Framework for Measurement

We looked at developing a framework for understanding some of the ways that we measure social media, and in specific, the differing values of different types of participation in social media. One of the group (Mike Blowers) provides a better account of the session than I can remember, so it might be best to check out his blog post here.


To me, at the centre of this was the whole 1%-9%-90% thing, with a tiny fraction of viewers creating, more people commenting and a huge majority consuming from the sidelines. It’s pretty easy to measure the amount of people consuming a piece of media related to a brand, on the one hand through plain old traffic analytics, and, as the graph suggests, the perennial influence of search, but if we assume that the really sexy part of social media is the conversation, then we are driven to put a quantity/quality metric onto this user-participation.

When it comes to social media monitoring, one of the real difficulties we face is the lack of apples-for-apples comparisons across different media channels and conversation types. Do 100 people commenting on a blog post, 100 people commenting on Flickr image and 100 people writing reviews of a book on Amazon equal the same effect? Certainly not. I remember that Ben Bland of and his group had an interesting points system related to this, and I believe tied in nicely with this, so hopefully that will be online soon (says me, who’s posted this 2 months late!). It’s well worth looking at another attendee’s blog for discussion around this area: Lauren Fisher of Propellernet provides a nice commentary here.

From the point of view of someone in marketing, the diagram illustrates that our goal is often to facilitate (or, to quote Forrester’s Charlene Li, ‘energize’) people to contribute to a conversation and ultimately to act as brand advocates; to increase people in that last ‘creators’ band of the graph. In fact, perhaps ‘advocates’ is a little strong, since they aren’t necessarily going to be pushing the message you want, but certainly generating new voices within a conversation contributes to a stronger advocacy effect, and that’s something that we can measure (objectively in numbers, subjectively in sentiment) and that our clients should be pleased about.

Just to throw a big scoop of contradiction onto the argument, the whole question of the relevance of trying to quantify conversations should be mentioned. As I said in the session, it’s like trying to quantify love – like saying “I love you 6”. But our goal should be to match that with a scale. I told my wife that it was a on a scale of one to five.

It’s important to thank Jason Ryan of icrossing, who first drafted this graph and to Brad Little of Neilson, who gave us all stacks of great insight, but also everyone in all groups, who contributed to a great session.

Ruby Quince

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