How to measure visitor engagement, redux

Published by Eric T. Peterson on October 22, 2007 All posts from Eric T. Peterson

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.

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  • Kenn Gold

    Ok, well you answered the one question I had from the presentation which was how come Di was not a % of Ci like Ri is. But when you went through them at eMetrics you might have missed saying it (or I missed hearing it….heh).

    Sometime in the next week I will have someone on my team see if we can trend this out over the year and see what it looks like.

    I think my primary concern is how to make it actionable. Because it is a combination of so many different factors, if you say are trending steadily along for a while and then your ‘engagement factor’ suddenly drops, you would then have to break it back apart to see what exactly changed, right?

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  • eric

    Kenn: I’d love to hear what you find out and the specifics of how you’re able to make the calculation using your specific analytics application.

    You make the visitor engagement metric actionable the exact same way you make any other metric actionable — by conducting careful analysis and making recommendations. In some instances you may look at the component metrics, but in others you may be able to examine the individual elements of whatever dimension you’re applying the metric to (referring domains or campaigns, for example.)

    In this regard the visitor engagement metric is the same as any other metric (conversion, average page views per session, etc.)

    I look forward to hearing back from you about your results!

  • Katie Paine

    I think this is great for a web site, but it really doesn’t tell you much about the engagement level with your brand, nor does it account for what Forrester calls “intimacy.” As always, I believe it will take a combination of metrics, including some human factor analysis before we get this right. As my father told an audience of ad execs 40 years ago: “If we can put a man in orbit, why can’t we determine the effectiveness of our communications? The reason is simple and perhaps, therefore, a little old-fashioned: people, human beings with a wide range of choice. Unpredictable, cantankerous,capricious, motivated by innumerable conflicting interests, and conflicting desires.”

  • eric

    Katie: Excellent point! I was recently at the SEMphonic X Change (which I highly recommend) listening to Digitas and Avenue A/Razorfish debate their measure of “brand engagement” when I decided what I’m really talking about is an operational measure of micro-engagement and what they were talking about was a multi-channel measure of macro-engagement.

    Thanks for reminding me of that!

    In terms of measuring oddly motivated, cantankerous, conflicted folks … I need to introduce you to my friend Joseph. Joseph is able to measure the impossible and make sense of the senseless. Me? I try and focus on the simple stuff that gets us 80% of the way there.

    Thanks for your comment!

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  • Scott Klein

    Some if not all of these can be captured in real time. How fruitful would it be for a website change itself in real time to maximize outcomes for a user with a given engagement level? That is, is it useful to move a user up the engagement scale by, say, offering them special content — or the opposite, starting to put up reg-walls — once they’ve identified themselves as more engaged than transient search engine traffic?

  • Bud Caddell

    Eric, what do you think about measuring participation inequality in communities? Do you think it’s worthwhile?

    Here’s my math:

  • eric

    Scott: It’s an excellent idea and there is very little that would prevent any vendor having sufficiently robust technology from leveraging my engagement scoring model. I suspect that this is actually not too far off what companies like Touchclarity and Kefta are doing underneath the hood.

    Still, until more complex systems are available, I enthusiastically recommend working backwards from the visitor engagement metric to examine search engine traffic in an attempt to optimize those engines/phrases driving the most “engaged” visitors.

    Bud: Neat idea! I am still working out how to demonstrate the statistics driving the engagement calculation but your system makes sense. In fact, I was just in New York last week working with a client that is trying out the same model! The only issue I have with your model is that in my experience it is hard to score visitors using currently available technology (but perhaps that will change over time.)

    Thanks to you both for your comments!

  • Steve Jackson

    Hi Eric,

    I think this is very comprehensive. It covers all of the bases, you could also use some of the elements you describe to segment audience sessions not just single visitors. In many cases honestly I think it will be too complex for web analytics practitioners to use all the factors in their metrics. On the other hand, I can also think of a lot of companies that could use this.

    Good post.

  • eric

    Steve: Thanks. I was telling a reporter today that I may have inadvertently given folks the impression that this metric is designed to highlight the activity of individual visitors. Sure, you can do that, but the metric is really more useful for traditional marketing and site design dimensions like referring domain, campaign, page, and content group.

