Back in December of last year when I first posted on measuring visitor engagement, I hardly imagined how much interest the topic would generate. Shortly after the first post, I commented that my definition of engagement was as follows:

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

I then went and wrote over a dozen posts, publishing feedback from some incredibly bright people and demonstrating the utility of a well-defined measure for engagement. Since that time, however, some have questioned the value of such a metric and thusly prompted me to update and publish the following calculation for visitor engagement:

I presented this calculation to a completely full room last week at Emetrics but wanted to provide an update to all my patient readers who were not able to make the event. You can download my entire Emetrics on “Web Analytics 2.0″ which includes the slides on measuring visitor engagement from the White Papers and Presentations section of my site.

I very much believe that engagement is a metric, not an excuse, and that the metric described in this post provides a powerful measurement framework for sites looking for new ways to examine and evaluate visitor interaction. I know that for my own site, the use of simple measures like “bounce rate”, “conversion rate” and “average time spent” is simply insufficient for selling anything other than my books. But I’m now in the business of selling consulting, a complex and sometimes time-consuming sale, and so I’m always on the hunt for any web analytics measure that will give me an edge and help identify truly qualified opportunities.

I believe this metric is exactly that.

This post is an extension of the work I did in late 2006 and early 2007 and was written to clarify my position, update my thinking in the context of “Web Analytics 2.0″, and reiterate my desire to have an open and honest conversation with my peers and other interested parties regarding the measurement of visitor engagement. Web analytics is hard but not impossible; the same is true regarding the calculation and use of robust measures of visitor behavior.

I believe the visitor engagement measurement to be perhaps the most important of all “Web Analytics 2.0″ measurements. Given that this model fully supports both quantitative and qualitative data, and given that the model is build as much around the measurement of “events” as much as page views, sessions, and visitors, I (perhaps haughtily) believe this calculation to be prototypical of the types of measurements we will see as we continue to explore the boundaries of “Web Analytics 2.0″ (download my presentation from SEMphonic X Change).

The Web Analytics Demystified Visitor Engagement Calculation

The latest version of my visitor engagement metric, with notes about its calculation and use, are as follows. If you’re too busy to read this entire post but would like to learn more about this measure, please write me directly and we can set up a time to discuss it.

This is a model, not an absolute calculation for all sites. I agree with other analysts and bloggers who insightfully say that there is no single calculation of engagement useful for all sites, but I do believe my model is robust and useful with only slight modification across a wide range of sites. The modification comes in the thresholds for individual indices, the qualitative component, and the measured events (see below); otherwise I believe that any site capable of making this calculation can do so without having to rethink the entire model.

The calculation needs to be made over the lifetime of visitor sessions to the site and also accommodate different time spans. This means that to calculate “percent of sessions having more than 5 page views” you need to examine all of the visitor’s sessions during the time-frame under examination and determine which had more than five page views. If the calculation is unbounded by time, you would examine all of the visitor’s sessions in the available dataset; if the calculation was bounded by the last 90 days, you would only examine sessions during the past 90 days.

The individual session-based indices are defined as follows (and these are slightly updated from past posts on the subject):

• Click-Depth Index (Ci) is the percent of sessions having more than “n” page views divided by all sessions.
• Recency Index (Ri) is the percent of sessions having more than “n” page views that occurred in the past “n” weeks divided by all sessions. The Recency Index captures recent sessions that were also deep enough to be measured in the Click-Depth Index.
• Duration Index (Di) is the percent of sessions longer than “n” minutes divided by all sessions.
• Brand Index (Bi) is the percent of sessions that either begin directly (i.e., have no referring URL) or are initiated by an external search for a “branded” term divided by all sessions (see additional explanation below)
• Feedback Index (Fi) is the percent of sessions where the visitor gave direct feedback via a Voice of Customer technology like ForeSee Results or OpinionLab divided by all sessions (see additional explanation below)
• Interaction Index (Ii) is the percent of sessions where the visitor completed one of any specific, tracked events divided by all sessions (see additional explanation below)

