Archive for 'Web Analytics Business Process'

When John Lovett joined Aurelie and I here at Web Analytics Demystified earlier this month an awful lot of people said, “Hey, nice job getting such nice guy on board,” “We love John, he’s great,” and “Man, what a great addition to your team!” Clearly John has the respect of the industry, but one thing that remained an open question in some people’s minds was “how will John make the transition from the ivory tower an analyst sits in to the ground floor where consultants actually do work?”

I admit, I wondered that too in a way, having made a slightly different transition myself years ago. It’s not easy to come away from a situation where you provide advice but are tasked with, honestly, doing very little real work. During my own tenure at JupiterResearch years ago I ensured my own connection to practical web analytics by writing my second and third books. But John had been an analyst for nearly 10 years … and so wondering how he’d hit the ground was a reasonable question.

Wonder no more.

While John has already contributed greatly to the businesses bottom line and helped out with one of our largest new retail clients, he absolutely floored me this morning when he published his post Defining a Web Analytics Strategy: A Manifesto. I asked him to elaborate on some comments he made at Emetrics where he essentially poo-pooed the use of so called “Web Analytics Maturity Models”, describing the almost religious zeal some people seem to have when talking about models and declaring himself as a “Model Atheist.”

Having written the original Web Analytics Maturity Model back in 2005, I have had first-hand experience with their failure to produce anything more than a generalized awareness that most companies simply don’t “get” web analytics, something that we more or less all know already. But honestly I was surprised when John took this position on the subject because, well, in my experience those that don’t do, teach, and models are a classic teaching tool.

I had assumed that as an analyst John was a teacher, not a do-er like I have been for years now in my capacity as a practice leader, consultant, and web analyst. Man was I wrong …

John’s “Manifesto” is perhaps the most lucid yet succinct explanation I have ever read detailing the steps required to make web analytics work for your business (as opposed to the other way around.) I almost asked him to edit the post for fear that he was opening our kimono too much, but if Social Media has taught us anything it has taught us that transparency is king. The fact that he managed to encapsulate what others have been trying to explain with long-winded speeches, tangential arguments, and downright rude behavior is a huge plus.

Some of you may read John’s manifesto and think “Gee, this seems to point to the need for outside consultants” which is a fair criticism. But before you react consider two things:

1. Consultants (like us) have a tendency to, you know, recommend consulting. Everyone’s perspective arises from their own personal biases, regardless of how many times they declare the contrary. We are consultants, consultants who want to feed their children. Forgive us our bias and we will forgive you yours …
2. Consultants in the Enterprise are like death and taxes, we are more or less inevitable. Often times an outside perspective is exactly what the business needs to actually start to act upon the message that otherwise great employees have been stating for years. Other times the business simply stops listening to their employees and won’t make a move until McKinsey, Bain, or Demystified come in and charge big money for insights that were already there. Either way, ours is the second (or is it third) oldest profession and it must be for a reason …

I would challenge you, dear reader, to spend some time reading John’s post and considering what he has to say. Think about how you could apply his ten insights to your business regardless of whether you turn to consultants for advice or not. Listen to your business partners needs, put away your models and roll up your sleeves, transcend mediocrity, establish your own waterfall and embrace change!

When I said “web analytics is hard” I meant it, I really, really did. But I wasn’t trying to box anyone in or establish myself as some kind of amazingly wonderful “guru”, I was simply telling you all the truth based on my dozen years of experience in the sector. Yes, getting started can be easy; yes, making Google Analytics do stuff can be easy; and yes, you can do an awful lot in an hour a day if you simply apply yourself to the task … but the problem is that within any business of size, complexity, or nuance — which is to say all businesses everywhere — the act of getting from raw data to valuable business insights that you can repeatedly take action upon is apparently so freaking difficult that almost nobody does it.

How is that “easy?”