    When you say that the metric is too complex do you mean too complex to implement and use or too complex to understand? Either way, hopefully my next post will help change your mind about that (but thanks for voicing your concern and being willing to disagree!)

    I hope all is well in Helsinki and thanks for your comment and for covering the post in your blog.

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  • Larry Freed

    A very interesting idea. I do struggle with a couple of concepts, which hopefully will make for interesting discussion.

    Is visitor engagement a measure of success? Sometimes yes, sometimes no.

    First you need to define. “what is success”. The objective of websites differ from one site to another and from one audience segment to another. Does the visitor engagement calculation translate to success? Some of the elements of the visitor engagement calculation can have a contradictory impact. Higher duration, higher clickdepth and frequency of feedback do not indicate either success or failure, and could indicate frustration and failure by a user.

    So what is the right measurement of success? How about, are your users satisfied and did they accomplish what they wanted to accomplish.

  • eric

    Larry: First, I’m honored to have you comment on my blog. I’m a huge fan of your work!

    Second, I’m not sure that visitor engagement is actually supposed to be a measure of success — isn’t that what “conversion rate” and “satisfaction” are measuring? I don’t think we need to call metrics “engagement” if they already have terms in use, which is exactly why I’ve taken the liberty to provide my own new measure of engagement as a straw-man for discussion, etc.

    You comment that high duration, high click-depth, and frequency of feedback don’t indicate success or failure actually is the perfect example of why my metric is useful. Without analyzing the underlying data more closely it’s impossible to determine if the session is positive or negative. But in my humble opinion a long session with a lot of clicks and a lot of feedback is a strong indicator of engagement, independent of satisfaction or success!

    So what I would suggest is that companies get used to the idea of measuring conversion, satisfaction, AND engagement as independent measures of visitor behavior. And, in the same way I recommend NOT including the primary conversion event in the Feedback Index (Fi), I recommend not including a calculated measure of satisfaction (like the ACSI score) in the Feedback Index. Instead I think more raw inputs that are direct measures of engagement are to be preferred (and would defer to your expertise to suggest what the most appropriate questions would be in a given situation … ;-)

    Anyway, I’m glad I made you struggle with the calculation since it means I’m getting the best and the brightest to really think about what this operational measure of engagement might add to the landscape.

    Thanks again for your comments!

  • Steve Jackson

    Hi Eric,

    When I said complex for many practitioners, I mean that from an implementation perspective. Understanding the idea is probably going to be fine for most people. Not that I’m saying web analytics is easy, but you explained it well. ;o)

    On set-up for instance I can see how it’s a five minute job to set-up a agement with all those factors included in a tool like Visual Sciences. Unfortunately not many of the companies I work with use it.

    On analyzing indivduals, Google Analytics for instance would require a complimentary system that measured individual usage and even then combining the data would be challenging. Omniture is in a similar position, as is CoreMetrics. So it makes it difficult for companies running those tools.

    On analyzing mass audiences as most of the tools do I think it’s a superb set of measures, used individually or in combination as required by each business and as I said very comprehensive.


  • eric

    Steve: Your post is especially interesting in the context of Omniture buying Visual Sciences just now. So OMTR and WebTrends both have the ability to make this kind of calculation … others will inevitably follow, don’t you think?

    Additionally, I have been talking to a number of the vendors and it’s not that this calculation is ** impossible ** for them to make, just that they’re not set up to do it today. Hopefully down the road this kind of metric will be “baked in” to the applications such that thresholds can be set, etc.

    Again, thanks for your comments!

  • Larry Freed

    In your response to my response….Good points. Although, I still think there needs to be some separation between “good” engagement and “bad” engagement.

    William C. Westmoreland, American General who commanded American military operations in the Vietnam War once said, “Militarily, we succeeded in Vietnam. We won every engagement we were involved in out there”. That is my idea of a bad engagement!

    For more on the topic, check out my recent blog post.


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  • Sébastien Brodeur

    How to you conciliate Click-Depth for a 10 pages sites vs a 1000 pages sites?

