I found myself thinking, “Are we really having this conversation?” today after reading Steve Rubel’s post today on “What will replace the allmighty page view?” where Mr. Rubel commented:

“The page view is on life support. It fails to capture all of the myriad of ways consumers engage in online activities without ever leaving a web page.“

Okaaaaaaaaaaaaay.

I suppose Steve is coming at this from a different perspective than anyone who works in the web analytics field, more-or-less looking at page views as a basis for comparing the relative value of one advertising opportunity to another. If that’s the case then yeah, page views are becoming increasingly limited in their utility.

But damn, as a web analytics professional, doesn’t all this talk about page views going the way of the Dodo bird just make your stomach feel all funny? Like, you know there are problems with the metric, but A) when compared to the other problems web site operators have vis-a-vis counting (cookie deletion, cookie blocking, poor implementations, caching, robots, lack of understanding, lack of interest) and B) when put in the context of the number of sites that still rely on good old fashioned HTML, don’t these proclamations seem a bit premature?

Is it just me? Maybe it’s just me …

Anyway, we can stop worrying about dying pages and dying page views now since the answer has been with us the whole time. It’s not unique visitors … too many problems with how unique visitors are counted, what with cookie deletion and some of the inaccuracies ascribed to panel-based services. It’s not time spent on site … the problems with this metric as the basis of comparison are many (connection speed, amount of content, quality of content, bathroom breaks, etc.)

It’s sessions.

Yep, sessions. Good old “start ‘em with the first page view and stop ‘em after 30 minutes of inactivity” sessions. And while they don’t necessarily solve the problem of how many impressions a site can serve (you need old fashioned web analytics for that), they provide a stable basis for comparison across sites:

• Sessions are defined by a widely-used and widely-understood standard, the 30 minute timeout between subsequent page views. Heck, in the web analytics industry, it’s pretty much the only standard we have …
• Sessions are counted once and only once when a visitor goes to a web site in a single web browser and are thusly not subject to inflation due to crappy web design or RIAs. No more complaints about MySpace!
• Sessions are time independent, except for the session timeout. You can click away all day and you’ll still only count one session, unless you walk away for 30 minutes and one second …
• Sessions mitigate out issues associated with error pages and the such, because again, the number of pages viewed is irrelevant after the visitor views the first page. Again, no more complaints about MySpace …
• Sessions are not affected by cookie deletion and are not always affected by cookie blocking. Whoopie! We can stop bugging out about cookie deletion …
• Sessions are not affected by users visiting sites from multiple web browsers, since regardless of location (home, work, etc.) the session is counted. Hurrah! No more massive over-counting of unique visitors during Fantasy Football season …
• Sessions can be counted even when the visitor is not on your web site, depending on what tracking technology you’re using and how it’s deployed. For example, a session can be counted when someone reads a post in their RSS reader …
• Sessions are easily tied back to relevant referring sources, such as advertising units, RSS feeds, search terms, etc. Yippie! Not only do we get more accurate counts, we know from where the sessions are originating …

Yep, good old fashioned sessions … who’da thunk it? You can call them “visits” if you’d like!

What’s better is that the reporting networks should just as easily be able to report on sessions as they do unique visitors. If they can report on “unique searches” and “time per person” and “page views” and all that, nothing should theoretically stop them from using “sessions” as the basis for reporting.

Clint Ivy pointed out to me that Hitwise uses sessions as the basis for their reporting platform, only they report however on percent market share and not the actual number of sessions which is almost certainly what advertisers would prefer to see. Neither of us were sure why they don’t give raw session counts, do any of you?

Just think of all the problems we can solve by using sessions to compare the popularity of web sites! No more complaints about newspaper sites reporting more unique visitors than live in the entire state. No more complaints about huge differences in reported numbers ascribed to cookie deletion. No more freaking out about inanimate objects dying …

What do you think? Am I crazy? Is it just me? As always, I welcome your comments.

(If you need to catch up on where we are to date, have a look at my last post in this series on measuring visitor engagement.)

I had a nice conversation a few days ago with Jeremiah Owyang, Web Strategist at PodTech.net, on how I have been measuring engagement. Jeremiah has been thinking about how engagement is defined for some time and had a very fresh perspective on the subject which has somewhat expanded my thinking on the subject. Jeremiah, by virtue of being an “A-list” blogger (IMHO) gets great critical feedback from folks like Forrester’s Charlene Li (who says that my measurement is too explicit, oh well …) After we talked, I realized that I really needed to get the promised post on measuring “social engagement in a Web 2.0 world” out the door. So here it is.

