(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 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.

While I have not had time to write Part V of my series on measuring visitor engagement, I wanted to take a few minutes to address some comments folks have made about the metric recently. It’s very encouraging to see folks like Gary Angel and Daniel Markus pushing the conversation about measuring engagement along as I can think of few more qualified to critique this work.

Gary Angel, who had very nice things to say about the metric, commented on how in some areas the metric is biased, specifically towards search engines and specific types of content. Gary is concerned that the Brand Index will unfairly bias towards search engines (given that one component is searches for brand-specific terms like “eric t. peterson” and “web analytics demystified”.) I examined this effect and it turns out that “branded searches” make up only a small part of the index for my site but Gary makes an excellent point, unnecessary bias should be removed from the index whenever possible. As such, in my current calculation I have removed this weighting from the Brand Index, redefining said index to only be direct sessions (non-search, non-referred.)

Score one for Gary.

Gary also commented that:

“… if I’m using my metric to measure the “engagement” produced by visitors who used a specific part of a site (like the blog or the press releases), it’s vitally important that my metric not include a strong built in bias toward one of the areas (like blogging). Some analysts might argue that this represents a flaw in the metric Eric proposes. I don’t think so. Every metric carries with it some biases – and no metric is appropriate to every situation.”

This is a good point, one that had been made by a handful of other folks who critiqued the metric early on. The problem I have with removing the Blog Index (ratio of blog reading sessions to all sessions) is the evidence that my weblog is a prime driver of engagement with my site and overall web analytics brand: Over the last 12 months, weblog subscribers are nearly 400 percent more likely to have returned to the site recently than non-readers; those visitors not subscribed to my blog (e.g., in Bloglines or Google Reader) but who are still reading blog content are 300 percent more likely to have returned recently.

Score one for Eric.

One thing worth noting, the way I am using Visual Site to measure weblog readership and subscription, this activity does not show up as traditional “page views” unless the reader A) reads the post on my web site or B) clicks through to the web site (at which time the post appears as a session “referrer”) — Visual Site is able to track external RSS and XML-based content using a non-page view event (something I call “reads”.) Not all web analytics systems afford their operators this flexibility so I thought it would be worth bringing up. This is part of the reason that the Blog Index needs to be a separate index, not part of the Click Depth Index as some have questioned.

But enough about Gary … Daniel Markus posted what I surmise to be a nice post about my visitor engagement metric at Marketing Facts late last week in which he called my calculation “the mother of all Web Analytics KPIs.” The post is entirely in Dutch and my Dutch is horrible so I wrote to Daniel and asked for a rough translation . While there were many good comments about the metric, they raised two concerns:

1. The calculation is complicated and difficult to understand.
2. There was some question of the utility of this metric, essentially calling into question the overall “actionability” (not a word) of visitor engagement.

Regarding the complexity of the calculation, as Gary has so eloquently stated any number of times, no indicator or metric is any use without understanding its components, its definition, and its inherent biases. Clearly the onus is on the web analyst to explain the metric and it’s definition to any audience they present engagement data, especially given the complete lack of formality around measuring “engagement” (at least until you started reading my posts on the subject.)

Given the complexity of the calculation, the latter concern is valid but one that misses the point of the metric. There are any number of loose definitions of “engagement” floating around in our community — duration, page views, average page views per session, sessions per visitor, etc. But none of these more easily understood (note: not easily interpreted) metrics, in my mind, captures the essence of an engaged visitor.

Visitor engagement has to be examined over diverse criteria, simple assessments simply do not work. To wit:

• To say that session duration is a good measure of engagement is fine, unless the visitor never returns to the site.
• To say that a high number of page views is a good measure of engagement is fine, unless the visitor runs up those page views in a very short period of time and was unlikely able to actually read content.
• To say that recency of visit is a good measure of engagement is fine, unless the visitor has only looked at your home page and left.
• To say that direct visits are a good measure of engagement is fine, unless those direct visits lead to short sessions of few pages viewed and the visitors never return.

