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Eric T. Peterson has been working in web analytics for over ten years and has built up an incredibly rich body of knowledge about the subject, knowledge Mr. Peterson works to share every week here in his Web Analytics Demystified weblog. Whether you're new to the subject or the most experienced practitioner, you should join the thousands of people around the globe already subscribing to Peterson's blog and start reading today.

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Archive for September, 2006

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Good old oblivious Clint is building fun Excel stuff for all of us!

Clint, who unfairly criticizes himself for missing my reference to his work a few weeks back but responds with a cool tachometer visualization in Excel that you can download and use for your own key performance indicators. One of Clint’s readers wisely points out that the tachometer doesn’t provide any historical context when presenting a value but I would counter that as a component of a well-designed dashboard a tachometer can be the visual element that some people need to really grok the data.

And that’s all you’re really trying to accomplish, aren’t you? Getting “the man” to grok the data and understand that “something” needs greater attention.

I would caution user’s of Clint’s work to carefully select which metrics they choose to throw in their tachometer. I like Clint’s use of acquisition mode (he called it “acquisition index” but I think I defined it as “acquisition mode” in Web Analytics Demystified) but I’m not so sure about Visitor Growth percentage and Percent Returning Visitors.

Regardless, great work Clint!

Have kids? You have to see this!

The Juice Analytics guys are hysterical! Any of you out there with small children must be thinking to yourselves, “How can I get that head’s up display?”

(Props to Clint for coming back from vacation and catching me crediting the Dashboard Spy with the above image. I guess I just assume that all that dashboard stuff comes from the Spy …)

Avinash proposes a Site Abandonment Rate

While I was on vacation Avinash was prolific as usual. Earlier this week he proposed something he calls a “Site Abandonment Rate” which he defines as:

Site Abandonment Rate (in percent terms) = [1 – (the total orders placed on the website divided by total add to cart clicks)].

Pretty good, except his metric as defined is not useful to the many non-commerce sites out there. I would propose that what Avinash has described is actually the “Transaction Abandonment Rate” — the likelihood that someone starting an online transaction will actually complete the transaction.

This metric can be added to the cart and checkout abandonment rates that are already well described, as well as to the cart and checkout usage rates that describe the likelihood that a visitor or session (depending on how you calculate it) will result in business-positive actions.

If you accept this change in nomenclature, then I would propose that a more inclusive definition of “Site Abandonment Rate” would be something like:

Site Abandonment Rate (in percent terms) = Total sessions where session page views is less than “some low number” / Total sessions

This way, each site can define what “some low number” is for themselves based on their observed distribution of page views per session. Perhaps a good place to start would be halving your average page views per session (you watch that KPI, right?)

Now Avinash comments to someone named Angie that he worries about extending his “Site Abandonment Rate” definition to a non-commerce world, worrying about confusion with “site exit rate” and “content non-consumption rate” While I have no idea what a “content non-consumption rate” is, I know that my “site exit rate” is 100 percent and so is yours — you cannot calculate a sitewide exit ratio since all sessions ultimately end in an exit.

Perhaps what Avinash meant was the site exit ratio for a page or a process, such as the “Search Results to Site Exits Ratio” I describe on page 67 in The Big Book of Key Performance Indicators?

Regardless I suspect that the number of analytics professionals who would benefit from a more inclusive definition of “Site Abandonment Rate” far outnumbers those who would confuse this definition with the “content non-consumption rate.”

All of this reminds me of the metric “Heavy User Share” which I first described in 2004 in Web Analytics Demystified based on Eisenberg and Novo’s Guide to Web Analytics and also my percent low/medium/high click-depth key performance indicators described in the more recent Big Book of Key Performance Indicators. All of these metrics (Avinash’s included) are an attempt to describe some aspect of visitor engagement and their potential for success (usually described in your terms, not theirs.)

Anyway, thanks to Aviash for pointing out this valuable addition to the body of key performance indicators in the world. I’ll surely make sure it gets added to upcoming editions of my books (and credit the author, of course!)

Ian Thomas at Microsoft is into measuring Web 2.0 too!

I got an email and a comment from Ian Thomas who recently signed on board at Microsoft Digital Advertising Solutions letting me know that he’d blogged about my rant about measuring Web 2.0. While Ian disagrees with some of what I said it seems like we’re in agreement that some standards would be nice.

Since the original post I’ve had the pleasure of talking with a number of bright folks about the subject of how to measure “intra-page events” (how’s that for a nice new term we can all use?) and how said events should be associated with the “parent URI” and when an event should and should not be measured and whether an event is really just Web 2.0 for a “hit” which we all had agreed was a Sterne-ism for “How Idiots Track Success” …

Phew!

Regardless, I rather enjoy the challenge and these moderately theoretical conversations break up otherwise tremendously busy days. If you have thoughts on the subject I’d almost certainly love to hear them, either via comments or directly via email.

Mobilytics