One of the links that Jeremiah references is this one from Wiredset, published in November of last year. In their post, Wiredset gives a definition of engagement as “a consumer based measurement that regards interaction with an aspect of a brand or media property” and goes on to say that “Web 2.0 Engagement” could include activities (Jeremiah refers to these as “gestures”) like:

• Publishing
• Creating and Publishing to a Group
• Posting
• Subscribing
• Favoriting
• Adding Friends
• Bookmarking
• Emailing
• Distributing
• Streaming
• Networking
• Creating Mash-up Content

I absolutely agree with Wiredset, and they go on to say:

“When measuring engagement, the level of user interaction (i.e. 200 vs. 2,000,000 streams) is an obvious and important component. Yet engagement is complex in that it is not comprised solely by clicks, but also a range of involved user actions.“

If you’ve been reading along the entire time, you’ll note that my current definition of visitor engagement is derived exclusively from click-stream data and it tries to be as independent of content as possible. While this makes sense for a lot of reasons, the larger conversation (as Clint and Jeremiah wisely point out) is about how a visitor engagement metric can help us better understand the value of emerging Internet technologies.

While Web Analytics Demystified is not your typical Web 2.0 or social community site, I have enough of the activities listed above on my site to apply a social media filter to my measurement calculation and look at the effects. Again, if you’ve been reading along, I covered many of these in Part III of this series.

Here is the list of things that I am tracking vis-a-vis social media/Web 2.0 on my site:

Now, up until this point I have basically fought applying any weighting to the visitor engagement metric, mostly because I think it’s pretty difficult to rationalize any particular weighting over another and it will complicate what has already been described as “the mother of all KPIs”. That said, I am scoring these social activities into what I call an “interaction index” (ratio of sessions with one of the activities above vs. sessions without) and using the interaction index to weight the visitor engagement metric.

So instead of the existing definition of visitor engagement:

We have the new definition of “Social Engagement”:

Both metrics are the sum of component indices divided by seven, so you can hopefully see that the latter metric is weighted by any contribution made by the “Interaction Index”. For definitions of the component indices, please see Part IV in this series.

So what does this give us? Well, if you were interested in tracking individual users based on their level of visitor or social engagement, you would be able to drill-down along each Web 2.0 activity and perhaps learn something interesting:

There is Frank Faubert from Unica again, not much more socially engaged with my site than he is otherwise engaged. Remember that Frank initially complained about his only having a 21 percent engagement score, to which I responded that I had lost him in my data. Well, I found him, and based on the evolving calculation, Frank is over 31 percent engaged but little of his measured engagement is “social” in nature.

But what if I drill-down along each of my defined social activities, what can I learn?

First we can see my good friend Jeff Katz, formerly of WebTrends, who is a regular reader of my blog and whose social engagement score is much higher than his visitor engagement score. Jeff has repeatedly joined the community (Web 2.0 Measurement Working Group, Web Analytics Wednesday attendee) and has also hosted a WAW event here in Portland, OR.

Looking at direct engagement via email, we can see the great Aurelie Pols from OX2 Belgium who has also submitted comments to my blog.

I can also apply the visitor and social engagement scores to other relevant dimensions like referrers:

Here you can see that I’ve calculated the variance between visitor and social engagement and am color-coding that against my site referrers. O’Reilly’s XML.com, E-consultancy, and Jim Sterne’s Emetrics web site all are sending visitors who are well-engaged socially.

Finally, you can see the difference between visitor and social engagement applied to the various blog posts I am tracking for Clint Ivy, Ian Houston, Robbin Steif, and Avinash Kaushik. Clint’s open letter to Jeff Jarvis (a controversial piece if ever there was one) is driving a great deal of Web 2.0 engagement amongst Clint’s readers. Nice work, Clint!

Hopefully you get the picture here. By weighting the visitor engagement metric with these social media activities, I am able to easily identify individuals, referring sources, marketing campaigns, rich Internet applications, etc. that are actively interacting, both on my site (join community, engage directly, submit a comment, contribute content) and off (host an event, share a social bookmark).

Wiredset’s proposes a distilled definition of “Engagement = Interaction/Attention” which makes sense to me … you have attention by virtue of their coming to the site, but can you drive interaction? I would propose that the visitor and social engagement metrics I have described in this series of blog posts describes this equation practically applied.

As always, I welcome your comments and criticism.

I got an email last night from Eric Enge who writes for Search Engine Watch. A conversation about web analytics that we had last month was recently posted. Eric asked me a variety of questions about Visual Sciences, making decisions based on data, uniquely identified users, content groups, some of the challenges associated with page tagging, and Avinash Kaushik’s 90/10 rule (which I disagree with due to the rule’s impracticality …)

If you have the time and inclination, give the interview a read, and thanks to Eric Enge for interviewing me.

A friend of mine in Atlanta, Tanya Echols, is looking for a metrics guru that can help the Cox family of newspaper web sites get more out of their investment in web analytics. I talked to Tanya briefly last week and she says the company needs an internal evangelist, someone who can really drive interest in web data and how that data can be used to improve the overall experience across the daily and weekly newspaper sites.

Tanya says this is a high-visibility position, reporting directly to her (Director of Business Intelligence) and supporting the Vice President of Digital Media and the President of the entire organization. In addition to web analytics data, COXnet has data from Scarborough, comScore, Nielsen, the major analyst groups, and an internal user registration system.