    Surely a 2 pages Click-Depth on a 10 pages sites versus a 5 pages Click-Depth on a 1000 pages sites is better?

    I think that engagement should be calculate uniquely on each site. Compare yourself with yourself.

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  • Henry

    Hi Eric,

    First time here, and I think your proposal is really interesting.

    Question, do you think values or weighting should be applied to any of your indexes or variables in the equation?

    It seems that certain consumer actions by virtue are more valuable to a brand than others (i.e. x clicks vs x feedback). It very well could be that I’m lost, but it seems that the off-setting mechanism in your equation is a restriction of “n” for each index.

    Why not incorporate an implied value to each action?


  • eric

    Henry: An excellent question! I have toyed a lot with the idea of weighting some/all of the component indices but the problem is this: I don’t really have any a priori way to determine which index deserves which weighting factor. I’d have to conduct more analysis to determine whether “duration” is more important than “click-depth” and would need to compare each component to another goal (perhaps conversion or satisfaction.)

    Not to say this is impossible, I just haven’t done it yet.

    Also, the visitor-based scores (Loyalty Index and Subscription Index) do provide some nominal weighting (albeit small). When you’ve come to the site more than five times, or if you’re a blog subscriber, you get “points” for that.

    It is definitely an area I’m looking into so keep watching this blog for an update. And thanks for writing!

  • Bryan Cristina

    Wow Eric, you definitely are crafting some nice tools for Mathemetrics (my word :D)

    At least someone is trying to tackle some way of figuring it out. It’s one of those nice measurements that never has been truly defined and changes from client to client and even day to day.

  • eric

    Bryan: Thanks for your feedback! It’s great fun to work on the definition and a lot of really bright people are helping out.

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  • Dennis Gorelik


    Could you give an example of recommendations that you made based on Engagement metric?
    So far it’s not clear how to make this metric actionable/useful.

  • eric

    Dennis: You may want to read back through the multitude of posts I have written on this subject, perhaps starting with this one:

    but looking back through the entire thread:

    Suffice to say, the visitor engagement metric is actionable/useful in exactly the same way a metric like bounce rate is. Visitor engagement will differentiate elements in the dimension it is applied to and give you the basis for additional study.

    You know, analysis. Because remember, no metric is immediately actionable or immediately useful. That is a myth.

    Keep watching my blog because I’m about to release a tool that will let you approximate the calculation using Google Analytics (which I see on your site)

    Thanks for your comment!

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  • Dave Manning

    Hi Eric,
    Great article but I found myself seeing the calculation of the Interaction Index differently. You say you’ve debated with including examination of “commerce transactions and other conversion events of fundamental import to the site”, but left it out of the equation as it already has a Key Performance Indicator – conversions. Furthermore, the second reason you give for not including that data into your calculations is “the visitor engagement metric is designed to provide information about the large number of visitors who do not convert”.

    Ok, so here’s my question; what about those who ‘attempt’ to convert thru the primary activity of the site but fail to do so, for whatever reason? Isn’t that a critical piece of information that might be missed by your current formula? I think we would all agree that a consumer is engaged in your site if they are attempting to complete a primary transaction, but if they fail to convert due confusion over the process, too many pageviews necessary etc. etc. – this seems to me to be an unmeasured loss of audience, at least as the calculations are made now.

    I’m not an analytical wiz but that was my initial thought when reading thru your explanation – that ‘attempted’ conversions should also be measured (albeit how is a totally different can of worms).

    Did I miss something?

  • eric

    Dave: I’m not sure you missed anything … I probably didn’t emphasize the idea enough. In a retail environment you would want the Interaction Index (Ii) to include the “Add to Cart” button, the “Checkout Now” button, and maybe even some of the steps in the cart as they are clearly important to a retailer’s notion of “engagement.” But as I said, by leaving the actual transaction out of the Interaction Index keeps that particular bias out of the calculation and better allows the direct comparison of Visitor Engagement and Conversion rates because they’re functionally measuring different aspects of the same thing (visitor tendency to purchase.)

    Does that make sense?