You all know I love a good debate so if you disagree with my comments here please let me know. If, however, you have something to add to John’s manifesto, I would encourage you to comment on his blog post directly.

Happy Holidays, everyone.

Recently Google published the results of a Forrester Research study they had commissioned (PDF) to help the broader market understand the use and adoption of free web analytics solution.  Google should be applauded for commissioning Forrester to conduct this work, especially given the quality of the research and the level of insights provided.  Without a doubt, free solutions like Google Analytics and Yahoo Web Analytics are having an impact on our industry and driving change in ways few of us ever imagined.

I really did enjoy the Forrester report, primarily because the author (John Lovett) managed to surface totally new data.  When he first told me that over half of Enterprise businesses were using free solutions I have to admit I didn’t believe him.  In a way I still don’t, but perhaps that’s only because I work with a slightly different sample than he presents.  Regardless, John’s report paints a picture of an increasingly challenging market for companies selling web analytics and a new sophistication among end users.

Speaking of sophistication, there are a few points in the report that I question, and since I have pretty good luck getting feedback from readers on big picture stories I figured I’d bring them up here in the blog.  Before I do I want to emphasize that I am not questioning Forrester or John’s work—I am merely trying to explore some data that I find contrary to my own experience in this public forum.  To this end I pose a handful of questions that I would love to discuss either openly in comments or via email.

The first point I question is the observation in Figure 3 that 70% of companies report having a “well-defined analytics strategy.”  Two years ago my own research found that fewer than 10% of companies worldwide had a well-defined strategy for web analytics.  Last year Econsultancy reported that only 18% of the companies in their sample had a strategy for analytics.  To jump from these low numbers to the majority of Enterprises just doesn’t square with my general experience in the industry.

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Remember, the implication of this data point is that 70% of all companies having more than 1,000 employees have a “well-defined analytics strategy.”  According to a 2004 report from the U.S. Census Bureau there were just over 12,000 companies in the U.S. with more than 1,000 employees.  Without assuming any growth between 2004 and 2009, Forrester’s 70% figure would result in over 8,500 companies in the U.S. that have a “well-defined” strategy for web analytics. Does that sound right to you?

Consider that the combined customer count for Omniture, WebTrends, Coremetrics, and Unica combined in the U.S. doesn’t even add up to 8,500 companies.  Even if you use the more conservative 13% who “strongly agree” with Forrester’s statement you end up with over 1,500 U.S. companies.  I may suffer from sample bias, but personally I can barely think of 150 companies that I would identify as having any strategy for web analytics, much less a “well-defined” one.

Most companies I talk to have the beginnings of an over-arching strategy—they’ve realized the need for people and are beginning to reduce their general reliance on click-stream data alone.  But given that I think about this topic from time to time, I think a “well-defined” strategy for web analytics takes into account multiple integrated technologies, appropriate staffing, and well thought-out business and knowledge processes for putting their technology and staff to work.  What does the phrase “well-defined strategy” imply to you?

Similarly, if 60% of companies truly believed that “investments in Web analytics people are more valuable than investments in Web analytics technology” there would be THOUSANDS of practitioners employed in the U.S. alone.  But again, every conference, every meeting, every conference call, and every other data point suggests that the need for people in web analytics is still an emerging need.  Hell, Emetrics in San Jose earlier this year barely drew 200 actual practitioners by my count.  How many web analytics practitioners do you think there are in the United States?

Same problem with the rest of the responses to Figure 3 on web analytics as a “technology we cannot do without” (75%) and the significance of the role web analytics plays in driving decisions (71%).  Perhaps I’m talking to entirely the wrong people, perhaps I’m interpreting these data wrong, and perhaps I’ve gone flat-out crazy, but these responses just don’t match my personal understanding and experience in the web analytics industry.