I believe that the complexity of the calculation is where visitor engagement derives its value. For practitioners who are lucky enough to have access to a platform that can actually make this calculation and who are willing to take the time to explain to their audience what the metric measures and what its limitations and biases are, the metric can yield insights that would be unlikely to fall out of “traditional” web analytics.

I will leave you with an example of how I am deriving small insights from my measurement of visitor engagement.

Marshall Sponder is the WebMetricsGuru blogger and all-in-all a pretty nice guy. He and I had a little tiff awhile back over Avinash’s web analytics blogger index (something Avinash has stopped doing for some reason …) when I was less than complimentary about the volume of web analytics posts that he produced relative to his blogging in general. Examining traffic metrics from Marshall’s blog I would interpret the value of having a good relationship with him based on a set of commonly understood data:

Almost no volume and no books sold. Come on Marshall, let’s see a nice recommendation for Analytics Demystified already! 😉

But wait, what if I have a closer look at the measured engagement of the visitors he’s been sending to my site:

While my “average” visitor to the site is only 24.2 percent engaged, visitors from Marshall’s posts are nearly 40 percent engaged with my site and, more importantly, of these visitors almost 10 percent are “highly engaged” (50 percent engagement or better.)

Marshall may not be selling books yet, but I have the nagging feeling if he tried even just a little, he could probably drive pretty good numbers given the engagement of the audience he referrers.

Now just imagine that you were running a million or billion dollar business, looking for new opportunities on the Internet. You have hundreds-if-not-thousands of sites sending you visitor traffic all day, every day. Maybe some of these people make purchases, but maybe you have nothing for them to purchase … how do you decide who to spend more time with and who to ignore?

Me, I’m going to write nice things about Marshall Sponder and if the folks from e-consultancy call me and want to do another interview, I’m taking that call right away! How’s that for a KPI defining an action?

For those of you keeping track at home, this is the fourth in what will likely be a five-part series on calculating an “engagement metric”. The first three posts are here:

• Part I
• Part II
• Part III

Originally I had postulated that an engaged visitor, at least on my web site, can be characterized as follows:

1. The visitor views “critical” content on the web site
2. The visitor has returned to the web site recently
3. The visitor returns directly to the web site some of the time
4. Some high percentage of the visitor’s sessions are “long” sessions
5. If available, the visitor is subscribed to at least one available site feed

Basically, the final calculation, one revised thanks to the valuable feedback of dozens of folks, is essentially the same with a few slight modifications. The final goals for my site, goals easily tweaked for any site, are as follows.

Well-engaged visitors will:

1. View a relatively large number of page views in a given session
2. Have visited the site in the last four weeks
3. Have relatively long sessions
4. Come directly to my site or come from a “Eric Peterson” branded search
5. Be reading my weblog in addition to non-blog content
6. Buy one or more of my books through my web site

As you can hopefully see, the first item in my original list (view “critical” content) has been softened somewhat. While the act of purchasing is necessitated by viewing critical content (my “thank you” page) ultimately I agreed with several reader comments that the a priori definition of visitor goals would skew the metric and reduce the metric’s ability to tell me about all of the content on my web site. Thanks to Victor and others for hammering this home.

Given all this, the visitor engagement metric is composed of six sub-metrics, each of which can be examined individually to provide context to the larger calculation. The six sub-metrics are:

1. Click-Depth Index: Percent of visitor sessions of “n” or more pages
2. Recency Index: Percent of visitor sessions occurring in the last “small n” weeks
3. Duration Index: Percent of visitor sessions of “n” or more minutes
4. Brand Index: Percent of visitor sessions originating directly or originating from search engine searches for terms like “eric t. peterson” and “web analytics demystified”, etc.
5. Blog Index: Ratio of blog reading sessions to all sessions
6. Conversion Index: In this case, session- or order-based conversion

Keep in mind, engagement is a visitor-based calculation, one designed to look at the lifetime of visitor sessions to the web site. So that the engagement of any visitor is a function of their lifetime of visits. Yeah, this assumes some stability in cookies so always use first-party cookies.