    It is worth noting that Joseph Carrabis disagrees with me on this point, something we’ve documented in our upcoming white paper on the subject. Given that Joseph is ** a lot ** smarter than I am, he’s probably correct and I may be forced to re-examine my assumption here ;-)

    Thanks for your comment!

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Tim Wilson, Partner

I’ll admit it: I’m a Nate Silver fanboy. That fandom is rooted in my political junky-ism and dates back to the first iteration of back in 2008. Since then, Silver joined the New York Times, so migrated to be part of that media behemoth, and, more recently, Silver left the New York Times for ESPN — another media behemoth.

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Demystified's Data Governance Principles
John Lovett, Senior Partner

In digital analytics, "Governance" is a term that is used casually to mean many different things. In our experience at Web Analytics Demystified, every organization inherently recognizes that governance is an important component of their data strategy, yet every company has a different interpretation of what it means to govern their data. In an effort to dispel the misconceptions surrounding what it means to truly steward digital data, Web Analytics Demystified has developed seven data governance principles that all organizations collecting and using digital data should adhere to.

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Three Foundational Tips to Successfully Recruit in Analytics
Michele Kiss, Partner

Hiring in the competitive analytics industry is no easy feat. In most organizations, it can be hard enough to get headcount – let alone actually find the right person! These three foundational tips are drawn from successful hiring processes in a variety of verticals and organizations.

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Slack Demystified
Adam Greco, Senior Partner

Those of you who follow my blog have come to know that when I learn a product (like Adobe SiteCatalyst), I really get to know it and evangelize it. Back in the 90′s I learned the Lotus Notes enterprise collaboration software and soon became one of the most proficient Lotus Notes developers in the world, building most of Arthur Andersen’s global internal Lotus Notes apps. In the 2000′s, I came across Omniture SiteCatalyst, and after a while had published hundreds of blog posts on Omniture’s (Adobe’s) website and my own and eventually a book! One of my favorite pastimes is finding creative ways to apply a technology to solve everyday problems or to make life easier.

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Profile Website Visitors via Campaign Codes and More
Adam Greco, Senior Partner

One of the things customers ask me about is the ability to profile website visitors. Unfortunately, most visitors to websites are anonymous, so you don't know if they are young, old, rich, poor, etc. If you are lucky enough to have authentication or a login on your website, you may have some of this information, but for most of my clients the "known" percentage is relatively low. In this post, I'll share some things you can do to increase your visitor profiling by using advertising campaigns and other tools.

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A Primer on Cookies in Web Analytics
Josh West, Partner

Some of you may have noticed that I don't blog as much as some of my colleagues (not to mention any names, but this one, this one, or this one). The main reason is that I'm a total nerd (just ask my wife), but in a way that is different from most analytics professionals. I don't spend all day in the data - I spend all data writing code. And it's often hard to translate code into entertaining blog posts, especially for the folks that tend to spend a lot of time reading what my partners have to say.

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Excel Dropdowns Done Right
Tim Wilson, Partner

Do you used in-cell dropdowns in your spreadsheets? I used them all the time. It's both an ease-of-use and a data quality maneuver: clicking a dropdown is faster than typing a value, and it's really hard to mis-type a value when you're not actually typing!

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The Downfall of Tesco and the Omniscience of Analytics
Michele Kiss, Partner

Yesterday, an article in the Harvard Business Review provided food for thought for the analytics industry. In Tesco's Downfall Is a Warning to Data-Driven Retailers, author Michael Schrage ponders how a darling of the "analytics as a competitive advantage" stories, British retailer Tesco, failed so spectacularly - despite a wealth of data and customer insight.

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Creating Conversion Funnels via Segmentation
Adam Greco, Senior Partner

Regardless of what type of website you manage, it is bound to have some sort of conversion funnel. If you are an online retailer, your funnel may consist of people looking at products, selecting products, and then buying products. If you are a B2B company, your funnel may be higher-level like acquisition, research, trial and then form completion.

