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Note that I do recognize the dissimilarity between groups like Marketo and Yodle.  (Yodle being much more of a local agency, while Marketo is a lead nurturing saas solution) They are trying to appeal to many of the same B2B marketing solutions, and can definitely do so using vehicles like Facebook and YouTube.

This data is current 12/26/2011.

It is of no surprise that the developer solutions (Hubspot, Constant Contact, and Marketo) rank better than marketing solutions like Reachlocal and Yodle.  Reachlocal especially, is well known to put much more emphasis on direct relationship sales.

Hubspot’s 12 ‘follows per tweet’ to date, versus Constant Contact’s 3 is worth noting.  In a Quora forum, Dharmesh Shah (Huspot CTO) and Mike Volpe (Hubspot CMO) briefly comment on their methods.

Also worth a mention is the standing ranks on YouTube metrics.   Both Hubspot and Constant Contact are approaching the 1MM view milestone of total ‘Video Views’.

More on this, along with company blogs in future posts.

I decided to do this for the following reasons:

1. I have about 150 followers.  (I can’t say I was too worried about exposure)
2. I rarely use Twitter, and when I do, the messages are usually direct.
3. I think this is the third best twitter handle for my name.  ’Superfreakin’ is twice as long (6 more characters) ..even if it does have a kind of porn star appeal.
4. Vanity naming is preference I have on all accounts if possible.

The main reason not to has to do this; with how much time I have had the account for, it did build some history with users.  I didn’t know exactly, but I would say it’s 2 or 3 years old.

One interesting thing to watch will be Klout.  (The @superfreakin score was 37 and the @jaoren score was 11)  We will see..

For those that have not tinkered with Omniture, there are three levels that one can segment traffic to.  (They call them ‘containers’)  The Page View, the Visit, and the Visitor.

Before explaining what each of these containers does, let me first define what each metric is:

1. Page View – A web based file resolved in a browser.  Pretty straight forward.
2. Visit – Starts when the first page loads, of the site  being tracked.. continues until all activity has stopped for 30 min.  (So, I can leave and come back to the site, and still be under the same visit)
3. Visitor – Persistent metric to the prior.  Regardless of the number of visits, if the cookie remains active in the browser, this will be read as the same visitor.  (and a repeat visit)

So to create a segment, I can apply any reported characteristic to whichever of these “containers” I choose, and apply any metric to that subset of traffic.

As such, the page view container is the most restrictive, and the visitor is the most broad.  If I decide to segment traffic based on page name, and only want  to show the amount of page views where page name was equal to ‘Page A’, I would apply it to each of the three layers:

1. Page View – It would display the page views that where page name was equal to ‘Page A’.
2. Visit – It would show the number of total number of page views, from all the pages where ‘Page A’ was referenced in the visit ..at any point in the visit.
3. Visitor – This would be the total number of page views from a browser, if the browser had loaded ‘Page A’ at any point. (Or, in all visits, was there ever been a ‘Page A’ page view..)

I write all this so I can complain about the onclick.

The onclick is the javascript function for an image request when an item is clicked on.  (Not surprisingly)  Onclicks are commonly used amongst publishers to capture usability insight, as clicks do not result in new page loads much of the time  ..which is fundamental for Omniture to function.

However, and this is the rub, the onclick image request fires outside the page being resolved in a browser..  As such, it can only be segemented to the ‘visit container’ and not the ‘page view container’.

This causes an inflation of numbers as there are, to use the example above:

More site-wide total page views, where the visit accessed page A at any point (Visit container) than actual page A page views (Page View container).

This holds true for all onclick segmentation, and this has plagued me since I have worked these analytics suites.

Correlation and subrelation are the only ways to get clean data with an onclick.  (These will be discussed in other blog posts, if you are not familiar) The big handicap with those two is the number of rules/traffic characteristics that can be applied is greatly reduced..

Stay tuned. More on those later.

My plan was to put something (anything) on my .com namesake.  I have put several quick-setups in place, most are just WordPress experiments with some custom attempts. Nothing panned.

On the latest round I had a few defined objectives.  1) I wanted to set up just one page.  2) Lots of feeds (See the Johngraph) 3) Ties into just one or two apps for content gen.

My original plan was going to be a Tumblr plugin that would generate WP posts, so I could just run everything through my Tumblr account.  No plugin like that exists, so I had to suffice for a widget.  And I have to say Tumblr is a great widget.

Done.

But now we have the hard part, actually filling this with  *something*…   media files from mobile apping, check-ins, tweets, and  ..oh yea, WordPress blog posts.  I have to get on my igame here.

I installed a really good app called EverPress..  It automatically passes over the content of public notebooks and saves it as a draft in WP.  I could save an article, pass it over as a WP draft and write, in essence, an editorial on it.

A few limitations are:

1. It shrinks pictures down from their orig size, which is captured by the Evernote feed.
2. The content drops out at 1024 characters. (Huge limitation)
3. Ref URL isn’t brought over with the feed, so it has to be captured manually.

Even with these drawbacks, it is still a very effective method to pushing snippet content to WP for ideas on articles.  (Evernote itself is such a good tool for capturing online content)

The days of regedits and configsys hacks, and frankenstein computing seemed to have been lost to the button pushing of devices and laptops.

I agree.  There is something to be said about the age of dirty beige, and the breaking down of the different components, both software and hardware.

We likened it to the whole story of the American automobile, maybe 50 years ago.

In the 60s and 70s cars were labored over in a very similar way.  The were hobbied out of necessity, and ultimately developed into more.  As did the people working on them.

And when the 80s rolled around, cars were bulletproof.  Automobiles really only required gas, oil, and tires.  Cars were worked on by the owner much less.  People became less knowledgeable about how cars really worked, and just took their performance for granted. And they really could..

Question being, will the masses understand less about computing as their computers learn more?

Last day of Movember.  Pictured here with two other Movemberists.

I managed to do a pretty decent job keeping track of facial hair by shooting a picture (or a few) nearly every day starting in Halloween.  I did this with Photo Booth and then screencast it to some boogie sounds.