The final calculation is simply a summation of the component indices divided by the total number of components which yields a simple percentage:

If you’re looking across multiple visitors, you would read this as “the average visitor is just under 27 percent engaged, as defined by X, Y, and Z.” If you’re looking at a single visitor you can break engagement down on a session-by-session basis, watching for increases and decreases in the visitor’s engagement over time. In aggregate, visitor engagement becomes a very powerful but elegant key performance indicator that tells you a great deal about the make-up of your audience.

Once you decide that you need more information about the basis for an increase or decrease in visitor engagement, and assuming you have the right technology powering your analysis, you would simply visualize each of the core components over time:

As Clint commented in my last post, there is a surprising stability in each of the components, which is in my mind what you’re looking for. I want to see the variation show up when I examine engagement against my business-critical dimensions (referrer, campaign, page, search term, etc.)

When you analyze the visitor engagement calculation against all of your site visitors, you’re looking for a more-or-less normal distribution. This distribution is spiky because of the calculation, but if you’re able to drill-down, you should see something like this:

(The bars that exceed the visualization’s scale represent peaks that occur as visitors achieve 100% of sessions for 1, 2, 3, 4, and 5 of the engagement calculation’s core components. If you want to see this image at 100% scale let me know …)

Another way I can think about this is to use a scatter-plot, basically showing the same thing but easier to visualize differences as you drill-down into specific dimensions:

All of these calculations actually become relevant when you actually apply them to a dimension of data. Here, for example, is visitor engagement mapped to blog posts from my and Avinash Kaushik’s weblog:

Pretty cool, huh? I mean, it’s no great surprise that Avinash’s 2007 Web Analytics Predictions post has the highest visitor engagement score in this image when you think about all of the follow-up predictions his original post spawned. But boy-howdy, isn’t it nice to see that in a metric that you can understand and actually use?!

Here is the visitor engagement metric applied to some of the referrers to my web site:

No great surprise again that Feedburner, Technorati, and WordPress are driving visitor engagement given that they are likely to be driving visitors maxing out their blog index score. But what about the folks at ROI Revolution, sending me visitors who are on average over 30% engaged, or Blackbeak and the folks at Conversion Chronicles, sending me visitors who are as engaged as my 27 percent site-wide average?

Arrrrrrr, indeed!

Finally, and I know that I showed this already but I just think it’s damn sexy, I can map visitor engagement against any geographic dimension in my system (continent, country, city, state, zip code, DMA, etc.) to see where I might want to focus my local marketing efforts in the future:

You two people in Midland, Michigan, get ready for an onslaught of Analytics Demystified promotions!

Oh, some random notes:

• You can add non-page view events (RIAs, AJAX, Flash, etc.) into the calculation easily. I don’t have much of that on my site but I have an “Event Index” calculation that can be added for sites heavily leveraging these types of applications.
• You can add the “social media index” that I discussed in my last post just as easily as you can add content-based indices for retail, customer support, business-to-business, or content.
• You can take or leave my idea of scoring the brand index against specific search terms. Visual Site gives me a really easy way to do that and I believe that engagement is very much a function of brand awareness, something notoriously difficult to measure in any practical way.
• Visual Sciences customers interested in deploying the visitor engagement metric should contact me directly via normal company channels. I have pretty much everything you need to get up and running with this in a ZIP file and I’d be happy to talk you through the process.

As always, I welcome your comments and feedback on the engagement calculation and anything else that comes to mind. In the next (and perhaps final) installment I will cover Clint’s inevitable complaint of “What the heck happened to all the great ‘social media’ stuff?!?” as well as talk about some specific applications of the engagement metric.

Curse Clint Ivy, curse him for being right some of the time! I mean, of course, Clint’s diatribe about my engagement calculation and it’s lack of social (media) value. In his post, Clint gives me credit for at least trying to work out how we can measure engagement, then proceeds to chop to pieces for forgetting about the everyday blogger in my calculations.

