Measuring online influence

[This article was originally posted on the IIAR blog a few weeks back]

This second post on online influence looks at how one might measure influence using online metrics. It follows on from last week’s post which posed a lot of questions, but few answers. Fair cop.

But first, I think there are a couple of principles of influence to consider:

1. People buy people. Therefore influence measures need to identify individuals. It’s not sufficient to conclude that Gartner (for example) is influential – duh. Vendors need to know (a) who within Gartner is influential, (b) what’s their influence relative to other analyst influencers, and (c) what’s their influence relative to other non-analyst influencers. Influence isn’t distributed equally, either within organisations or throughout the market.

2. Influence is multi-dimensional. Some influencers are subject gurus, some command statutory authority, some are thought leaders and idea planters, some structure the financial elements of procurement, and so on. It’s important to understand why someone is influential, as much as the fact that they are influential.

So. Let’s look at some of the ways influence claims to be measured online:

- Citations – this measures the number of times a source refers back to an originating source. Google PageRank works this way: it rates pages highly if other people link back to it. It’s also how academic research works: a recent paper will refer to previous papers, and the more references a paper gets the more influential it is considered to be. Its strength is its weakness – it will persist in referring back to previously cited sources, even if they become superceded. It also build in something called the Matthew effect, where longevity is favoured over originality.

- Connections – how many outbound links a source has. LinkedIn, Facebook, MySpace, Twitter (following) and other social networks work this way. Count the connections to determine how well connected the person is. It’s also easy to fake, by link swaps, indiscriminate “friending” and so on.

- Subscriptions and readership – Technorati works this way, measuring the number of readers a blog has, and Twitter also publishes this information as followers.

- Noise – references to subjects and/or individual firms. Radian 6, Techrigy, and a bunch of other providers do this, measuring the number of times your firm is mentioned. Some also claim to measure the sentiment of the mention, usually using natural language processing tech.

All of these measures are indicators of online activity, and you can see the usefulness of them, as far as they go. They are, in my view, the equivalent of PR clippings services.

However, none of them measure whether the critical community, decision makers, are remotely influenced by online channels. It’s always necessary to ask: Influence on whom? Do any of these measures accurately assess the impact on real decision makers? In other words, do they measure the likely impact on behaviour of a buyer? Because if they don’t, if they measure a vague notion of industry activity or sentiment, then do they really reflect the ecosystem of influencers that impacts decisions?

More critically, can vendors construct marketing programmes around these measures to improve knowledge, lead generation and useful sales collateral? Because if they can’t, what are these measures useful for?

Tssk – more questions.That last one was rhetorical.

Next week’s post will probably pose more questions about how AR can use online channels to increase influence on their firms’ prospective customers.

Personal versus ‘Firm’ influence

Does a person’s influence come from their own expertise and authority, or does it come from the credibility of the firm they work for? Actually it’s a bit of both, and I was intrigued to read via Augie Ray that Forrester is banning its analysts from blogging outside the ‘official’ Forrester blog platform.

The policy reads: “analysts with personally-branded research blogs must take the blog down or redirect readers to a Forrester-branded role-based blogs” (Source: Sagecircle).

I wonder if this is in any way related to the rise of Charlene Li and her groundswell blog and book, which positioned her as a leading social media analyst, only for her to scamper away and set up Altimeter, personal influence and credibility intact.

It’s a tough one for firms in the influence game. Do you, like McKinsey, hide the identities and personalities behind an anonymous company byline? Does your company brand immediately convey authority? Or do you promote your top staff as stars, in the hope that their individual reputation rubs off on the rest of the firm, and subsequent sales?