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10 Tips for Building a Dashboard in Excel
Tim Wilson, Partner

This post has an unintentionally link bait-y post title, I realize. But, I did a quick thought experiment a few weeks ago after walking a client through the structure of a dashboard I'd built for them to see if I could come up with ten discrete tips that I'd put to use when I built it. Turns out…I can!

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Exploring Optimal Post Timing ... Redux
Tim Wilson, Partner

Back in 2012, I developed an Excel worksheet that would take post-level data exported from Facebook Insights and do a little pivot tabling on it to generate some simple heat maps that would provide a visual way to explore when, for a given page, the optimal times of day and days of the week are for posting.

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What I Love: Adobe and Google Analytics*
Tim Wilson, Partner

While in Atlanta last week for ACCELERATE, I got into the age-old discussion of "Adobe Analytics vs. Google Analytics." I'm up to my elbows in both of them, and they're both gunning for each other, so this list is a lot shorter than it would have been a couple of years ago.

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Top 5 Metrics You're Measuring Incorrectly ... or Not
Eric T. Peterson, Senior Partner

Last night as I was casually perusing the days digital analytics news - yes, yes I really do that - I came across a headline and article that got my attention. While the article's title ("Top 5 Metrics You're Measuring Incorrectly") is the sort I am used to seeing in our Buzzfeed-ified world of pithy "made you click" headlines, it was the article's author that got my attention.

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Bulletproof Business Requirements
John Lovett, Senior Partner

As a digital analytics professional, you've probably been tasked with collecting business requirements for measuring a new website/app/feature/etc. This seems like a task that's easy enough, but all too often people get wrapped around the axle and fail to capture what's truly important from a business users' perspective. The result is typically a great deal of wasted time, frustrated business users, and a deep-seated distrust for analytics data.

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Welcome to Team Demystified: Nancy Koons and Elizabeth Eckels!
Eric T. Peterson, Senior Partner

I am delighted to announce that our Team Demystified business unit is continuing to expand with the addition of Nancy Koons and Elizabeth "Smalls" Eckels. Our Team Demystified efforts are exceeding all expectation and are allowing Web Analytics Demystified to provide truly world-class services to our Enterprise-class clients at an entirely new scale.

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When to Use Variables vs SAINT in Adobe Analytics
Adam Greco, Senior Partner

In one of my recent Adobe SiteCatalyst (Analytics) "Top Gun" training classes, a student asked me the following question: When should you use a variable (i.e. eVar or sProp) vs. using SAINT Classifications? This is an interesting question that comes up often, so I thought I would share my thoughts on this and my rules of thumb on the topic.

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5 Tips for #ACCELERATE Exceptionalism
Tim Wilson, Partner

Next month's ACCELERATE conference in Atlanta on September 18th will be the fifth - FIFTH!!! - one. I wish I could say I'd attended every one, but, sadly, I missed Boston due to a recent job change at the time. I was there in San Francisco in 2010, I made a day trip to Chicago in 2011, and I personally scheduled fantastic weather for Columbus in 2013.

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I've Become Aware that Awareness Is a #measure Bugaboo
Tim Wilson, Partner

A Big Question that social and digital media marketers grapple with constantly, whether they realize it or not: Is "awareness" a valid objective for marketing activity?

I've gotten into more than a few heated debates that, at their core, center around this question. Some of those debates have been with myself (those are the ones where I most need a skilled moderator!).

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Advanced Conversion Syntax Merchandising
Adam Greco, Senior Partner

As I have mentioned in the past, one of the Adobe SiteCatalyst (Analytics) topics I loathe talking about is Product Merchandising. Product Merchandising is complicated and often leaves people scratching their heads in my "Top Gun" training classes. However, many people have mentioned to me that my previous post on Product Merchandising eVars helped them a lot so I am going to continue sharing information on this topic.

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Team Demystified Update from Wendy Greco
Eric T. Peterson, Senior Partner

When Eric Peterson asked me to lead Team Demystified a year ago, I couldn't say no! Having seen how hard all of the Web Analytics Demystified partners work and that they are still not able to keep up with the demand of clients for their services, it made sense for Web Analytics Demystified to find another way to scale their services. Since the Demystified team knows all of the best people in our industry and has tons of great clients, it is not surprising that our new Team Demystified venture has taken off as quickly as it has.

