Jason Egan started a really cool thread over at the Yahoo! group asking about people who Twitter and it got a ton of response. I think W. David Rhee from OX2/LBi is working on a master list but I thought of a more “Web 2.0″ way to self-identify using Twitter channels and Twemes.

The channel “#wa” was more or less open (as are most channels, for now at least) and so if you’re a Twitterer and you’re writing about web analytics, you can help create what might become a useful body of knowledge and help self-identify like minded individuals on Twitter by simply adding “#wa” to your twits.

So I might Twitter: “#wa Boy, people sure are complaining about Omniture being slow today … is it slow for you too?”

You can follow the channel at http://twemes.com/wa or subscribe to the RSS feed http://twemes.com/wa.rss

For me the jury is still out on whether Twittering is actually useful, although I do admit it’s kinda fun (at least when Twitter is not down, which it is from time to time.) Who knows, maybe the #wa channel would become the new Yahoo! group, complete with inappropriate Fred McMurray comments and everything ;-)

This morning the fine folks at Nedstat in Holland published a white paper that Michiel Berger and I co-wrote titled Measuring Multimedia Content in a Web 2.0 World.  This free white paper explores the emerging direct measurement model for multimedia content by examining several common business cases for deploying video and provides a new set of definitions and key performance indicators (KPIs) designed to help companies effectively track their investment in video based content.

The timing is somewhat ironic because Judah has been writing a fair amount about Video Analytics over in his blog — I guess great minds think alike!

While video measurement has been around for awhile, the new social media certainly increases the complexity associated with determining the efficacy of video from a business perspective.  The folks at Nedstat are committed to helping their customers resolve these issues, and are generously making our white paper available without registration requirements.

You can read the press release about the paper’s availability or download your own copy right away.

Thanks to Tealeaf I’m excited to be able to present a free webinar on December 11th titled “Who, What, Where, When, and Why: Understanding Visitor Interactions on the Internet.” I’ll be presenting my thoughts on Web Analytics 2.0 and discussing the Web Site Optimization Ecosystem fundamental to helping companies effectively measure and manage visitor and customer experiences in a Web 2.0 world. Plus, everyone who registers will get copy of a whitepaper I recently published sponsored by Tealeaf titled Customer Experience Management and Web Analytics: From KPIs to Customer Transactions.

When: December 11th at 9 AM Pacific / Noon Eastern
Register at: The Tealeaf web site

If you’ve ever wondered about Tealeaf and how their technology is best integrated with your existing web analytics practice I’d encourage you to attend this free seminar.

My post last week on measuring visitor engagement was pretty long by the time I outlined the calculation, so I put off publishing examples of how the metric could be used until now. I’m excited to see that this topic has generated so much interest, both in terms of comments and emails sent to me directly.

My goal for this post is to provide a few examples and explanations to show how the metric can be used to supplement our otherwise already-rich set of web analytics data. Since so many folks have been willing to explore the engagement metric, I have embedded a bunch of questions in this post in italics that I’d love your feedback on.

Distribution of engagement scores and segmentation. Here is the distribution of engagement scores for about six months at Web Analytics Demystified by percent of visitors. As you can see, these scores are left-skewed and tail off as the score increases, showing that nearly half (47.6%) of visitors to my site are “poorly engaged”. When I look at this distribution it makes perfect sense to me — what do you think?

I have created segments to group visitors by their engagement score: “Well engaged” visitors have engagement scores over 30%, “moderately engaged” visitors are those between 10% and 30%, and”poorly engaged” visitors score less than 10%. These segments can then be used to explore how the behavior of visitors in each engagement group differs by looking at my page and referring source dimensions (page, content group, referring domain, campaign, search phrase, etc.)

Identify relationships that might otherwise not be found. At the top of this report you can see the pronounced difference in visitor engagement (and traditional metrics) for “branded” and unbranded searches (”None”) bringing visitors to my site. Now, because branded searches are a component of the calculation (Brand Index), you definitely expect to see a difference between the two engagement scores. What is interesting is that while other metrics (duration, sessions per visitor, page views per session) show a slight difference, visitor engagement and conversion are all three times higher for branded searches. I think this difference observed in all the metrics is further evidence that brand-driven searches are bringing more engaged visitors — what do you think?

