For a long time Fry was (and may even still be) one of the “Suggestions” for new Twitter users to follow. Twitter doesn’t want new users to start with an empty timeline, so it encourages them to follow popular accounts. If you were looking for such a thing, it’s a great example of the Preferential Attachment.

Paradoxically, the “Suggested Users” decreases the overall quality of a users’ followers. New users are much more likely to churn (leave Twitter, never to return) than active users of the service. So it’s fair to say that really famous Twitter users have a certain amount of inflation in their numbers. It’s not their fault: it’s Twitter’s. So let’s look at another platform.

For all sorts of reasons, YouTube is more exciting than Twitter for advertisers. Making short videos is well understood by the business. There’s a strong paid media platform. And we’ve seen an explosion of young, talented creators who’ve established close, direct relationships with their loyal audiences.

Money is beginning to flow in: certain categories (beauty, lifestyle, food) are better represented and funded than others; but as those niches fill, other content areas are strengthening.

Oddly, there aren’t many really great data sources for YouTube analysis (please do tell me if you know of anything.) Instead, YouTubers are often ranked (and valued) according to their subscriber counts. Let’s take a look at that.

Here’s a typical YouTuber, with a typical price: £15,000 to have them create a video around our brand. They’ve got 1,200,000 subscribers, so a naive calculation puts that at £12.50 CPM (for non-advertising people, that’s “Cost per Mille”, or cost per thousand views.)

Video CPMs typically range between £8 and £25, so this feels like a bargain.

So I counted the views on every video this YouTuber had made. Each dot on this chart shows the views for one video. There are a few stand-out amazing films; but the majority are clustered near the bottom of the chart.

Another way of looking at the same data (I did tell you I like histograms). You can see there’s a pretty tight grouping between about 50,000 and 150,000 views per video. This YouTuber gets a median 111,000 views per video: or about 10% of his subscriber count.

So instead of netting out at £12.50 per video, we might actually be paying £135 for those “organic” views. That’s more than 10x the naive estimate.

Another thing: demographics aren’t public. You need to ask the YouTuber or their agent for them. Often, though, you’ll see something like this: YouTubers’ audiences can skew young and female. And maybe not in your own country.

This sort of thing isn’t restricted to YouTube: the price you pay with earned media on the web is a certain lack of control over targeting: you don’t get to say who you reach unless you invest in paid media.

This really is only a series of somewhat related thoughts. But to summarise briefly:

Talking about “Influencer Marketing” isn’t helpful. No-one shares a good definition of “influencer” and very smart people who I trust call into question their very existence.

I’d strongly caution against adopting new metrics or KPIs for influencer marketing. Instead, try to judge influencer activity against your existing activity. Does adding incremental “influencer” activity increase efficiencies at an acceptable cost?

And I’d strongly recommend using paid media to amplify and target content that you create with the influencers — we’ve seen excellent responses to promoted retweets, for example. On the whole, though (and for all the reasons cited) I instinctively prefer to think of talent as co-creators rather than as channel.

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