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SiteCatalyst Unannounced Features
Adam Greco, Senior Partner

Lately, Adobe has been sneaking in some cool new features into the SiteCatalyst product and doing it without much fanfare. While I am sure these are buried somewhere in release notes, I thought I'd call out two of them that I really like, so you know that they are there.

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Hello. I'm a Radical Analytics Pragmatist
Tim Wilson, Partner

I was reading a post last week by one of the Big Names in web analytics…and it royally pissed me off. I started to comment and then thought, "Why pick a fight?" We've had more than enough of those for our little industry over the past few years. So I let it go.

Except I didn't let it go.

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Competitor Pricing Analysis
Adam Greco, Senior Partner

One of my newest clients is in a highly competitive business in which they sell similar products as other retailers. These days, many online retailers have a hunch that they are being "Amazon-ed," which they define as visitors finding products on their website and then going to see if they can get it cheaper/faster on This client was attempting to use time spent on page as a way to tell if/when visitors were leaving their site to go price shopping.

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How to Deliver Better Recommendations: Forecast the Impact!
Michele Kiss, Partner

One of the most valuable ways to be sure your recommendations are heard is to forecast the impact of your proposal. Consider what is more likely to be heard: "I think we should do X ..." vs "I think we should do X, and with a 2% increase in conversion, that would drive a $1MM increase in revenue ..."

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ACCELERATE 2014 "Advanced Analytics Education" Classes Posted
Eric T. Peterson, Senior Partner

I am delighted to share the news that our 2014 "Advanced Analytics Education" classes have been posted and are available for registration. We expanded our offering this year and will be offering four concurrent analytics and optimization training sessions from all of the Web Analytics Demystified Partners and Senior Partners on September 16th and 17th at the Cobb Galaria in Atlanta, Georgia.

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Product Cart Addition Sequence
Adam Greco, Senior Partner

In working with a client recently, an interesting question arose around cart additions. This client wanted to know the order in which visitors were adding products to the shopping cart. Which products tended to be added first, second third, etc.? They also wanted to know which products were added after a specific product was added to the cart (i.e. if a visitor adds product A, what is the next product they tend to add?). Finally, they wondered which cart add product combinations most often lead to orders.

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7 Tips For Delivering Better Analytics Recommendations
Michele Kiss, Partner

As an analyst, your value is not just in the data you deliver, but in the insight and recommendations you can provide. But what is an analyst to do when those recommendations seem to fall on deaf ears?

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Overcoming The Analyst Curse: DON'T Show Your Math!
Michele Kiss, Partner

If I could give one piece of advice to an aspiring analyst, it would be this: Stop showing your "math". A tendency towards "TMI deliverables" is common, especially in newer analysts. However, while analysts typically do this in an attempt to demonstrate credibility ("See? I used all the right data and methods!") they do so at the expense of actually being heard.

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Making Tables of Numbers Comprehensible
Tim Wilson, Partner

I'm always amazed (read: dismayed) when I see the results of an analysis presented with a key set of the results delivered as a raw table of numbers. It is impossible to instantly comprehend a data table that has more than 3 or 4 rows and 3 or 4 columns. And, "instant comprehension" should be the goal of any presentation of information - it's the hook that gets your audience's brain wrapped around the material and ready to ponder it more deeply.

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Automating the Cleanup of Facebook Insights Exports
Tim Wilson, Partner

This post (the download, really - it's not much of a post) is about dealing with exports from Facebook Insights. If that's not something you do, skip it. Go back to Facebook and watch some cat videos. If you are in a situation where you get data about your Facebook page by exporting .csv or .xls files from the Facebook Insights web interface, then you probably sometimes think you need a 52" monitor to manage the horizontal scrolling.

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The Recent Forrester Wave on Web Analytics ... is Wrong
Eric T. Peterson, Senior Partner

Having worked as an industry analyst back in the day I still find myself interested in what the analyst community has to say about web analytics, especially when it comes to vendor evaluation. The evaluations are interesting because of the sheer amount of work that goes into them in an attempt to distill entire companies down into simple infographics, tables, and single paragraph summaries.