Maybe he wasn’t that mean, but it’s late and I’m cranky … and he makes a good point. In my previous posts, I have been assigning some value in my engagement calculation directly to the viewing of specific content on my web site. But, given my respect for Mr. Ivy, and the fact that others have commented about this, I took out the high- and moderate-value content scoring and have substituted (experimentally) a Social Media Index. After looking at my site, I am now scoring the following “social media” activities one can engage in at Analytics Demystified:

• Reading my weblog
• Reading other user generated content on the web site
• Participating in a truly social activity facilitated by my site
• Joining a social network of web analytics people
• Contributing content directly to the web site
• Submitting a comment to my weblog
• Emailing me directly

Yeah, I know that my site is no Digg.com, nor is it Friendster or YouTube, but hopefully you get the gist. The measurement works pretty much the same, regardless of the volume of traffic. The net effect is to, at least in my mind, remove some of the content-specificity from the calculation while improving the metrics ability to help sites understand visitor attraction to activities designed to draw the visitor in.

This list could just as easily include providing a rating, tagging, Digging, etc. Depending on the technology you use, the measurements don’t even need to be direct. Think of my list as a strawman, one that can be brutally beaten into better shape (but, unlike almost everything else I’ve seen so far, one that actually functions now …)

One thing that Clint and I talked about off-line that wasn’t represented in his post (or maybe it was, it is getting later by the minute) was whether the engagement calculation would provide any additional value, relative to “corporate” measurements like conversion rate. I took a look at that, mapping my buyer conversion rate and engagement against the visitor’s session number. I got this:

While it clearly looks like if I don’t get ’em to buy one of my books pretty quickly after they first come to the site my opportunity to convert goes down pretty fast, the opposite is true for engagement. It actually appears that, at least on my site, there is a sweet spot for visitor engagement between about 40 and 50 sessions … heck, I even sold a few books to folks well after their initial visit once their engagement ran up to over 55 percent!

The nice thing about the engagement metric is that it helps resolve the problem that Gary describes in his recent post on visitor classification. Gary, in talking about the need to capture and visualize both absolute and relative content usage on a site says this:

The problem is that heavily engaged users of your site will show up (and often drive the statistics for) virtually every area of your site. For publishing clients, a small segment of heavily engaged users inevitably show up in every single content area. And the smaller the overall usage of that area, the more the heavily engaged component influences the results.

Yep, so wouldn’t it be nice if you could not only create on-the-fly visitor segments that are inclusive of any different number of content areas and pages on your site plus easily determine how much of an influence highly engaged visitors are on your absolute content usage measurements? If you could do that, it would probably look something like this:

I know it’s hard to see, but I simply dragged a bunch of pages, groups of pages, and content groups onto the page visualization map and told Visual Site to color the nodes by visitor engagement (the height of the bars represents the relative number of sessions to each node.) I could then select-in or select-out visitors based on their relative level of engagement to identify the special kinds of customers Gary refers to.

Anyway, I’m going to have beers with the good Mr. Ivy next week and I didn’t want that whole “social media” thing hanging over my head. And while I recognize that this metric (which I still have yet to share the calculation) doesn’t capture fully the elaborate needs of the really smart folks working to pound out Social Media Measurement, I heartily agree with Clint’s friend Jeremiah Owyang when he says that “Social Media is about people. People connecting to other people to build better relationships, fostering communities and increasing collective knowledge” and “Measurement and Metrics are one way to help to tell the story of Social Media.”

Measurement and Metrics, indeed.

Given that my last post on measuring engagement generated a fair amount of feedback, I wanted to follow-up with the post that in retrospect I should have published first, the nuts and bolts behind the engagement calculation.

Since there are numerous definitions of “engagement” that could be applied to the online channel, I choose to use the following definition:

Engagement is an estimate of the degree and depth of visitor interaction on the site against a clearly defined set of goals.