This issue of data that simply does not make sense, while not universally manifest in the report, manifests elsewhere as well. For example, Figure 8 reports on the percentage of application used segmented by fee and free tools:

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When I look at these responses and see that 63 percent of respondents using fee-based tools and 50 percent of respondents using free tools claim to be effectively using more than half the available functionality, again I find myself scratching my head. As this data appears to speak to the general sophistication of use of analytics I went back and looked at Dennis Mortensen’s quantitative study of how IndexTools was being used around the world.

Dennis reports that fewer than 10% of his customers were using even the most basic “advanced” features in web analytics (report customization) and that fewer that 4% of his customers (on average) are making any “advanced” use of the IndexTools application. While this dataset is somewhat biased towards European companies who I believe, on average, to be somewhat behind their U.S. counterparts it does provide an objective view in how web analytics are used that seems to directly contradict the self-reported responses in Forrester’s figure 8.

Clearly there is a gap between the responses John collected and the current state of the web analytics market.  Since John is a very smart guy I know part of his rebuttal will include the observation that he surveyed people directly responsible for web analytics (see Forrester’s methodology) and that people in general have a tendency towards positivism. Trust me, my son is the most handsome little boy ever born and my daughter’s beauty is only matched by that of Aphrodite … same for your kids, right?

Given the difficulty associated with gathering truly objective data regarding the use of web analytics, this type of self-reported data is usually what we have to go on.  While Omniture, WebTrends, Coremetrics, and Unica all have the fundamental capability to report data similar to that provided by Mr. Mortensen, it may not be in their best interests to expose underwhelming adoption and unsophisticated use (if that is what the analysis uncovered.)  Ultimately we’re forced to accept these self-reported responses and  then reconcile them against our own views, which is why I’m asking my readers what they think about the data Forrester is reporting!

Regarding these self-reported attitudinal responses on how web analytics is used strategically, perhaps the truth is found in the companies who “strongly agree” with John’s statements.  If we apply this lens, as opposed to the more optimistic view, we get the following:

• 17% of companies recognize that web analytics is a technology they cannot live without;
• Web analytics plays a significant role in driving decisions at 12% of companies;
• 13% of companies have a well-defined web analytics strategy;
• 9% of companies recognize that investments in people are more valuable than investments in technology

These numbers start to make a lot more sense to me.  Likely the truth, as with so much in our industry, lies somewhere in between, but I would love to hear what you think about these adjusted numbers.  Do the lower numbers make more sense to you, or do you agree with John’s more optimistic assessment?

Unfortunately if the lower numbers are correct the implication is that despite the incredibly hard work that companies, consultants, and industry thought-leaders around the world have done for years we still have an incredibly long way to go before web analytics is recognized as the valuable business practice that you all know it can be!

Regardless I want to state that I do not disagree at all with the fundamental thesis in this report, that “free” is creating a whole new level of interest in web analytics and that, given proper consideration, free is an excellent alternative to paid solutions.  Lacking clear strategy and resources, too many companies have wasted too much money on paid solutions for free to not be compelling.  Thanks to the dedication of the Google and Yahoo teams, the world now has access to great applications that are in some regards more compelling than fee-based alternatives.

While I may not have said this a few years ago, today I honestly do believe that “free” is a viable and appropriate alternative to fee-based solutions. While not appropriate in every situation, it is irresponsible to suggest that any company not willing to fully engage in web analytics should pay for ongoing services and support. Given advances from Google and the availability of Yahoo Web Analytics, any motivated company large or small now has access to a wealth of data that can be translated into information, insights, and recommendations.

Conversely I agree with John (and Jim, and almost ever thought leader I respect) who states that you need to “prioritize your business needs and culture for analytics first and then evaluate the tools.”  This goes back to the fundamental value proposition at Web Analytics Demystified: It’s not the tools you use but how you use them. If you’re not invested in developing and executing a clearly defined strategy for digital measurement, you may as well be grepping your log files.

I would love your feedback on this post, either directly in comments or via email. Thanks again to the folks at Google for making this awesome research freely available and to John Lovett for shedding light on this incredibly important aspect of our sector.  Remember: we are analysts—our jobs are to ask hard questions and then ask even harder ones!

