Was thinking about Gary Angels recent post titled Semphonic’s Two-Tiered Segmentation: Segmentation for Digital Analytics Done Right and realized the new Radian6 Insights features support such a segmentation, though imperfectly, as of yet.   Gary mentions that KPI (Key Performance Indicators) are often useless because they are based on a single segmentations that do not provide enough information – and which the state may be “quantum”, either true/false, all true or all false at the same time; here’s some examples he provided:

I picked these specific examples because in each case they’ve presented me with real-world problems in interpretation where different people actually put forward radically different interpretations of the data. Consider the following explanations of each statement:

Statement: Our traffic is up!
Explanation #1: We have more visitors with customer support issues because our last release sucked.
Explanation #2: Our SEO has improved.

Statement: Our Conversion Rate is down!
Explanation #1: Our new site design is broken.
Explanation #2: Our SEO is improved so we’re getting more visitors who are less qualified.

Statement: Our online revenue is up!
Explanation #1: We’ve improved our order process.
Explanation #2: Offline customers are shifting online but they spend less with us than they used to.

Statement: Our NetPromoter score is unchanged!
Explanation #1: We aren’t moving the needle on customer satisfaction and have to add deeper experience to our site.
Explanation #2: Our satisfaction with existing customers is up but our marketing has added more prospects that tend to have a lower score – the two trends pretty much balance out.

“….Management Reporting based on a Two-Tiered Segmentation is completely different than what people are used to. The most important KPIs emerge out of the segmentation – the simple counts of how many of each Visitor/Visit Type the site actually receives. These simple counts are often the most important KPI for any siteand I venture to suggest they are not even in the conversation for most expert report designers and KPI authorities.”

However using an additional level of segmentation (answering an additional question) takes the information out of quantum state and defines it much more acutely so it might then be helpful; here’s some examples:

If traffic for a site is rising, the first question I’d ask is “With whom?” Is traffic rising for existing customers, for online only customers, or just prospects?

If revenue is going up, I’d want to know exactly the same thing. Is revenue going up with all customers, or is it going up with customers who purchase high-end merchandise or is it up because we’re getting more customers?

…. The “Who” is the first tier in a two-tiered segmentation. It’s a classic database marketing style segmentation and it’s essential context to understand almost any metric. It doesn’t matter whether the metric you’re reporting on is Page Views, Visits, Conversion Rate, or Revenue – if “Who” is the first question you should ask when you see a change in the metric, shouldn’t the answer be baked into the reporting?

The second level of segmentation is the “why” -

….. 2nd level of segmentation – Visit Intent – is actually the most important segmentation in Web analytics. It forms the 2nd tier of our segmentation scheme and it is essential.

….Suppose traffic is going up and I find that the increase is due to more customer traffic. That’s interesting, but it’s still not enough information to create real understanding. The question I want a decision-maker to ask when I present a fact like “Customer Traffic is up” is “Why? What are they trying to do?” Are customers coming to the site to buy more, or because our latest product release is generating a dramatic spike in customer support visits?

…. If you tell me that Customer Visits for Installation Support are up, that’s starting to be meaningful. If you tell me that Revenue and Conversion Rate are up with Prospects coming to the Website with an “Intent to Buy”, that’s starting to be meaningful. If you tell me that time on site is down for Customers coming to find out a telephone number to call for information, that’s starting to be meaningful. Metrics have meaning (and the appropriate metrics can only be chosen) in the context of a segmentation – and that segmentation should have at least two tiers.

To be honest, who and why are two segmentations that are not always easy to supply in Web Analytics, much less Social Media Analytics.  The mapping of the data to segmentations that are meaningful yet almost always missing from Social Media Analytics, but they are sometimes present in Web Analytics when there is a customized implementation, usually in large organizations that can afford to think that way and pay the right people to implement it.

I took Gary Angel’s post and applied it to the Radian6 Insights that I’ve  been looking at for the last month or so before the news was announced earlier this week in Boston at #social2011.  Taking some screenshots earlier (while I still had access) I was able to collect enough information to answer the question I just posed.

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Here’s the possible segmentations available with Radian6 Insights as of last week –  the analysis was done using a query about the online buzz for a film over the last 30 days.

Using Gary Angel’s definition of a two level segmentation, we’d have to first determine a metric to look at (such as a change in amplitude in blogs, message boards, twitter, facebook, etc) and then answer  ”Who” is that change made up of  - that’s the first segmentation.

Given there is a limited amount of data in the Insights Cube so far, all we’ll get to today is a small sample of the total number of mentions on the subject of our query – and this level of segmentation (age / sex) that is being provided by Radian6 Insights overlay isn’t much different than what we’d have gotten from Sysomos MAP, Alterian SM2 or other platforms that attempt to provide some information on demographics.   However, it’s fair to say that Radian6 Insights, with data feeds from  as well as other platforms that are based on different collection and tracking methodologies, has the potential to be much more versatile than Sysomos or Alterian could ever possibly be, because no matter how advanced they become, they do not allow for new datafeeds or dimensions to be easily added where as the Insights platform does allow for this – while today there is less than a dozen Insights partners, and only 4 or 5 that are functional, in a year there may be hundreds, and some of those datafeeds might end up being your own house data files – something almost no platform outside of Insights could pull off easily today (and if they could – it would be costly custom hack on the backend).

So far, I answered the first of Gary Angel’s questions on segmentation – Who … in this case the “who” is made up mostly of young adults – 21-24 year olds and 18-21 year olds – and even if the actual numbers of insights are small – someone who knew how, could craft a good story out of this  - but remember, this is still not actionable – because we’ve only drilled down one dimension.   We could pull gender out  as well, but we’d have to dig around a bit to find out if the same insights that apply to age are also applying to gender.  Never mind, a Marcom shop is probably going to stop here and be happy they got this far.

What about the “second dimension”  or “Why”?  I find the Insights platform probably isn’t going to be that helpful here – but if so, you’ll have to dig around and use your imagination, somewhat – to find out “why”.  I used Klout (which is an entirely different data set with a different methodology and in all truth, different data applied to different online mentions) to come up with a possible “why” …. the 18-24 year olds are being driven by Lady Gaga.  Ha!

The main problem is ….. I probably can’t prove it – so I can do the second level segmentation, but chances are my level of accuracy will be low – and therefore, none of this social data, in it’s current form, is something you can go to the bank with.     On the other hand, it’s great for Insights that can drive new marketing campaigns, or improve what you have – so there’s value in it, but more for what you can learn about your audiences.

As far as the Web Journal Part of this post – here’s a few links to articles and posts I found interesting so far this month:

PeerIndex Interview – Felt I should take more time to look at PeerIndex – will try to do so in the near future

Philip Sheldrake’s presentations about Influence – met Philip at #social2011 this week – may be speaking at his conference in London next month – let’s hope so, it’s about the Internet of Things.

Did you know some people actually live in apartments in NYC that are 90 Square feet? Wha? That is like those storage rooms Japanese are said to stay in while commuting ….. all I can say .. that’s a closet not an apartment.  The video below was viewed over 3 million times since last October, when it was posted!

Also found Social Media Case Study: Tesco’s Social Media Vacuum interesting.

That’s it for now – hopefully a long but rewarding week ahead.