There are two things a company can do. Firstly it can recognise where company influence is most effective. This is in market reach and independence. Market reach, it turns out is influenced by both company reach and personal reach. Few have both, many benefit from just company reach, and personal reach is (obviously) the most transferable. But many an influencer has underestimated the balance between the two, having left their prestigious employers and struggle to make it on their personal brand alone. Gary Barnett at The Bathwick Group (and ex-Ovum colleague of mine) calls the critical degree of personal reach the ‘personal escape velocity’, which neatly explains the idea. Does an individual have enough ‘velocity’ to escape the ‘gravitational pull’ of the company?

A company’s stance also determines precisely the extent of independence of an individual. One may be an acknowledged expert in an area, but if the person also works for a vendor then their overall influence is qualified by this, and diluted accordingly. A non-vendor company can increase the influence of its staff by having a clear position on independence. Most analyst firms get this now, but the journey was fraught with conflicts and there remain some ‘analyst-for-hire’* firms. Other types of firm can also benefit from a stance on independence, including consulting firms, channel partners, services firms, and so on.

The second thing forms can do to increase the influence of its staff is to get them closer to decision makers. Normally the people that get to talk to decision makers are sales people, which are exactly the wrong type of people to position as influencers. Now despite the warm and cuddly reputation that sales people have, they unfortunately possess a fatal flaw, in that they want to sell something. It’s the hardest thing to influence someone while trying to sell them something**, because you have a clear, unambiguous and pressing interest in the outcome. Additionally, most sales people lack sufficient expertise and other influence attributes.

But there are other people in an organisation that are more suited to be positioned as influencers. The best influencers often come from the technical department, product development and design areas. They have a deep expertise, enthusiasm and energy, and a surprising lack of interest in making a sale. These people can be employed as influencers on specific deals, but they become much more influential at a market level if given the scope and support. Many adopt blogs as a medium to increase their reach and frequency of impact, which is why blogs are a useful influence enabler, but other avenues of outreach should also be used (conferences, seminars, press, etc).

Consultants, analysts and any other adviser types have the best chance at influencing decision makers, because (often) that is precisely what they are employed to do. They advise decision makers on what decisions to make, and in that sense they are professional influencers.

Companies trying to position staff as influencers must make their employees at least as effects as the professional influencers for them to have measureable impact on decision makers.

*This term was coined by Bill Hopkins at KCG.

**In a B2B world, that is. The influence of sales in B2C seems to be much easier…

Influence isn’t pyramid shaped

I was directed to this video of a model for influence by a member of the Influencer Marketing LinkedIn group. Have a look.

It is, of course, utter nonsense:

Influence is not unidirectional – it flows in all directions. You are as likely to influence as you are to be influenced.

Influence does not cascade down from ’super-influencers’, despite what advertising executives might want you to believe.

Influencers are not always obvious and high-profile. You are more likely to be influenced by a Tesco’s wine buyers than any wine connoisseur.

Influence models need to be holistic in order to be effective. The decision maker needs to hear the same message from a wide array of influencers, so you need to identify and engage different types of influencer. Where are the health influencers in this pyramid model? Ethical sourcing and Fairtrade influencers? Retail supply chain?

There is danger in simplifying complex ideas like influence – communicating the wrong framework just stores up trouble later on.

FT’s Decision Dynamics – 3

This is the third and final part of a look at the FT’s Decision Dynamics survey (see here for part 1 and part 2. This part looks at the role of social media in decision making.

Frankly, I think there’s a lot of tosh and assertion on the importance of social media in influence. In short, while there’s been a rapid rise in the use of social media there’s little evidence that it’s impact decisions, particularly in the B2B world. We’re all blogging and Twittering and ‘LinkingIn’ to each other but not in the context of decision making. Anyway, back to the FT survey.

This first chart (below) illustrates that rapid rise in use of social media by enterprise decision makers. As the FT notes, “Work-related use of social bookmarking and community sites has exploded in the past year.” It has, but only to the levels seen to date for reading blogs and using professional networking sites.