In addition to the session-based indices, I have added two small, binary weighting factors based on visitor behavior:

• Loyalty Index (Li) is scored as “1″ if the visitor has come to the site more than “n” times during the time-frame under examination (and otherwise scored “0″)
• Subscription Index (Si) is scored as “1″ if the visitor is a known content subscriber (i.e., subscribed to my blog) during the time-frame under examination (and otherwise scored “0″)

You take the value of each of the component indices, sum them, and then divide by “8″ (the total number of indices in my model) to get a very clean value between “0″ and “1″ that is easily converted to a percentage. Given sufficient robust technology, you can then segment against the calculated value, build super-useful KPIs like “percent highly-engaged visitors” and add the engagement metric to the reports you’re already running.

The Visitor Engagement Calculation in Detail

The Click-Depth, Recency, and Duration indices are all pretty straight forward and are more-or-less the traditional indicators that most people (incorrectly) call “measures of engagement”. Each of these are very important to the overall calculation, but none of these alone are sufficiently robust to describe “engaged” visitors. I set the “n” values for my site’s calculation based on the average value for each and this seems to work pretty well (meaning my Ci looks for sessions more than “5 page views” in depth, my Ri looks for sessions more than “5 page views” that occurred in the “past three weeks” and my Di is looking for sessions longer than about “5 minutes” in length.)

Brand Index is a little more complicated. Here I have made a list of all the terms I believe to be “branded” for my site and business, terms like eric t. peterson, web analytics demystified, web site measurement hacks, web analytics wednesday, and the big book of key performance indicators. Whenever a session begins either with no referring domain or comes from a search engine with one of these terms attached, I count this as a “branded session” and score appropriately. While this index perhaps unfairly weights towards search engines, I firmly believe that if you’re starting your session with either my branded URL, my name, or the name of one of my books that you are already engaged.

Feedback Index is the sole qualitative input to this model but it can easily be expanded if necessary. Here I am simply scoring sessions based on whether visitors are providing qualitative feedback via the OpinionLab “O” present throughout my web site or writing me directly by clicking a “mailto:” link. I’m not looking at whether the feedback is positive or negative, only whether feedback was given, operating under the belief that anyone willing to provide direct feedback is engaged.

The Feedback Index could easily be expanded by scoring based on the answer to direct questions posed to the visitor, questions like “do you find the content on this site valuable?”, “do you plan on calling Web Analytics Demystified about consulting?” and “would you described yourself as engaged with this site?” Given a sufficiently robust mechanism for making the calculation, the Feedback Index can provide a tremendously powerful input to the visitor engagement model.

The Interaction Index captures sessions in which specific “engaged events” occur other than the site’s primary conversion event — events like downloading a white paper, providing an email address, requesting a presentation or PDF, commenting on a blog post, Digging a post, emailing content to a friend, printing a page, etc. The Interaction Index is designed to capture a small weighting from those measurable goals on your site you believe to be indicative of engagement.

The Interaction Index specifically does not examine commerce transactions and other conversion events of fundamental import to the site. While I have debated this in the past, here is the rationale for recommending the exclusion of primary conversion events:

1. These events already have their own key performance indicator: conversion. Given that conversion is likely already defined for most transactional sites and tracked in great detail, adding conversion to the visitor engagement calculation is superfluous in my opinion.
2. The visitor engagement metric is designed to provide information about the large number of visitors who do not convert. Given relatively low conversion rates online, having visitor engagement be decoupled from conversion provides a cleaner measure for use in exploring non-purchaser behavior, including looking for independent correlation between the two measures.
3. By excluding conversion, the two metrics can be used side-by-side to look for visitor behaviors may not be obvious otherwise. Given the lifetime of possible visitor behaviors, having a way to look for well-engaged visitors who have not completed a transaction online or have completed a transaction outside of the available data set provides a critical view not otherwise readily attained.

