Some are rubbing their eyes at the Adoble -Omniture deal, but I feel it’s worth a go, even if there is a 10% chance this merger will work  (as opposed to a 90% chance it won’t).

I hope the merger works and Adobe makes Flash easy to track (and give us some decent heat maps in Site Catalyst, while they’re at it,  too).

What I’ve noticed, in general, -  whenever something becomes too hard or complicated it doesn’t  get done well, or at all (but, easy and hard or subjective – what’s hard for one person might be easier for someone else – what  I’m referring to something being, overall, easy or hard – take Flash tagging, it is difficult, overall, to do well … though in some instances, it’s done very well, even, easily).

We can even argue that Web Analytics is both easy and hard – easy for a few and hard for most … but why is it hard  to do Web Analytics?  It’s hard to figure out what people want to measure and then, go back and enable content so it can be measured …… but does enabling content, so it can be tracked well have to be so hard?    Maybe … it doesn’t.

I think the prevailing thoughts are …. it’s  hard, accept it and plan for it. or Web Analytics is easy – just put Google Analytics on your site and you can do all the analysis yourself.  The truth is somewhere in between.

That reminds me; I wrote about  Oglivy’s Conversation Impact and Razorfish’s Fluent Social Media scorecards as  being  too difficult to set up and  maintain (in my post  On Measuring Social Media … thoughts and a Scorecard) and realized that fits in with this post, in that ways of collecting and processing data that become too difficult, in this instance, also,  don’t get done well, which is why I like a Social Media Scorecard based on Digital Footprint Index, instead.

Getting back to  the point of this post, the Adobe/Omniture merger might end up addressing the ability to track Flash easily, and well, consistently – and it’s not a small thing because so many parts of websites now are in Flash or Ajax,  if someone comes up with a way to make it easy to measure it – it’s worth the risk the venture might fail.

Looking at it another way,  90% of the time, we’ve failed to track Rich Media well, or at all (because it’s hard to set up and do well).

The other point of the Lucidiom post is that the people who work with and support site analytics are almost entirely separate (and don’t talk to each other) than the people who use Flash/Shockwave/Acrobat – here’s a quote from the post:

“….Do they share customers? Only at the logo level. They sell to the same companies, but the customers within those companies don’t typically see each other, let alone effectively talk to each other. In other words, in order to cause their client companies to adopt and integrate their disparate solutions, they are going to have to force their customers (in those companies) to behave differently. It’s not impossible, but such efforts typically take a very long time and a huge amount of cash to drive behavioral change, especially within large organizations.”

My point – Adobe doesn’t have to  force people to behave differently …. the  changes in how people are behaving  are happening anyway – just ask people who are doing Web Analytics if their jobs are starting to encompass (Search, Social Media, Online Marketing and now, Creative …. ).  They are, more and more.

I could go even one step more and say the obvious – Omniture was sold because …. they might not be able to maintain their business model much longer since site analytics has become a “commodity” (charging large sums to blue chip clients might not been sustainable in this new economic reality we find ourselves in), along with Web Analysts (since, with Google Analytics, now, everyone can be a “web analyst” (at least, that’s the perception … it doesn’t matter whether it’s true or not, any longer).

Maybe, just maybe,   Josh James took the money he could get now – 1.2 Billion, instead of waiting a few years more … and see the web analytics market continue to disintegrate as data collection and analysis becomes  a commodity.

And the idea of merging Analytics with Creative and Community Organizer (Social Media) isn’t entirely new, it was brought up at the Most Creative Social Media Campaigns of 2009 session at OMMA Global, last week by Matt Monahan of CF Holdings  – who said the idea team approach (that hasn’t yet been tried) for new creative is to throw in a Analytics Guru (like me) with a Creative (like the people who come up with pitches for PR and Advertising) plus a Social Media Community Organizer …. that this was the “model” that’s waiting to be tried …. that people are beginning to think is possible.

In fact, I think the only way we’re going to get regular, reliable, actionable insight – is by assembling teams like this …. that fact that it hasn’t happened yet, or much, is besides the point … it will happen, more and more – and some of the “web analysts” the “Omniture people” are going to be talking with the “Creatives – Adobe” people in the same corporation – regardless of this deal happening, or not.

Isn’t that what the Adobe-Omniture merger is really about? … helping this merging to happen?

True, the Creatives, Social Media Strategists/Community Organizers and Web Analysts do talk now … though they don’t normally collaborate well, or at all… and the Lucidiom post mentions people who use Adobe products don’t generally talk to people who use Omniture.

Maybe …but …  that’s a perception … and it’s changing ..  the Adobe-Omniture deal is just an acceptance of that change – that’s what i think.

By the way – some other analysis of the Adobe – Omniture deal are Marketing Pilgrim’s  Adobe’s Analysis of Omniture: Pass the Cigars, It’s a Buy! and VentureBeat.

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.

I planned to write one post that shows my own approach to Social Media Scorecarding but found there’s too much to discuss – so this first of two posts talks about what is wrong with two of three Agency Approaches to Measuring Social Media I will discuss below.

First, I read Agency Approaches to Measuring Social Media by Nathan Gilliatt recently and his post comparing Conversation Impact , Fluent 2009 and Digital Footprint Index, lead me to think deeply about all three approaches and come up with my own scorecard, based on the Digital Footprint Index with I will publish in my next post.

I think the Oglivy’s Conversation Impact and Razorfish’s Fluent Social Media scorecarding are hard to maintain – maybe building indicators that are hard to compile and maintain are not worth the effort – I’ve thought long and hard about this.