In the middle table you can see search phrases bringing visitor to my site, showing visitor engagement, page views per session, and sessions per visitor. Here three phrases stand out to me:

1. “web analytics book” and “web analytics process”, neither of which are particularly distinguished from other search phrases based on page views per session or sessions per visitor but both of which have visitor engagement scores over double my site-wide average of 8.8%. This is important to me because these are un-branded search terms that are critically important to my business.
2. “vendor discovery tool” which would appear to be pretty important based on traditional metrics but only stands out slightly using the visitor engagement score (at 13.6%) I spend a lot of time trying to figure out how to drive folks using the vendor discovery tool to take other actions (buy books, inquire about consulting) and this data suggests that there is an unrealized opportunity.
3. “performance indicators” which shows that the visitor engagement metric is useful to identify terms that you’d think are important to the site but aren’t attracting the right audience (average engagement score for these visitors is only 5.6%)

I think this level of information is actually pretty helpful for identifying search marketing opportunities — what do you think?

Engagement-derivative metrics like “Percent Highly Engaged Visitors” are useful. Here you can see a select group of referring domains showing the percent of highly and percent moderately engaged visitors they’re sending my way (with conversion to show that engagement and conversion are in fact different!) Avinash Kaushik is sending me a few (0.2%) highly engaged visitors (thanks!) but Ian Thomas is sending me a bunch (70.4%) of moderately engaged visitors, many of whom are purchasing books (1.2% conversion rate.)

By looking at traffic from Avinash’s site over time (bar graph) I can see peaks and valleys in overall engagement from folks coming from his site, which would be useful to back into those peaks to try and determine what other blogger’s readers might be reacting to when they’re exhibiting highly-engaged behavior on my site (see late August and early September.) Given that Clint proved that conversion is a poor measure of success when trying to evaluate traffic from other bloggers, I think visitor engagement is useful for examining the non-revenue value of referring sources — what do you think?

Those of you who are looking for correlation between engagement and conversion, have a look at the data for Mr. Jim Sterne’s wonderful site emetrics.org —  5.6% of the folks coming from Jim’s site are highly engaged, 66.2% moderately engaged, and man-oh-man does Jim help sell some copies of Web Analytics Demystified.  You’re the man, Jim!

Visitor engagement is globally useful. At least in Visual Sciences Visual Site you can apply engagement metrics and segments to pretty much any dimension tracked. Here I’m looking at the percentage of “highly” engaged visitors (50% or more) in my “well engaged” segment broken down by country. Now, this is certainly more interesting in light of the total volume of traffic coming from each geographic location, and as I think about localizing my books and planning future trips around the world this information becomes very helpful.

There is more, including some of the more granular visitor-level stuff I talked about in the first series of posts on the subject, but I want to be sensitive to protecting the identity of individual users on my site. If you’re interested in helping me collect some “ground truth” regarding the engagement calculation, write me and I’ll explain how you can help.

So what do you think? Do the screen-shots help you understand the calculation better? Or do they still make it look super-complicated and scary? Is there something specific you’d like to see me demonstrate with the calculation? Or do you think you could come up with these same insights using more traditional metrics?

If you’re reading the web analytics blogs, you’ve probably already heard about the recent presentations I’ve given on the subject of “Web Analytics 2.0″. The future of web analytics and the relationship between Web 2.0 technology and measurement is something I’ve been talking about for over six months — I actually have a Web Analytics 2.0 workshop that I regularly give that you can read about under Analytics Consulting on my site — but given that it is “conference season” it is no wonder that this subject is getting attention from other folks in the industry. I have given my presentation at Web Analytics Day in Brussels, SEMphonic X Change in Napa, and will be giving a variation on same at Jim Sterne’s Marketing Optimization Summit in October.

Due to demand, you can download a PDF of the presentation from the white papers section of my site. If you’re interested in learning more about Web Analytics 2.0, please give me a call and I’d be happy to discuss it with you.

Strangely enough, the slides that are generating the most interest and commentary are not those about the Web Site Optimization Ecosystem, the integration of quantitative and qualitative data, or the Web Analytics Demystified RAMP, but rather the few slides I included outlining my thoughts about Web 3.0 and what I am calling Web Analytics 3.0.

What the heck is Web Analytics 3.0?!

Before I can tell you what Web Analytics 3.0 is, I need to tell you what I think Web 3.0 is going to be. The good old Wikipedia basically dodges this by saying:

Web 3.0 is a term that has been coined with different meanings to describe the evolution of Web usage and interaction along several separate paths. These include transforming the Web into a database, a move towards making content accessible by multiple non-browser applications, the leveraging of artificial intelligence technologies, the Semantic web, the Geospatial Web, or the 3D web.

