In my last post, I mentioned creating a Social Media Scorecard using the Digital Footprint Index method and presented a point of view the Digital Footprint Index is the only approach, today, that makes any sense to build on, and possibly use – read my post On Measuring Social Media … thoughts and a Scorecard to understand this post.

Here’s part of my scorecard – it measures a social media footprint, if you will, of a small non-profit cancer society over June 09 – till the end of this month, now.

A couple of things to point out - my social media scorecard is built on Height, Width and Depth data that comes mostly from Radian6; I had to use Alterian/Techrigy/SM2 for the “Depth” part since Radian6 didn’t give me the Sentiment data I needed in the same granularity that matches the rest of the data they provided so well – my guess is that will be improved upon in a future release when Radian6 offers a “metrics scorecard” – as I’ve written about, recently (in Radian6’s Web Analytics and Salesforce.com Intergration).

Lets talk about the Digital Footprint Index Scorecard I created- I’m using percentages so you can see progress in the same measure across 3 dimensions. One of the issues that immediately sticks out to me is that much of the “width” dimension depends on “Twitter Followers” and Radian6 didn’t show many for August 09; it could be that one of the twitter accounts with a large following didn’t tweet about the Cancer Non-Profit, and that’s why the numbers went down – or it could be a data collection issue on Radian6′s part – don’t know – but here’s the actual data, below in raw numbers.

With so much “width” taken up with Twitter followers (which are the potential impressions a message might have through a network of followers – a metric that Radian6 nicely collects for us) it’s easy to see why so much focus is on Twitter now – it provides more “Social Media Impressions” than any other single thing we can measure … what else can possible match it at this point? I will go out and say it – Twitter is the closest thing to “mass media” in Social Media – as the “message” or “impression” can be broadcast out to millions of followers, potentially (though it says almost nothing about the likelyhood anyone sees the message).

This scorecard isn’t complete – any more than Radian6′s data is complete – it doesn’t have Facebook or MySpace (though a partnership with Unbound Technology would help Radian6 greatly in this); bit.ly data might also be missing, along with any other Url Shortening- and of course, Web Analytics data is missing – but the DFI Scorecard doesn’t need it while Oglivy’s Conversation Impact and Razorfish’s Fluent Social Media scorecarding do need a lot of other data and are more limited in what they actually can measure (to the Brand level), the DFI scorecard approach can be as granular as you want it to be – right down to a campaign or product.

Height (see below)

According to this approach - “Height” or “volume” of blog posts for this non-profit dropped in September (it’s still September – so the data up to today leaves out the last 4 days of the month – but even then ……….it’s less than it should be – given the trend – so that’s something to look at – dig into, so to speak – an actionable item).

Width

Here’s where the issue with “Twitter Followers” i mentioned earlier shows up – it might be some adjustment might be needed so that we give more importance to some parts than others – if so, it would be built into the “Width” part of the DFI, though I could see it in the Height dimension, as well – and there is a lot of customization on can do around DFI and still keep to the approach (I like this approach because there’s room for everyone to build on it – it’s like “open source”).

Depth (from Alterian)

It’s a bit more challenging to decide what to do with the “Depth” part of this index, especially since the data didn’t come from Radian6, I suppose we could look for general changes in sentiment over time, dig into the actual content and try to work sentiment up or down – providing the Sentiment part is accurate, at all – as we know it’s often flawed, and I’ve written about that several times (60%-70% accurate – but it’s misleading – it’s actually much worse than that because the sentiment and topic can both be off- still – scoring sentiment by hand is very time consuming – though that’s what Brian Solis does, so who knows, maybe that’s the way to go).

Ultimately, what we need is to visualize the DFI in 3 dimensions and over time - like a cube that changes, gradually over time, an animation, if you will – something like the Graphing Calculator by Runiter – but with a chart instead of an equation .

I will, however, try to say with this Digital Footprint Index theme for a few blog posts over the next few weeks – trying to dig deeper for insights that might come from it.

One more point, Radian6 provides what appears, on first viewing, to be a metric similar to “Unique Visitor” but for content – though I might be wrong about this …it’s called “Unique Source Count” – and might be the absolute number of content of any type, without syndication involved – I’ll need some feedback from Radian6 on this – because if it is what I think it is – it could also be useful in the Digital Footprint Index.

Enjoy, and let me know your thoughts about it.