The Loyalty Index is a reflection of my belief that repeat visitation behavior is perhaps the best measure of engagement available. Based on the distribution of visitor loyalty data at Web Analytics Demystified, I score “1″ when visitors have come to the site more than five times in the past 12 months.

The Subscription Index is a reflection that truly engaged visitors are able to self-identify by subscribing to our blogs or newsletters; if you have taken the time to subscribe to one of the Web Analytics Demystified blogs I believe you to be engaged. If your site does not have some type of XML-based content subscription you can either drop this index or (perhaps better) look for an opportunity to develop a subscription service, thusly giving your visitors another good engagement point.

How Does This All Work in Practice?

Careful readers will likely have already figured out that as visitors come to your site over time, their cumulative “lifetime engagement score” changes as they satisfy the criteria of each individual index. So someone coming from a Google search for “web analytics demystified” who looks at 10 pages over the course of 7 minutes, downloads a white paper and then returns to my site the next day will have a higher visitor engagement value than someone coming from a blog post who looks at 2 pages and leaves 2 minutes later, never to return.

If you think about it for just a bit, and consider the components in the full calculation, the visitor engagement metric starts to make an awful lot of sense. Consider the following:

• A visitor can quickly move through a lot of pages, getting exactly what they need, and still be scored usefully through the Click-Depth Index
• A visitor can slowly and methodically read a few pages and be scored usefully through the Duration Index
• A visitor can come to the site frequently and do little more than read a single page of content and be usefully scored through the Recency and Loyalty Indices
• A visitor can come to the site once, subscribe to the blog, return later and download a presentation, and be usefully scored through the Subscription and Interaction Indices
• A visitor can come to the site, click on dozens of pages but fail to find what they are looking for, then tell me so using my feedback mechanisms and be usefully scored through the Click-Depth and Feedback Indices

The power of the metric is appreciated when you apply it to the commonly measured dimensions found in web analytics: referring domain/URL, search engine/phrase, campaign/placement/creative, content group and page, browser/operating system, etc. Suddenly instead of looking at simple measures, you’re examining the potential of visitors coming from or going to each element in the dimension. To see the metric in action, I encourage you to read my post on the gradual building of context, at least until I’m able to publish new screenshots later this week.

Some Parting Thoughts about Measuring Visitor Engagement

Some folks have complained that this metric is “not immediately useful”, that nobody will understand it, and that it is impossible to calculate. Perhaps, but I would argue that A) no metric is truly immediately useful and B) most people don’t understand web analytics because web analytics is hard. The assumption that a diverse organization is going to be more successful using “bounce rate” because it can be glibly explained by saying “your content sucks” is just wrong — all of this stuff needs to be explained regardless of the complexity of the metrics involved.

Regarding the metric being impossible to calculate, it fully depends on which application you’re using. If you’re trying to get by using free tools then yes, you’re out of luck. But if you’re using robust tools like the high-end offerings from Unica, IndexTools, Visual Sciences, and WebTrends then you should have little trouble using the metric I describe in this post.

I personally believe that Web Analytics 2.0 both requires and allows us to be more creative and thoughtful in our use of metrics. Why not use a robust indicator if one is warranted? Especially if you’re not selling anything online, or if you’re selling high-consideration items, my visitor engagement metric can be shown to be an extremely powerful measurement.

Given the assertion that some consultants are apparently charging $200,000 USD for complex “engagement index” work, and given that someone working for Google is in the process of trying to patent a much simpler version of this equation, I am happy to give my work away to the entire industry in an effort to promote the use of more meaningful metrics to be brought to bear on increasingly complex measurement problems.

What do you think? Did you see my Emetrics presentation and still have questions? Did you read every word of my series on engagement and still not believe me? Do you need to see engagement in action before you’re willing to say it’s not just an excuse? Or are you chomping at the bit to have a robust measure like this for use on your own site?