While I know that Judah is all hopped up on the notion of the semantic web, after having traveled to Tokyo and Europe in the past month, I find myself absolutely convinced that the next technology era will be characterized by our collective ability to access the Internet anyplace, anytime, using so many devices we begin to look back on computers much the same way young people do television today — as something nice to use when YouTube is unavailable. Rolf Skyberg, a disruptive innovator from eBay who I met in Rotterdam a few weeks back, called it “digital ubiquity” — the point where we forget that the Internet actually exists and take our ability to access information completely for granted.

Given so many sexy alternatives — 3D web, transforming the Internet into a database, artificial intelligence, and the such — why am I so convinced that in the next three years we’ll be talking about Web 3.0 when we talk about mobile phones and non-traditional browsers?

Easy. The financial opportunity available via the mobile Internet makes the billions transacted today look like pocket change.

Think about it:

• Most people in the U.S. haven’t seen QR codes in advertising yet, but they will.
• Most mobile users around the globe don’t have access to a brilliant browsing experience through their phones, but they will.
• Most marketers aren’t advertising on mobile platforms yet, but they will.
• The most-loved company in the world isn’t in the mobile market yet, but they will be.
• Most mobile platforms aren’t passing the phone number (or a derivative) along with HTTP requests, but they certainly could.
• Most mobile platforms aren’t passing along GPS coordinates along with HTTP requests, but they certainly could.

Just think for a minute about how your browsing experience might change if the web sites you visited remembered you and delivered a tailored experience based on your demographic profile (theoretically available via your phone number), your browsing history (accurate because you’re not deleting your phone number) and your specific geographic location when you make the request?

Now think about how the advertising buying experience would change if the same were true, not to mention behavioral targeting. I mean, given GPS and demographic data, the behavior being tracked could be “works downtown during the day, checks Facebook on his phone often, lives in the suburbs, surfs sports scores from his neighborhood bar.” The Starbucks web site could have a link at the top with a coupon to save $1 on my double-tall non-fat latte in stores 1 block, 2 blocks, and 5 blocks from my current location; the Best Buy web site could have an in-store promotion for the store I am standing in, targeted to my age and gender; and my search engine could disambiguate my searches based on my demographic profile, my geographic location, and my recent search history to serve me paid search ads designed to influence my geo-spatial movement, not just my likelihood to click.

Jeepers, huh?

Sure there are privacy issues, but given the intensely personal relationship most people have with their cell phones, and the fact that far more people in the world have mobile phones than computers (Gartner estimates 271 million units sold to end-users by Q2 2007) it is easy to make a convincing case for mobile computing and digital ubiquity defining the next technology era, much like social networking, AJAX, XML, and mashed-up business models define the current Web 2.0 era we’re living in today.

Okay, mobile is the future. So what the heck is Web Analytics 3.0?

If Web Analytics 1.0 was all about measuring page views to generate reports and define key performance indicators, and if Web Analytics 2.0 is about measuring events and integrating qualitative and quantitative data, then Web Analytics 3.0 is about measuring real people and optimizing the flow of information to individuals as they interact with the world around them.

Your log file analyzer can do that, right?

The current state of mobile measurement isn’t about Omniture and Visual Sciences, it isn’t about JavaScript and cookies, and it isn’t about page views, visits, and visitors. Web Analytics 3.0 is going to be something completely different, and it will depend on completely new technology. Anil Batra and I talked about a project he did a few years back while he was at digiMine — he hacked together WAP gateway logs into a pseduo-log file, using the phone number in place of a cookie. Brilliant, and the fact that Anil has this experience propels him to very near the head of the class for Web Analytics 3.0 analysts.

In theory, the mobile Internet has many of the same measurements as the hard-wired Internet. But as the information the platform and device providers make available changes, something I very much believe will happen, the quality and volume of information at our disposal will increase and improve. The W3C document on “Mobile Best Practices 1.0″ already exists but surprisingly enough don’t have a section about logging requests or measuring user interaction. M:Metrics is out there providing analyst reports, but the service is more similar to comScore and Nielsen than WebTrends and ClickTracks.

This post is already extremely long but I wanted to start the conversation. In future posts, as time allows, I’ll expand on some of what I believe is possible and how. In the interim, let me know what you think! Am I wrong? Is Web 3.0 bigger than mobile? Or do you already have a handle on measuring your mobile content, even without GPS and phone numbers as unique IDs? Do you personally have experience doing analysis on mobile content? If so, I’d love to hear about your experience.

As usual, I very much welcome your comments but am happy to receive your comments directly via email. Also, if you’re a mobile service provider or device manufacturer concerned with how advertisers and marketers will measure their success through your platform, application, or device, I would love to talk to you about the Web Analytics Demystified vision for Web Analytics 3.0.