(Source: Financial Times Annual Decision Dynamics Survey 2009)

The most telling chart, though, is this next one, which shows the usage of various types of social media in a work or leisure context. It’s interesting to see the use of social bookmarking (Dig, Delicious, etc) as a decision making aid. But webcasts and streaming video are also important and more established in the B2B marketing mix.

Most surprising, given the volume of noise on the subject, is the low penetration of Twitter.

(Source: Financial Times Annual Decision Dynamics Survey 2009)

My own take on this is that Twitter:

(a) Is useful if you know who you want to follow, but can’t help identify important people

(b) Is a great echo chamber of followers and followees, but has limited reach outside the Twitterverse, and

(c) Is really only used by people that don’t have a “traditional” job (autonomous, mobile, networked), which may include most of the readership of this blog and most analysts, but not your average CIO.

Who do you influence?

What, and who, do you influence? Thinking about influence from your own perspective can change your understanding of how influence works.

My list (work in progress):

• My Wife (but not much…)
• My kids (more than I know…)
• Extended family
• My boss
• My work colleagues
• Clients
• Prospects (maybe!)
• Wednesday night football mates
• Sunday morning football under13’s parents
• My window cleaner (competitive new entrant muscling in to his patch)
• Building Control (don’t build on Pitt Manor)
• Local politicians (you need to earn my vote)
• Teachers
• Neighbours
• etc.

And so on.

Epiphany and the customer decision journey

One of my most commented-upon posts in recent months was that on McKinsey’s Consumer Decision Journey. The most intriguing feature of the concept outlined by McKinsey was the non-linear structure of decisions. It’s clear to me that, having read Predictably Irrational, The Decisive Moment, and Risk, decision making is anything but linear.

So I did some digging into my Feedblitz archives to see what other posts I’ve read have to say on this, and found this post from February from Chris Koch at ITSMA. It notes the existence of an Epiphany stage in a decision lifecycle, which precedes awareness (or attention) and which marks the realisation of an important business need.

I remember reading it at the time and noting similarities between ITSMA’s Epipany stage and the Wave chart in the book (below). Epiphany corresponds directly to the first ‘crest’ in the Wave – we (imaginatively) call it Deciding to do something in the book. McKinsey call it a trigger event.

Realising that there’s something to be done is the first stage in a decision process. Influencing that epiphany – getting prospects to acknowledge that they have to do something – is the hardest part in the decision lifecycle. It’s where the majority of influencers have a role, where most of the activity and noise exists, and where buyer skepticism is most prevalent.

But it’s also the most important stage. As we say in the book, influence early and you influence the deal.

What’s your strategy for influencing influencers that impact buyer epiphany?

Influence doesn’t scale

Influence is not about scale – it’s about focus. Unfortunately, marketers are usually of a mindset that drives them towards ever larger numbers of people, as Seth explains.

One of my clients is obsessed with market reach – how many people can we get to? This ignores what happens when you get to them, and if you mess that up the scale becomes your enemy.

There are plenty of people with big market reach and little influence. Most bloggers and many journalists are examples of this. There are also plenty of people with low reach and lots of influence. Academics and sourcing advisors are examples here.

Understanding influencers is about using a rifle rather than a shotgun to target the key individuals. Scattershot approaches may be more familiar and comforting but they are measures of activity, not effectiveness.

LinkedIn influence – is this for real?

So I’m cruising around LinkedIn when I spot, under the heading Viewers of this profile also viewed…, this entry:

Hillary Clinton

I admit, curiosity got the better of me so I took a quick look. It is indeed the US secretary of state and presidential candidate. On her profile, again under the Viewers of this profile also viewed… showed:

Barack Obama

Rudi Guiliana

Sarah Palin

Matt Damon

Kevin Bacon

and so on.

Is this for real? Are politicians and actors really using LinkedIn as a networking device? Mr Obama’s entry has (not surprisingly) over 500 connections.