Especially on this subject I relish your feedback, either via comments or via email — your choice! I find the subject fascinating and welcome the opportunity to discuss it you, my (hopefully) engaged readers.

Blogger Avinash Kaushik kicked off a little debate in the blogosphere a few weeks when he declared:

“Engagement is not a metric that anyone understands and even when used it rarely drives the action / improvement on the website.

Why?

Because it is not really a metric, it is an excuse.”

Suffice to say, some pretty bright folks disagreed with Avinash, openly and vocally. Anil Jasra has a good summary of a panel from WebTrends Engage where Gary Angel, Andy Beal, Manoj Jasra, Jim Novo and Jim Sterne all apparently voiced their opinion that engagement is a metric, not an excuse.

Perhaps ironically, in an interview with Eric Enge from February of this year, Enge asked Kaushilk about my long series of posts on measuring engagement (emphasis mine)

Eric Enge: Another thing I read about recently was Eric Peterson’s notion of an engagement metric. Can you comment on that?

Avinash Kaushik: Sure. You know that Eric is obviously a leader in the industry. We are all following the trail that Eric has blazed. He is just an awesome guy and a really great thinker. And, in terms of the specific post that you are referring for engagement, I think Eric’s initial proposal for the methodology is a very good one, and it does extend the conversation in terms of what it is possible for us to measure, because Eric obviously has access to some pretty good tools that allow for deeper analysis. But my preference is to ask a random sampling of people, or every single person who comes to website, are you engaged, here is my definition of engagement, do you like this site or product, are you going to recommend it, or whatever is the case.

Now, to be fair, I agree with part of Avinash’s argument — qualitative data is a valuable input into measuring visitor engagement — I just don’t think qualitative data is the only input. Nor do I think that it is “nearly impossible to define engagement”. For over a year I have been calculating visitor engagement on my site using the following equation:

Looks complicated, huh? It is. But if you’re running a site like mine where the major outcome you’re trying to create is simply not measurable online, wouldn’t you like to have some reasonable proxy that would help you identify where your best leads are coming from, what those leads are looking at, and who your highest quality leads actually are?!

I know I do.

Obviously the equation above doesn’t tell you very much. If you want to hear the rest of the story, you have two options:

1. Come to my Web Analytics 2.0 presentation next Wednesday at 1:30 PM in the Blue Ballroom at Emetrics
2. Wait until next Thursday and download my updated Web Analytics 2.0 presentation from my web site

Ironically this little debate prompted me to stick the long-awaited explanation of how to measure and use visitor engagement into my Web Analytics 2.0 presentation. Thanks to Avinash for kicking off a nice (if a bit lopsided) debate!

See you in Washington!

I wanted to say congratulations to Jason Burby, Angie Brown, and everyone on the Web Analytics Association’s Standards Committee for publishing their standards document last week. Given the number of web analytics terms they defined (26) and the somewhat slow process the Association has for getting documents approved, this effort is a huge milestone for the organization, one that Jason and Angie deserve great praise for indeed!

If you haven’t already downloaded and read the definitions, check them out here (PDF download).

While the PDF document says that the final product is “Web Analytics Definitions - Version 4.0″ this is clearly a “Web Analytics 1.0″ document. The committee relegated all of the really wonderful Web 2.0 stuff like AJAX, RSS, XML, and the such to the same confusing obscurity they exist in today with the comment “certain technologies including (but not limited to) Flash, AJAX, media files, downloads, documents, and PDFs do not follow the typical page paradigm but may be definable as pages in specific tools.”

Given the last year’s push towards measuring Web 2.0 the right way and some great, insightful work from folks like Ian Houston and Judah Phillips it is kind of a shame that this document doesn’t address event-based measurement architecture more directly. The group does define “event” but only does so under the header of “Conversion Metrics” stating that an event is “any logged or recorded action that has a specific date and time assigned to it by either the browser or server.

Sounds like the definition of a Web 2.0 event to me, but I’m not sure why this is relegated to conversion metrics.