(Last week I published PART I of this post which you should read first if you haven’t already done so.)

STEP FOUR: DETERMINE YOUR KEY PERFORMANCE INDICATORS AND CRITICAL REPORTS

You’re probably thinking “shouldn’t we have done this after we defined our business objectives and activities?” Conventional wisdom would probably say you should, but in my experience if you don’t have a clear process for leveraging those key performance indicators (KPIs) and critical reports, you may end up with one of three things:

1. A huge report of 40 KPIs distributed across the organization that few people are likely to read and even fewer likely to act upon
2. No KPIs distributed at all, and the expectation that everyone will simply “log in” and get the information on their own
3. Well-defined and clearly articulated KPIs distributed hierarchically throughout the organization (because hey maybe you read a great book on the subject at some point)

The problem is that only the third possibility will deeply benefit your organization. I know that some people talk about hundreds of internal users who really get web analytics and all make superb decisions with the data, but this is very much the exception, not the rule. Remember, in our Web Analytics Demystified Spring Survey 69 percent of respondents said that they did not believe the majority of people using web analytics data in their organization actually understood that data.

It is far better for your analytics hub, as mandated by their executive sponsor in agreement with his or her peers throughout the organization, work directly with the individual spokes to ensure that appropriate KPIs are defined and the basis for those measures is clear. The hub then follows-up with appropriate explanation about the measures, including training on the reports and data that forms the basis of the indicators.

Your critical reports are directly tied to your key performance indicators (which remember are tied directly to your business objectives.) If you belong to the marketing organization than your KPIs will be measures like “Campaign Response Rate”, “Campaign Conversion Rate” and “Campaign Cost per Click”. Obviously as these KPIs change, appropriate tactical resources in the marketing spoke will review campaign response, conversion, and cost reports in your analytics application.

Your KPIs and critical reports will differ dramatically depending on what department you work for and where in that department you work — remember that the best practice for key performance indicator distribution is to deliver the specifically and hierarchically. Most attempts that I have seen to send “everything to everybody” have failed (often miserably).

STEP 5: DETERMINE HOW YOU’LL DELIVER ANALYSIS

Once you know what your KPIs and critical reports will look like, the next step is to determine how you’ll produce and deliver analysis. Let’s assume for a moment that you’ve got a hub-and-spoke model in place and the hub is receiving regular requests for more information, insights, and recommendations. The question then becomes “how will you deliver those insights and recommendations?”

As I said last week, there is no one “right” way to communicate about web analytics data but there are many, many wrong ways. The central challenge when delivering analysis stems from the fact that so few people really understand what web analytics terms mean, what the limitations of the technology are, and what is possible and impossible to report on. But it’s not like you can just give up and ignore the confusion, so what’s a great analyst to do?

The answer is “work harder, and think outside the box” (to use an overused term). While reports and raw data are best delivered using the Bottom Line Up Front (BLUF) method, analysis really needs to be more engaging. Remember: when you deliver analysis, what you really need to do is to convince the listeners that they need to take some action. To do this you absolutely have to be engaging.

Things that have worked for clients of mine in the past include:

• Well-delivered presentations, given IN PERSON, not just sent via email in hopes that people will review and understand
• Well-written documents, followed by a meeting to make sure that everyone READ the document and is on the same page
• Short summary documents, written up like a newsletter or newspaper article, designed to get people to attend a meeting or presentation

Since we’re in a Web 2.0 world, and since many of you are increasingly comfortable using new technology, a few other things you may want to consider include:

• An internal analysis Wiki that people can subscribe to and participate in. The Wiki is a good idea because it allows you to capture the conversation in a searchable format
• A regular analysis podcast, providing an update on past analysis and summarizing the data currently being reviewed
• A analysis video or vidcast, created with tools like TechSmith Camasis that allow you to easily blend images, live screen capture (useful when showing people live data in your analytics application), and annotation

The advantage the final two ideas confer is their ability to be downloaded to an MP3 player like the iPod or iPhone. If you have busy executives, you might be better able to reach them if you give them something to watch on the airplane or listen to on the drive home.