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Funnel Visualizations That Make Sense
Tim Wilson, Partner

Funnels, as a concept, make some sense (although someone once made a good argument that they make no sense, since, when the concept is applied by marketers, the funnel is really more a "very, very leaky funnel," which would be a worthless funnel - real-world funnels get all of a liquid from a wide opening through a smaller spout; but, let's not quibble).

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Reenergizing Your Web Analytics Program & Implementation
Adam Greco, Senior Partner

Those of you who have read my blog posts (and book) over the years, know that I have lots of opinions when it comes to web analytics, web analytics implementations and especially those using Adobe Analytics. Whenever possible, I try to impart lessons I have learned during my web analytics career so you can improve things at your organization.

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Registration for ACCELERATE 2014 is now open
Eric T. Peterson, Senior Partner

I am excited to announce that registration for ACCELERATE 2014 on September 18th in Atlanta, Georgia is now open. You can learn more about the event and our unique "Ten Tips in Twenty Minutes" format on our ACCELERATE mini-site, and we plan to have registration open for our Advanced Analytics Education pre-ACCELERATE training sessions in the coming weeks.

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Current Order Value
Adam Greco, Senior Partner

I recently had a client pose an interesting question related to their shopping cart. They wanted to know the distribution of money its visitors were bringing with them to each step of the shopping cart funnel.

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A Guide to Segment Sharing in Adobe Analytics
Tim Wilson, Partner

Over the past year, I've run into situations multiple times where I wanted an Adobe Analytics segment to be available in multiple Adobe Analytics platforms. It turns out…that's not as easy as it sounds. I actually went multiple rounds with Client Care once trying to get it figured out. And, I've found "the answer" on more than one occasion, only to later realize that that answer was a bit misguided.

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Currencies & Exchange Rates
Adam Greco, Senior Partner

If your web analytics work covers websites or apps that span different countries, there are some important aspects of Adobe SiteCatalyst (Analytics) that you must know. In this post, I will share some of the things I have learned over the years related to currencies and exchange rates in SiteCatalyst.

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Linking Authenticated Visitors Across Devices
Adam Greco, Senior Partner

In the last few years, people have become accustomed to using multiple digital devices simultaneously. While watching the recent winter Olympics, consumers might be on the Olympics website, while also using native mobile or tablet apps. As a result, some of my clients have asked me whether it is possible to link visits and paths across these devices so they can see cross-device paths and other behaviors.

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The 80/20 Rule for Analytics Teams
Eric T. Peterson, Senior Partner

I had the pleasure last week of visiting with one of Web Analytics Demystified's longest-standing and, at least from a digital analytical perspective, most successful clients. The team has grown tremendously over the years in terms of size and, more importantly, stature within the broader multi-channel business and has become one of the most productive and mature digital analytics groups that I personally am aware of across the industry.

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Ten Things You Should ALWAYS Do (or Not Do) in Excel
Tim Wilson, Partner

Last week I was surprised by the Twitter conversation a fairly innocuous vent-via-Twitter tweet started, with several people noting that they had no idea you could simple turn off the gridlines.

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Omni Man (and Team Demystified) Needs You!
Adam Greco, Senior Partner

As someone in the web analytics field, you probably hear how lucky you are due to the fact that there are always web analytics jobs available. When the rest of the country is looking for work and you get daily calls from recruiters, it isn't a bad position to be in! At Web Analytics Demystified, we have more than doubled in the past year and still cannot keep up with the demand, so I am reaching out to you ...

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A Useful Framework for Social Media "Engagements"
Tim Wilson, Partner

Whether you have a single toe dipped in the waters of social media analytics or are fully submerged and drowning, you've almost certainly grappled with "engagement." This post isn't going to answer the question "Is engagement ROI?" ...

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It's not about "Big Data", it's about the "RIGHT data"
Michele Kiss, Partner

Unless you've been living under a rock, you have heard (and perhaps grown tired) of the buzzword "big data." But in attempts to chase the "next shiny thing", companies may focus too much on "big data" rather than the "right data."

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