Regardless, this is great and valuable and useful work on the part of these hard-working volunteers. But the definition of standards raises one particularly important question: Given the definition of standards, what the hell do web analytics practitioners do with them?

The Fundamental Problem

The fundamental problem with these definitions (and any standard definitions IMHO) is that without an enforcement mechanism they are unlikely to provide any real benefit to the folks in the trenches. As long as smart folks like Eric Enge at Stone Temple Consulting continue to uncover as much as a 154% difference in the measured number of visitors and a 161% difference in the measured number of page views between concurrently deployed solutions, the average web analytics end user should not be comforted by the existence of standards.

Put another way, it is not the definition of standards that makes a difference, it is the adherence to standards by technology vendors that will provide the portability of skills, knowledge, and solutions so desired by many in our industry. Jason Burby sagely points this out in his Clickz article on his volunteer work when he says:

“Companies often switch metrics tools and subsequently change the terms they use to discuss analytics. One tool will call something one name, while another tool calls it by a different name or applies different meanings to a very similar name. When people switch tools and bring data with them, they don’t get an apples-to-apples comparisons. As a result, companies lose the important year-over-year view.

Though the new standards won’t instantly take care of that issue, they provide a step in the right direction.”

The Barrier to the Adoption of Standards

The problem as I see it is this: For many web analytics vendors, the way they calculate some of the critical metrics in web analytics is the “secret sauce” in their solution. Consider the WAA’s definition of unique visitors which states that unique visitors are:

“The number of inferred individual people (filtered for spiders and robots), with a designated reporting timeframe, with activity consisting of one or more visits to a site. Each individual is counted only once in the unique visitor measure for the reporting period.”

This is perfectly reasonable, but the definition goes on to say that “a unique visitor count is always associated with a time period (most often a day, week, or month), and it is a non-additive metric.”

Do you wonder what the folks at Visual Sciences who have spent millions to perfect their “data wheels” technology that effectively removes the “time period” requirement would say to this? One of the major value propositions at Visual Sciences (at least during my brief tenure) was that time was irrelevant — if you wanted the number of unique visitors for the football season, you dragged your mouse across the calendar; if you wanted the number of unique visitors for a few hours during the day, you dragged your mouse; if you wanted the number of unique visitors to your site since recording began, you dragged your mouse.

You can make the case that this example more or less removes the time dependence associated with the WAA definition. But should all the vendors who don’t have this capability (anywhere you are forced to use metrics like “Daily Unique Visitors”) spend the R&D money necessary to eliminate the dependence on time? Or should Visual back this functionality out of their application?

When you start to think about these kinds of things, much less issues associated with data sampling and data roll-off that occurs for a litany of reasons, you can start to understand why I made this somewhat snide comment in a MediaShift article awhile back:

“A friend of mine described it as the most beautiful fantasy…but it would never happen,” consultant Peterson said. “Omniture has a $1 billion market cap, and I don’t see Omniture tearing apart their technology to calculate unique visitors and page views differently because all their competitors have decided there’s a different way to do it. It’s hard to imagine. Not impossible. Fantasies sometimes come true.”

Ironically the cost isn’t the main problem: The impact on existing customers who would be forced to learn new definitions and suffer from potentially dramatic changes in data collection and reporting is the main problem. Do you want to be the person who has to tell a Fortune 500 customer that because you’re adopting more standard definitions that their page view count will suddenly drop by 35% month-over-month?

I had to do that once. Trust me here, it wasn’t a fun conversation to have.

An Idea in the Absence of a Solution

Given that I think that the WAA has produced some incredibly valuable work, despite some potential barriers to the work’s adoption, I do have an idea that I would love to see the Association follow-up on, one that would add a tremendous amount of value to this already great work.