Keep in mind that none of these “Web 2.0″ strategies should replace well-written, well-presented analysis, delivered in person whenever possible and making specific recommendations for changes (including a testing plan when possible!)

STEP 6: PUT IT ALL TOGETHER!

Assuming you’ve completed the previous five steps, you now have a functional web analytics organization, one capable of delivering relevant reports and producing actionable analysis. Now the challenge is to stop spending all of your time generating reports and start delivering analysis!

Unfortunately, for many organizations this is really, really difficult. Even when there are dedicated resources — people specifically hired to do web “analytics” (not web “reporting”) — far too many bright folks end us spending all of their time churning out reports. Even worse, these reports often go unread, unused, and unnoticed despite the real and opportunity costs associated with generating them.

To be really, really successful with web analytics you have to train the organization to stop looking for reports and start asking for analysis, insights, and recommendations. While every situation is different, ask yourself how closely your organization follows these steps:

1. Automated KPI reports arrive, highlighting a potential problem associated with a core business objective
2. Line of business analytics resources consult critical reports directly looking for a reasonable explanation
3. Failing a reasonable explanation, business resources request analysis resources from the analytics hub
4. Analytics hub double-checks LOB’s cursory analysis, confirming the need for deeper exploration
5. Analytics hub prioritizes analysis with the business based on pre-agreed criteria
6. Analysis is delivered back to the business along with recommendations and a testing plan
7. Recommendations are reviewed by the business, test plan is agreed upon
8. Tests are run, results are socialized as follow-up to the original analysis
9. Incremental value of change is recorded to help calculate web analytics return on investment

Individual departments are still getting their reports, but they’re generating them by themselves. Senior managers have an appropriate view into the metrics, and their own resources to evaluate observed changes. Those resources have a way to get help when help is needed. Help (the hub) isn’t bogged down generating ad hoc reports all the time and is able to focus on high-value priorities. People produce analysis and make recommendations. Recommendations are tested. Optimization happens.

Kinda brings a tear to your eye, doesn’t it?

I know there are a hundred other things that come up in the line of business for any of you who are working practitioners, but having a clear communication strategy is the first step towards whittling that list down to something reasonable and, more importantly, valuable to your organization. Defining your business objectives, clarifying ownership and organization structures, establishing KPIs and critical reports, and knowing what your analysis output will actually look like is fundamental.

Defining your web analytics communication strategy will let the data work for you, not make you work for the data. It will help you move from making purely tactical decisions and start using web analytics strategically as part of your entire business. Over time you’ll find that a clear strategy, no surprise, helps the entire organization better understand web analytics in general and the value your investment can provide. And perhaps most importantly, a clear strategy will cut down on the volume of under-used, unused, and ignored reports traveling across your network.

If you’re interested in defining a web analytics communication strategy in your organization, I’d love to talk to you. If you don’t need help, I’m still happy to provide encouragement. If I can help you, great. If I can’t help you, I bet I know somebody who can!

Judah’s recent post titled “what does your web analytics team look like” reminded me of something that has been on my mind a lot since I presented my Web Analytics: A Day a Month webcast for the American Marketing Association last month. As I travel the world talking about web analytics to companies of all shapes and sizes, one thing I’m struck by is the number of differences in how companies approach sharing web analytic data and information.

It’s not as if there is any one “right” way to communicate about web analytics, but it is clear that there are many, many wrong ways to do it. But rather than dwell on wrongness, I prefer to focus on rightness so here are a few thoughts on developing a clear strategy for communicating web analytics.