I’ve settled on Digital Footprint Index as the best scorecarding approach, even though there are few practical examples – and it’s the only approach where a Scorecard can be created entirely from a Social Media Listening Platform such as Radian6 (or in my case, Radian6 and Alterian/Techrigy/SM2) alone.

Before I show you my scorecard for a small non-profit that is totally unconnected with any Public Relations Social Media metrics work I’m currently working on – I want to point out why I suggest avoiding Oglivy’s Conversation Impact and Razorfish’s Fluent Social Media scorecarding. As Nathan Gilliatt points out in his post …..

• Conversation Impact, Ogilvy proposes a framework with three sets of metrics that correlate to the traditional marketing funnel: Reach and positioning, based on a combination of web analytics, media analysis, and search visibility; Preference, based on media analysis and traditional research; and Action, based on measurable client objectives (such as sales conversions).
• Social Influence Measurement, Razorfish (with TNS Cymfony and Keller Fay Group)The SIM score, as introducing in the Fluent 2009 report, compares sentiment for a company to sentiment for its industry. The report also mentions share of voice and weighting for influence, although the formulas for the metric do not.

… both of these approaches rely, to some extent, on hybrid sources of information that can be unreliable and time consuming to compile and maintain. While it’s true that Radian6 and other monitoring platforms are beginning to incorporate hybrid sources of data (Radian6 has Compete, WebTrends and Salesforce data, to name a few – don’t know what Cymfony and Keller Fay Group incorporate since I’ve never worked on that platform) the current state of the field suggests that no platform has all the data you’ll need to do Conversation Impact or Fluent today, though it’s possible that Social Media listening platforms might someday have all the data needed in one place. That suggests, eventually, it might be possible to build either of these scorecards from one platform, but DFI is only one, I think, you could today.

I believe the Conversation Impact approach is flawed, Web Analytics data will vary from platform analyzing the same exact website, and many of the metrics needed are based on implementation, which will also depend on who sets up and configures the site analytics. When a firm wants to get ahold of a client’s analytics they often can’t get all the needed data, and even if they could, it’s often not in the right form for use with a Social Media Scorecard.

Search Visibility depends on keywords used and the method to query search engines – which is highly dependent on what package to run a ranking report is used and when it is run and on what engines it’s run against. The same ranking report run during a weekday and on the weekend might yield entirely different results because the size of search engine indexes changes hourly, and is often much smaller over the weekend, and even at night – esp with Google, which still holds the lion’s share of search traffic (not withstanding Bing coming into the picture).

To be fair, positioning data can now be obtained from Google Analytics, so, it’s possible that ranking data could make it into a Social Media Dashboard.

Conversion data that is used to measure a clients’ objectives is also pretty hard to get a hold of in a reliable way (though easier than good Web Analytics data, in my opinion) – and when you put the hybrid data together, with the social media listening platform you get a subjective “mess” that is both time consuming to compile, almost impossible to maintain past a campaign launch (read my post PANEL: The New Socialism – Marketing Industry Growth Engine? – OMMA Global)

Another problem with both Conversation Impact and Fluent is they’re better suited for Brand Measurement than Campaign Measurement – here’s one example – SIM score in the Fluent 2009 report is compares sentiment for a company to sentiment for its industry – that might not be so hard to do for a Ford Sedan against all Ford Cars and against the rest of the Car Industry – but what about measuring the SIM score of particular Razor Model, against all the other Razor models out there – researching that and then writing filters to pick up all that data is a nightmare – and maintaining it over time (if anyone cares to, is an even worse nightmare – given the current state of listening platforms – much of the data your picking up isn’t relevant and has to be filtered out). While one could come up with a Razor blade model, and it’s competitors, the problem gets almost unmanageable when your taking something more exotic, like a vacation package or service – and the metrics will be entirely subjective.

The only measurement platform Nathan Gilliatt mentioned his post that I suggest building on is the Digital Footprint Index. Here’s what I like about the DFI Index -

1. The Digital Footprint Index is based on the physical dimensions of Height, Width and Depth, and fits well with how we visualize and move in 3D space. According to Nathan Gilliatt:

• Digital Footprint Index, Zócalo Group (with DePaul University) evaluates a brand’s online presence in three dimensions: Height, which represents the total volume of brand mentions; Width, based on consumer engagement with online content; and Depth, based on message saturation and sentiment.

2. I looked at the DFI Index and the Scorecard can be done almost entirely from Radian6 with some help from Alterian/Techrigy/SM2 – this makes the scorecard much easier to customize, maintain and build on top of.

3. DePaul University contributed to the formulation of Digital Footprint Index and I trust a university to come up with more thought out solution to measurement.

I also thought about the hybrid approach of Oglivy’s Conversation Impact and Razorfish’s Fluent Social Media scorecarding as something that might be seen as a good thing – because if your using 10 sources of data, and one is off, you still have the other 9…ha! ha! On the other hand, if your looking at progress from month to month, or week to week, and some of your data is off, and you have 10 imputs to your formula – figuring out which one is off can become headache.

On the other hand, using Digital Footprint Index, Zócalo Group (with DePaul University) and just Radian6 with Alterian, you can still run into a problem with some of the data they’re gathering as it might not always be reliable, or correctly categorized – or your topic profile might not be sufficiently targeted enough to capture the right data – but at least – you can fix a problem in your topic profile and if Radian6 has a problem with some data they’ve gathered, you can take it up with them and they can try to correct an issue.

In my next post, to be published shortly, I’ll present my own interpretation of the Digital Footprint Index.