I would love to see the Standards Committee create a matrix of standards compliance for each of the vendors in the marketplace today. Basically a checklist that details on a term-by-term basis which vendors are currently using the WAA definitions that would let companies looking for a solution to include that criteria in their assessment. Something that would let everyone quickly determine:

1. How standards compliant a given solution is (and which solution today is “most compliant”)
2. Which standard definitions are calculated out-of-box in each solution (for example, “Original Referrer” and “Bounce Rate”)
3. Which currently available solutions dramatically differ from the norm in their use of standard terms

Something like this would probably have to be backed up with some documentation or examples as proof points, just for reference. And yeah, this is kind of a lot of work, but if you think about it all you really need is for one WAA member per solution to poke around in their documentation and then someone (Jason and Angie maybe) to collate the results and write it up. I would be happy to contribute the matrix assessment for the web analytics solution I’m using now if that would up!

Who knows, maybe we’d discover that all the vendors are already standards compliant and there really isn’t a problem with definitions!

What Do You Think?
I’d love to hear what all of you think about the new standards and my concerns about how they’ll be used (or not used.) Am I missing something? Were you disappointed to not see something that spoke more clearly to your concerns about Web 2.0 technology? Or are you just pleased that the WAA published these definitions and see them as a small-but-important first step?

In all of the fuss about NetRatings dropping page views as a metric used to calculate site popularity is the fact that the company actually did a pretty smart thing: they took my advice from February 15th of this year and rolled in a very valuable and useful “sessions” metric. Well, maybe it wasn’t my advice they took, but I think it was a great idea either way to drop page views since they’ve become increasingly inconsistent to instead focus on the one metric that is consistently applied and well defined, sessions.

Unfortunately NetRatings chose to focus their announcement on “total minutes” saying that time was a better measure of engagement. Personally I’ve never been a very big fan of the time spent metrics — I guess I’ve just looked too long and too hard at all the problems associated with how time is collected and recorded in the web analytics realm.

There is a really engaged thread at the Web Analytics Forum at Yahoo! Groups on this subject that is definitely worth a read if you’re interested.

And I’ll admit, I don’t have all the details associated with how panel-based services like Neilsen and comScore track time spent. If they’re actively tracking the user and only counting time when the browser window is active and the mouse is moving, well that would be a good use of the panel. My suspicion is that, like in web analytics, they’re simply recording the delta between the first and last request for a page in the domain — a strategy that suffers from a litany of well-described problems.

The two I see as most problematic are:

• Single page visits are either difficult to count or not counted in time spent calculations
• The amount of time a web page is open is likely only poorly correlated to their actual engagement with the page

Some have already noted that the fact that very popular sites like Google will do poorly in time spent on site because one of the dominant use cases involves only a single page (I search and I go.) Conversely, depending on how time spent on site is calculated, the search engines may have inordinately long times spent based on a search leading to a long browse time on a discovered site, leading back to the search results (same session, clock is presumably still ticking), leading to the next discovered site, etc.

I for one use iGoogle in exactly this way: I load the page frequently throughout the day and do nothing more than look at a single page view. In fact, unless Nielsen is either tracking the AJAX-interaction with the iGoogle interface, or counting single page view sessions, it is likely that my interaction with iGoogle is not counted at all. But let me assure you, I am quite engaged with the content in my Google portal (something that would be well evidenced by the total session count I generate at the site each day.)

As I looked back through the plethora of comments that my original post on using sessions to compare sites I noticed that I had made this statement in response to a comment from Jacques Warren:

• If you want to compare two or more web sites, use sessions because of the reasons I outlined in my original post.
• If you’re interested in the number of people coming to one web site (presumably yours), use de-duplicated unique visitors but be mindful of cookie deletion.
• If you’re interested in the activity of people on your web site, and if you have a “Web 1.0″ web site, use page views but be mindful of issues like code coverage, proxies, robots, etc.
• If you’re interested in the activity of people on your web site, and if you have a “Web 2.0″ web site built around RIAs, etc., use some form of event model.