This post may seem pretty basic to many of you, but if it does I would encourage you to ask yourself these questions:

• What decisions are web analytics driving in your organization?
• Are those decisions largely tactical or are they truly strategic?
• Do you feel like most people in your organization understand web data?
• Are you producing reports that are going under-used, unused, or are flat out being ignored?

If you are less than impressed with your responses I would encourage you to read on. I’m not saying you’ll necessarily learn anything new, but maybe you’ll read something that you think your boss should hear.

STEP ONE: DEFINE YOUR BUSINESS OBJECTIVES

I know, I know, you’ve heard me say this before. I’ve been saying this since 2002 but I’m going to keep on saying it since it bears repeating. By clearly defining your business objectives you get two things done:

1. You remind everyone in your organization why you have a web site and why those of you who work online come to work every day.
2. You build a framework against which you will define the core activities and interactions that are worth measuring and communicating

The second point is important: You cannot measure everything effectively and efficiently — you have to have some basis for deciding what to measure and what to report. I have seen any number of companies work hard to collect “all possible data” only to realize that few people are actually asking for that data and even fewer are doing anything with it.

When you define your business objectives and get consensus on what is most important to your online business, the measurable activities that you will be communicating across the organization become clear. Suddenly rather than struggling to measure every aspect of every page across every segment you’re able to focus on critical measures in critical paths in your most important visitor segments.

I covered all of this in Web Analytics Demystified what seems like years ago and again in Web Site Measurement Hacks (which you can now purchase direct from my site, had I mentioned that?) but again it is worth repeating. And while it is far less common now that I will ask companies about their business objectives and get conflicting opinions, many companies have still not gone through the process of clearly documenting these objectives and the associated activities to serve as basis for their measurement efforts.

STEP TWO: DETERMINE WHO OWNS ANALYTICS AT YOUR COMPANY

One of the biggest problems I see in web analytics today is a lack of clarity regarding ownership of analytics inside the organization. On this point I will be as clear as possible:

The owner of web analytics in your company NEEDS to be someone senior enough to ensure that analysis is being produced and used!

I spend an awful lot of time as a consultant talking about ownership and structure in analytics. Your executive sponsor needs to be closely connected to web analytics and have a clear understanding of the value and opportunity measurement provides. If this is not the case, you may spend an awful lot of time producing reports that go unread and analysis that goes unused.

I suspect that my fellow blogger Daniel Shields can attest to the goodness in this recommendation, working for a great boss at CableOrganizer, but more often than not when I ask the question “Who owns web analytics?” I get responses that talk about budget centers, middle-management who haven’t got budget authority or enough political clout, or worse yet, nothing but uncomfortable laughter.

Clients almost always ask “Where should web analytics live? Should it live in Finance, I.T., Marketing, or Research?” to which I almost always answer “Who is the most senior, well-connected person in your organization that is likely to really understand what web analytics is good for?” and then give their department as my answer. Here are some additional thoughts:

• Finance: Analytics living in your finance organization is fine because your CFO understands how to produce detailed analysis and make that analysis valuable internally
• Marketing: Marketing is great since in many cases marketing has the most to gain (or lose) based on web analytics data and analysis
• Research: If you have a market research organization this is also a great home since the analysis team in research usually has an excellent understanding of the customer and their (offline) behavior
• Information Technology: I personally don’t usually recommend that web analytics live in I.T. There is often too much baggage and a disconnect between I.T. and the business for this to work (but I do know of a handful of examples where I.T. ownership of analytics does work)

At the end of the day the most successful analytics organizations are those where the executive sponsor “gets it” and is able to champion for the cause at a very high level. They will need money, resources, and time from the rest of the company to deeply integrate the necessary web analytics business processes, so seniority is an absolute must.