My definition sounds like conversion rate except engagement is a more flexible concept, one that can accommodate a variety of business needs such as those described by Bill Gassman in his comments to my last post and those of Craig Danuloff who is looking for a metric that accommodates a variety of visitor activities.

Based on my knowledge of my site visitors and their long-term usage patterns, my engagement goals are as follows:

1. I would like that visitors would view and interact with certain content on my site;
2. I would like visitors to subscribe to this weblog to stay connected;
3. I would like visitors to maintain a low recency with my content, regardless of whether they’re reading blog posts or viewing pages on my site;
4. When visitors are on my web site, I would like them to spend a reasonable amount of time interacting with my content;
5. When visitors return to my site, I prefer they remember my domain name and return to my site directly, either via a bookmark or by directly entering my URL into their browser.

Now, many of you will likely argue with these criteria, and fairly so. It’s fine that you may have a different definition of engagement for your site; I think that Bill put it best when he commented:

“Each organization’s version of engagement will be unique. It will be derived from a number of root metrics, probably under a dozen. Common root metrics will be frequency, recency, length of visit, purchases and lifetime value. Some organizations may include visitor actions, such as subscribing, providing personal information, writing a comment, or participating in a blog.”

I’m using the criteria I listed above based on my knowledge of my site visitors, mined from a variety of channels including site activity, email, comments, personal conversations, etc., juxtaposed against my site’s business objectives (see below.) Given a sufficiently flexible analytics package you can build your engagement metric using any goals you like …

Regarding item #1 in the list above, wanting visitors to interact with certain content on my site, here are the activities I am tracking broken down by moderate- and high-value:

Moderate-Value Activities

• Read my weblog
• Read about the Web Analytics Business Process
• Research web analytics jobs
• Add a link to my link database
• Read comments about my books
• Give me an email address
• Host a Web Analytics Wednesday
• Join the Web 2.0 Measurement Working Group

High-Value Activities

• Consider buying one or more of my books
• Buy one or more of my books
• Read about any of my books
• Read about my Key Performance Indicator Worksheets
• Download a sample copy of one of my books
• Email me directly
• Submit a comment to my weblog
• Go to Amazon.com to check out my books

Because it is very difficult to know a visitor’s intent when they visit a web site, these activities are designed to allow me to examine the visitor not in the context of their intent but rather in the context of my site’s specific objectives. I maintain Analytics Demystified for three primary reasons:

1. To sell my books
2. To maintain my visibility in the web analytics field
3. To have a channel through which I can continue to contribute ideas to our community

You may argue that tracking a visitor’s interaction with specific contents is a poor measure of engagement given that visitors may be looking at an entirely different set of content and are intensely engaged … fair enough. But these lists represent the activities that visitors can perform on my web site that are in-line with my stated business objectives.

If highly-engaged visitors are interacting with some other content on my site, that would prompt me to reconsider that contents contribution to my engagement calculation and perhaps add it to one of the lists above. My belief is that any engagement estimate must take content consumption into account given that it is the content that drives visitor engagement in the first place.

This post is getting long so it’s clear I’ll need a “Part III” (and maybe a “Part IV”) but here is something tangible to chew on until I have time to post again. Based on my five business goals stated above, my engagement calculation is essentially this:

(Pct High-Value Content Consumption Sessions + Pct Moderate-Value Content Consumption Sessions + Blog Subscriber Reads per Session + Pct Recent Sessions + Pct “Long” Sessions + Pct Direct Sessions) / 6

I am calculating the percentage of sessions on a per-visitor basis and summing those percentages to generate an “engagement score” between 0.0 and 6.0. I convert this score to a percentage itself to make it easier to read and voila! I can apply my engagement metric to any dimension I am tracking in Visual Site.

By clearly defining my engagement goals and then systematically scoring visitors against that framework, I can build a metric that can be objectively applied regardless of whether visitors buy a book. I can apply my engagement estimate to any dimension I am tracking on my site, allowing me to discover patterns of visitor behavior that would not be obvious based on more traditional metrics such as conversion rate, session duration, or page view count.