If it’s all a spoof it’s expertly done. The understatements are wonderful: Mr Obama describes his current role as “I am serving as the 44th President of the United States of America.” Kevin Bacon is an “Independent Entertainment Professional”. There is no hint of sarcasm or irony. It could be authentic.

Is it? Can anyone confirm this?

Linked helpfully informs me, on the How you’re connected to Barack sidebar, that I’m only 3 connections away from the president of the United States. But I suspect that everyone else is too. Unfortunately, LinkedIn also notes that “Barack Obama is not currently open to receiving Introductions or InMail”

Oh well…

PS – As far as I can see our own nation’s leader, Gordon Brown, isn’t on LinkedIn. Is anyone surprised by this?

Directional influence and the Obama question

A few days ago I read an interesting post by Auren Hoffman on homophily – the phenomenon of being affected by one’s friends and close associates. Intuitively this makes sense – we all make decisions influenced by those around us.

But there’s an important distinction to make between the existence of influence and its direction. What I mean is this: you might be influenced in the purchase of a new digital camera by a friend who has bought one recently. But are you more or less likely to buy the same model as your friend? You might be inclined not to buy that model, even though it might be the best model for you, precisely because your friend just bought one.

I was reminded of this case while reading Dan Ariely’s excellent Predictably Irrational. His example of ordering beer demonstrates the phenomenon at work. It turns out that when ordering out loud people in a group opt for more variety, not less. Ariely suggests that this is because people need to choose something different to show they have a mind of their own, that their order conveys individuality, or perhaps that they are trying to impress.

This might mean that people order beer they don’t actually want to drink. Irrational maybe, but experimentally validated.

The really interesting part is that when people are allowed to order in private, by writing down their order, they order what they want.

Understanding this, from an influence viewpoint, is important:

1. People are influenced by others, but that influence may cause a decision contrary to the choices made by others;
2. People may make better (or at least more truthful) decisions by being protected from the influence of others and making their decisions in private.

This is very pertinent today of all days, as the US goes to the polls. The well-documented Bradley effect is an example of how some people will state their voting intentions in public, but vote differently when in the privacy of the polling booth. Will people who said they’ll vote for Obama really vote for him?

We’ll see shortly in which direction the US public has truly been influenced.

The influence of online product reviewers

Rubicon Consulting has written a white paper based on research conducted on US-based web users. Rubicon is run by Nilofer Merchant, with whom I worked in compiling case studies for the book.

There are some important points to pull out from the study. It finds that those people that regularly post reviews and comments are not your average customer, but enthusiasts (or enthusiastic detractors). Some firms may decide that these folk exist at the extreme ends of the customer spectrum, are not typical of general customer, and can therefore be ignored.

This is a mistake: although average customers don’t post reviews they do read them. Importantly, product reviews drive product purchases, so ignoring the review posters is dangerous. As the paper concludes:

“The most frequent contributors are the influencers, and they have a strong influence on purchase decisions because they write most of the online recommendations and reviews.”

This means that firms can’t ignore frequent contributors, but they have to talk to them in a different way to ‘normal’ customers. This is music to my ears, echoing Influencer50’s own mantra of “Don’t pitch to influencers.”

Other findings I picked out include:

• Approaches that work well in one type of community may fail utterly in another. Confirmation of the ‘horses for courses’ guide to influence ecosystems.
• Confirmation of the 90-9-1 rule: 90% of users are lurkers, 9% of users contribute from time to time, and 1% of users participate a lot and account for most contributions.
• Influence of product reviews varies by category. You’re more likely to use an online review to buy a digital camera than you are to choose a doctor. (I’m relieved to hear this!)
• Online discussion is theatre: “Web discussion is a performance in which a small group of people interact with each other, and with companies, for the benefit, education, and amusement of everyone else.” Understand this and it shapes your entire approach to online communities.

There is a ton of other information on web usage in the US, which makes interesting reading. For example, the research finds that web users are more likely to vote Democratic. That should be an interesting theory to check in the coming week…