STEP THREE: DETERMINE YOUR ANALYTICS ORGANIZATIONAL STRUCTURE

This is the step I’ve been thinking a lot about lately, how analytics organizations are structured and integrated into medium-to-large-to-very large companies. As I’m sure you know, this piece is far from a no-brainer — whether you subscribe to 10/90, 10/20/70, or some other percentage-wise distribution of effort, I think we can all agree that people are critical to web analytics success.

But as Judah deftly points out, just hiring someone is only the beginning of the work: The more important piece is determining how those resources are going to actually provide benefit back to the entire organization. You need to have a clear strategy for leveraging these resources to produce the maximum number of insights possible.

For about four yeas I have been talking about the “hub and spoke” model for web analytics organizations, especially to medium, large, and very large companies. The hub and spoke is basically a centralized/decentralized model for measurement, one that centralizes deep analysis expertise for use across the organization but mandates that each individual department and line of business takes responsibility for their own reporting needs.

The folks in the analytics hub are directly responsible for things like:

• Producing analysis, real analysis, to support business decisions
• Providing training out to the rest of the organization on tools and data
• Communicating about the goodness (or lack thereof) in the data collected
• Interfacing with the vendor(s) providing measurement software and services
• Managing multivariate tests and analyzing their results
• Working with I.T. to make changes to data collection and integration

Perhaps most importantly, the hub work directly for the executive sponsor for analytics (see STEP TWO above.) Establishing a real web analytics hub is the first thing you need to do if you want to STOP spending 80 percent of your time generating reports (something a prospect recently referred to as being a “report monkey” which they didn’t seem super-excited about …)

The folks in the individual departments and LOBs are responsible for things like:

• Paying careful attention to their key performance indicators and react to observed changes
• Spend enough time learning the available technology to answer at least basic questions when changes are observed
• Generating whatever reports are necessary on a regular basis and modifying those as required
• Interface with the analytics hub to ensure that requests for testing and analysis are clearly communicated
• Respond to test results and analysis by putting the insights generated to work for the organization

The best possible news is that the folks in the spokes don’t have to be web analytics experts! Hell, they don’t even need to read the available literature if they don’t want to (but they should.) They really only need to take enough time to learn what their KPIs are telling them and which reports in the analytics application(s) are relevant when things change.

Thinking about the relationship between the hub and spokes:

• The hub does analysis, and the spokes do reporting
• The hub executes multivariate tests, but the spokes recommend them
• The hub work directly with I.T., the spokes get to continue avoiding I.T.
• The hub helps to plan, manage, and monitor KPIs, the spokes live and die by them
• The hub runs something like Omniture Discover or IndexTools Rubix, the spokes use SiteCatalyst or Google Analytics

This is great news because there are many, many people out there that have a 0.2, a 0.33, or a 0.5 FTE for web analytics — not nearly enough time to really get deep into web analytics but enough to create the expectation that they’ll use the data to make business decisions. The hub and spoke model creates a business process to support partial FTE in their endeavor to use and benefit from web analytics, which those partial FTE seem to truly, truly appreciate!

In my experience, over time the people who really like this kind of work will pop up and ask great questions, looking to push the boundaries of their understanding of “our little craft.” They’ll read books, blogs, go to conferences, etc. and over time may realize that they really want to work in the field of web analytics full time. Which is great, because without those people flowing into the system, the multitude of recruiters and companies across the globe looking for experience web analytics professionals haven’t got a prayer.

Since Judah, Daniel, and I have been talking about the length of out posts lately I think I’ll stop here and publish Part II of this post later this week.

The key takeaways from the thoughts here are:

1. You have to have a web analytics communication strategy
2. You have to clearly define your business objectives and supporting activities
3. You need to define and establish an analytics organization
4. Your analytics organization needs to report to an appropriately senior person
5. The hub and spoke model for web analytics has many advantages, especially in large organizations
6. Web analytics done well has a tendency to make people more, not less, interested in web analytics (which is good!)