Just so I don’t lose you, here is one of the visualizations I am using to better understand visitor engagement showing visitor engagement by percent of visitors by visitor city:

It’s hard to see with the scale I’m providing in this image but I can assure you that the long-tail is there. And sure, with my $50 book I’m unlikely to launch a geo-targeted marketing campaign in markets where visitors are, on average, twice as engaged as my site-wide population … but maybe you would!

Until next time, I welcome your comments and criticism.

My good friend Clint Ivy and I were talking awhile back and he asked me, “So what do you think about Scoble’s call for an engagement metric?” I said, “Huh?” since I had long since stopped reading Robert Scoble, but apparently he had rubbed Clint the wrong way.

Anyway, I had been working on a project for a customer and we had been talking about how to measure engagement on their web site. We’d gone round-and-round on ideas about what constitutes an “engaged” visitor and narrowed it down to a few key areas:

1. The visitor views “critical” content on the web site
2. The visitor has returned to the web site recently
3. The visitor returns directly to the web site some of the time
4. Some high percentage of the visitor’s sessions are “long” sessions
5. If available, the visitor is subscribed to at least one available site feed

So, with this in mind, visitors that are consuming content slowly and methodically and returning directly to the site are well-engaged. Visitors who have also subscribed to some type of “push” feed are more engaged, and even more so if they’ve returned to the site recently.

Sounds reasonable, doesn’t it?

Using this model, sites like Yahoo! and Digg will have very engaged visitors, whereas sites like mine will have slightly less engaged visitors. That also sounds reasonable, given that Yahoo! and Digg are social networks and Analytics Demystified is more or less a weblog, a geek hub, and a job board (in that order).

It turns out that my audience is, on the whole, 32.3 percent engaged.

Perhaps more importantly, visitors that I get from the following sources are engaged at the following rates:

You can see there that my friend Avinash is sending me pretty good folks but Avinash’s people are slightly less engaged with my site than the “average” visitor. That and they hopefully already have my books because pretty much none of them are buying Analytics Demystified or The Big Book of Key Performance Indicators from my site! They’re not even taking advantage of the great combo-offer I have on both books!

Perhaps most interesting and wonderful is that my engagement metric allows me to build wonderful visualizations like this scatter-plot to compare the volume of referred visitors to engagement in a way that more easy on the eyes than your basic table or line graph.

Most importantly, because I am running the industry’s most easy-to-use yet powerful web analytics application supporting multi-source and multi-channel data analysis, I can vet my engagement index against real people who have come or are coming to my web site!

Cool, huh?

So Bill Gassman from the Gartner Group is among the most engaged visitors I have (I am quite honored, Bill!) Bill is consuming the content I deem most important to creating a relationship with my visitors, he is subscribed to my weblog, he keeps coming back, his sessions are of reasonable length, and he comes directly to my site or feed over 2/3rds of the time.

Bill is nearly 54 percent engaged with my site, approaching twice the average!

Compare Bill to Frank Faubert from Sane Solutions. Frank is seeing all of the content I believe to be most important and he also is well retained. However, Frank is not subscribed to my RSS feed so most of the time he is getting to my content indirectly then only spending a short period of time reading that content. Moreover, Frank hasn’t been to my site in the last 90 days.

Frank is only 21 percent engaged with my site so I guess maybe he doesn’t like me now that I’m not an objective, third-party anymore.

I’m interested in your thoughts about my engagement metric. Do you think I’m using the right inputs? Or am I missing something critical to how this metric should be calculated? I’d love you input since I know that the folks reading my weblog are among the brightest in the web analytics industry …

Next Time: Being a big fan of “showing my work”, I’ll provide the calculations behind my engagement metric so that you can calculate your site’s engagement in the safety of your own home. My hope is that through your comments and criticism I’ll be able to refine this metric down to something that any vendor can implement and any practitioner can use.