I’ll stand by this. Until I know more about how N/NR and comScore calculate their time spent on site metrics it’s hard to believe their numbers to be any more useful or accurate than those provided by direct measurement systems. That said, I’d welcome a briefing on the subject from either company if they’re reading this and are interested in having me pick apart their methodology spending some time with me.

If companies really need to use time spent on site, they should consider using better key performance indicators for time such as Percent Low/Medium/High Time Spent on Site categories (something I talk about at length in The Big Book of Key Performance Indicators.)  That way N/NR could report on the percent of all tracked sessions that were “30 seconds or less”, “31 seconds to 5 minutes”, and “More than 5 minutes” (as an example) which would give us a more powerful view into the relationship between visitors and the time they spend on site.
At the end of the day I like that N/NR has provided a consistent and easily compared metric to their customers in “total sessions” which is what I will inevitably focus on as a measure of site popularity. Having devoted quite a bit of time to describing what I believe to be a solid measure of visitor engagement, it’s difficult for me to think about “time spent on site” (or even “total sessions”) as a good proxy. Time spent, recency, depth of session, session number, etc. are all components of engagement, not direct measures.

What do you think? Is Nielsen right and I’m crazy? Have you been looking closely at your time spent on site metric for years and are delighted that the rest of the world has finally caught up? Or are you like me and spend far too much time browsing from site to site, flipping from task to task, and thusly confounding clocks and counters on every site you visit?

I welcome your comments.

Today I am happy to announce Web Analytics Demystified’s third business partnership and our relationship with Josh Manion’s firm Stratigent.  I’ve known Josh for years and have always had a tremendous respect for the work he’s done and the firm he has built from the ground up.  Stratigent has a proven history of successful execution in long-term and tactical web analytics engagements, as well as a methodical approach to the vendor selection process (a service I have opted specifically to not provide through Web Analytics Demystified.)

You can read about our business partnership in the press release and I’m happy to take any questions directly via email.

Eric T. Peterson delivering the keynote at SEMphonic XChange conference
Also recently announced was SEMphonic’s XChange Conference where I will be delivering the keynote presentation and a class on key performance indicators.  I’m very excited about this conference and was thrilled when Gary asked me to deliver the keynote given that several other great speakers will be at the event including Gary, Paul Bruemmer, Jacques Warren, and Manoj Jasra.

You can learn more about the SEMphonic XChange Conference at SEMphonic’s web site.

Response to last week’s research announcement
The research that Web Analytics Demystified and the Web Analytics Association put out last week was very well received, having been written up in Newsfactor, BtoB, Daily Research News, ClickZ, MarketingVOX, E-consultancy, Online Media Daily, and DMNews.  You can follow all references to my company, our work and our research on this sites Articles and Interviews page.

If you haven’t yet seen the research, you can download the PDF from our web site.

Eric T. Peterson is writing for DM News!

I had been waiting and waiting to make this announcement until my first article was published by when I formed the company the nice folks at DM News asked me to write a regular, monthly column on web analytics.  My first article appeared in the June issue of DM News and is available online at dmnews.com.

I have been a big fan of DM News ever since they published their special report on web analytics in August of 2006. Since that time I have been lucky enough to be a trusted resource on the subject for the publication and look forward to this new relationship.

Presentation on KPIs at BMA Annual Conference, Thursday June 14th

Tomorrow (Thursday June 14th) at 10 AM I will be presenting on key performance indicators at the Business Marketing Association’s annual conference in Las Vegas.  The presentation will be a dramatically shortened version of our workshop on KPIs.  If you’re at the conference and would like to meet please come to the presentation and look for me afterwards.

Special Web Analytics Wednesday on TUESDAY in Boston, next week

Hopefully those of you who live in beautiful Boston, Mass. will be able to join me, my good friend Judah Phillips, my business partner Aquent, and the nice folks from Unica at a very special Web Analytics Wednesday event next Tuesday, June 19th.  I will be testing out a slightly new format for WAW events and giving a short presentation so hopefully that goes well.

If you’re in or near Boston please sign up today to join us for this special event.