I asked Tanya how web data has been used in the past. She related a very good story about how, in the midst of a mad rush to be “Web 2.0″ with podcasts, web analytics was able to tell a somewhat counter-intuitive story about the value of podcasting to the company. Based on the results, resources were re-allocated appropriately, theoretically saving the company a substantial sum of money (although shame on me I forgot to ask how much!)

Given that Tanya is one of the nicest folks I know in the space, that she clearly understands the value of web analytics (and would thusly be a very supportive boss), and that COXnet is willing to relocate the right candidate, I encourage you to have a look at this posting if you’re interested in web analytics with a heavy media spin.

Check out the posting here and apply today!

Man, it seems like I just cannot get away from Gary Angel lately. He and I are engaged in some kind of crazy mutual-admiration society thing, which would normally worry me, but I know few people as into all this as Gary. He recently posted about “that darn engagement metric” where he expanded on some of my ideas and his response to my ideas and my response to his response to my ideas, etc. One thing Gary said really stuck out in my mind. Regarding my use of the visitor engagement metric to tell a story about the traffic that Marshall Sponder sends to my web site, Gary commented:

“This gradual building of a context around a measure is exactly how I think reporting actually works - and how analysis drives to actionable understanding.”

Exactly! The gradual building of context is what this is all about. The reason I’ve defined a visitor engagement metric is to provide another firm basis for the establishment of said context, another indicator on which we can draw to better understand a dimension or set of dimensions of data we collect.

Much has been written about the value of bloggers to business; it seems like you can’t open Business Week, Fortune, or even Newsweek anymore without having to read about the next big thing that bloggers and blogging are doing to change both business and society. But what can we know about the traffic bloggers send us? And what actions can we take based on that information? Let’s have a look …

Here I’ve rank-ordered the folks I list in my blogroll by percent of sessions they drive back to my web site. No great insights here based on my KPI “percent of sessions” but I suppose if I wanted to I could add Clint, Steve, Tim, and Eric Butler to my holiday card list.

Now I’ve added the session conversion KPI to my list of bloggers. Immediately I see two sets of actions I could possibly take: The first would be to send “much love” to Steve, Mike, Gary, Xavier, Manoj, Aurelie, and Tim for helping me pay for my children’s college education (all book proceeds go to my kids’s Fidelity 529 plans). The second would be to see what I could do to get the rest of the bloggers to say something like “Hey, go buy Peterson’s books, they’re great!”

Still, I should probably check to see first that these folks aren’t referring me traffic that later returns to the site and makes a purchase, right? I need to roll in a visitor-based conversion metric:

Ah ha, now I can see that I owe some serious thanks to Steven Jackson and the folks at the Blackbeak Blog! Better than one in ten people Steve has sent my way have made a book purchase, which is awesome. But it looks like I have a problem with bloggers like Anil Batra, Matt Jacobs, Robbin Steif and even Marshall Sponder. Zero percent visitor-based conversion to purchases on my site … must be some problem with how those folks are talking about me, right?

Oh, or maybe not, at least not in every case. I added a KPI for percent buy path sessions, basically the percentage of sessions in which a visitor at least starts down the book purchase path. Now I can see that Anil, Justin, and Robbin are all doing a pretty good job of getting people into the purchase consideration process, but for some reason those folks aren’t completing the purchase on my site. It’s not their fault, it’s my fault!

Damn.

But hey, maybe it’s still not my fault. Maybe even though the bloggers are sending me traffic that hits the buy path, maybe those folks aren’t really all that engaged with my site and content. Maybe the visitor’s they refer me are just looking at one page in the buy path and leaving, never to return.

Okay, or maybe not. Anil, Matt Jacobs, Marshall, and Clint are all sending me visitors that I consider to be “well engaged” with my site (my site-wide visitor engagement average is 30 percent.) Now I can see two clear action items:

1. I need to reach out to Anil and see if he and I can work out a deal to help further encourage his readers to completing the book purchase on my site. Anil, if you’re reading this, call me, we need to talk.
2. I need to reach out folks like Matt, Marshall, and Clint and see if there is some way I can get them to more passionately advocate for my books in their weblogs. Given that their visitors are more highly engaged than the “average visitor”, I have to believe their is an opportunity to sell more books.

But wait, I’m not done. In fact, I’ve only just begun to mine for the true opportunity here. But hopefully you can see, this gradual building of context is well-supported by each of the key performance indicators I keep in my arsenal, both simple metrics like “percent of sessions” and the more complex “visitor engagement”.

This post was a really long way of saying I agree with Gary about no one KPI driving a specific and easily understood action. All of our efforts are ultimately designed to help the online business better mine for opportunity and understand how that opportunity might potentially be leveraged. There are no easy answers, there are no silver bullets, there is no magic, nor mystery, nor puzzles …

There is only the gradual building of context …