Last week I had the privilege and pleasure of attending SEMphonic’s first ever X Change conference. My friend Gary Angel asked me to give the keynote speech and lead a “huddle” on the processes involved in doing web analytics. As I posted back in August, I was pretty excited about the event because of the format SEMphonic had selected — building the event around small-group interactions rather than the “big room, talk-at-you-not-to-you” format so common in conferences today.

Not that I have anything against big conferences, Jim Sterne’s formerly-called-the-Emetrics Summit is still my favorite conference of all time even thought it will probably grow past 600 in Washington next month, and I had a blast at both Shop.ORG (2000+) and Holland’s E-Day (1500+) and hope to be invited to Internet Retailer’s event in Chicago next summer (rumored to be 5000+). But in my experience big conferences actually limit what you’re able to learn if you’re a face-to-face communicator like me. I always end up having short conversations with people in the hallways between presentations or at social events, and the really deep stuff ends up happening in the proverbial (and real!) lobby bar.

SEMphonic X Change was different.

The huddles more-or-less forced us all to expand on our ideas and share our experiences. The one I led on process was great (I thought) and I ended up agreeing to print and produce “NO TAGS, NO TRACKING” t-shirts for all 15 people in the room. But I was absolutely blown away by the huddles I attended:

• Terry Cohen of Digitas, leading a conversation about measuring engagement that covered how engagement can be measured from the microscopic to the macroscopic level.
• Joseph Carrabis of NextStage, leading a conversation about attitudes and communication
• Matt Belkin of Omniture, leading a conversation about combining online and offline data
• Aaron Gray of WebTrends, leading a conversation about using behavioral data (an EXCELLENT huddle IMHO!)

Think about it: Four huddles led by four of the brightest minds in measurement today (okay, three, since Joseph explicitly states that he’s not a measurement wonk like the rest of us, but he’s the biggest thinker I know …) and there were only 10 people in the conversation on average.

How cool is that?

Not all the huddles were apparently as good as the four I attended, but overall everyone I talked to was quite impressed with the format. And everyone I talked to agreed that they would be back at X Change next year (providing Gary and Joel have the event, which I certainly hope they do!)

I strongly recommend that you consider SEMphonic X Change next year if your schedule permits, especially if you’re an opinionated measurement wonk who isn’t afraid to spout off about stuff they believe to be important (yes Ian, you.)

On that point, this event would have been even better if just a few more people would have made the trip, thought-leaders like Avinash Kaushik (shockingly absent, despite being able to basically walk to Napa from his house if you’re in shape), Brett Crosby from Google Analytics, the Jims (Novo and Sterne), at least one Eisenberg (they sent JQvT instead), Stephane Hamel (budget constraints), Rene and Aurelie, Steve Jackson, the aforementioned Ian, and probably a few dozen more people I’m forgetting, apologies!

I say this because I really believe what I said in my keynote:

Collectively “we” are the web analytics industry.

The vendors are not the industry, the Web Analytics Association is not the industry, all of us are the web analytics industry, and collectively we need to debate and discuss what this industry is going to become. But I don’t believe we can make the decisions necessary in the Yahoo! group, on phone calls, or over email. We need to sit down, face-to-face-to-face and talk about standards, debate definitions, compare notes, and use our old fashioned “Web 0.0″ skills to hash out some of the really hard stuff that remains left to tackle.

Jacques Warren made a similar comment in my call for the WAA to “do something” with their recently published standards document and he is spot-on correct. Web analytics is hard, and it isn’t going to get any easier if we just sit and listen. Let’s sit and talk, let’s debate, let’s act.

’nuff said.

I’ll leave you with this parting shot about X Change, a comparison I’m shocked that nobody smarter than I has already made:

• Emetrics is the Web 1.0 conference for web analytics where you will learn a ton and be very happy
• X Change is the Web 2.0 conference for web analytics where you will contribute a ton and be very satisfied

Mad props to Gary, Joel, Grace, Barbara, Phil, June, and everyone else at SEMphonic for throwing such an amazing event!