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 Its strength is its weakness – it will persist in referring back to previously cited sources, even if they become superceded.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.