Howdy All,
this post is a response to Visitor Engagement: Time for a reality check? posted by Matt Belkin, 14 July 08 on Industry Insights The Omniture Blog. A friend sent me the link and asked for my thoughts. I read the post, formed my thoughts, asked some folks to review what I wrote, did a little editing and went to post it today. I clicked to post my comment and my screen went blank (quite common for me) hence I’m posting my comment here and on Responding to “Visitor Engagement: Time for a reality check?”. Susan Bratton calls me a Righteous Luddite. Not sure about the righteous part, definitely a Luddite.

Anyway, here’s what I posted as a comment to “Visitor Engagement: Time for a reality check?”

Hello,

If I read the paragraphs under the The Basic Premise of Measurement heading correctly the logical outcome is that the only measurements being made (or provide value) are those that are subject to intervention (”improve”).

I have to admit that confuses me. I agree that lots of things are measured with the goal of acting upon the results of the measurement. I also recognize that lots of measurements are made simply to know what they are and with no realistic hopes of being able to modifying what’s being measured. I did look in several texts (started with Horst De la Croix’s Psychological Measurement and Prediction, went to Sabins’ Remote Sensing: Principles and Interpretation (I’ve often thought this discipline most closely demonstrates concepts and principles that would be valuable to people doing web analytics. I mean, consider Sabins’ opening statement to the 2nd edition, “Remote Sensing is broadly defined as collecting and interpreting information about a target without being in physical contact with the object.” Admittedly website visitors aren’t the subject of the book and the theoretical constructs often apply, I think), skipped over to Fukunaga’s Introduction to Statistical Pattern Recognition with lots of side trips into other fields) to find something similar to “The basic premise of measurement is that you want to measure something so you can improve it, if necessary.”

The best I could come up with was that the basic premise of measurement is to determine if something is measurable. I think the reframe is valid and leads to some necessary concerns.

Example: Some factor, A, is recognized and known to exist within a system (a website). A measurement is taken. Modifications are made to the system and another measurement is taken. But factor A hasn’t changed. Perhaps something else has changed and not factor A, the recognized and known factor of concern.

So we recognize that whatever is being modified based on the above measurement has no effect on factor A hence whatever is being measured is not factor A nor does it contribute substantially to factor A.

Yet still we want to understand factor A and be able to effect it at will. Other measurements are made and only of things we know we can improve. Nothing in our existing measurement framework seems to affect factor A.

Shall we continue? Shall we pursue other metrics? Shall we investigate until we can produce a “this = that” metric that has statistical validity?

“The basic premise of measurement is that you want to measure something so you can improve it, if necessary.” Thus the answer is no, we neither pursue nor investigate.

Nothing in our existing repertoire successfully produces a “measurement so you can change it” tool for factor A therefore we can conclude any of a) factor A doesn’t exist (even though it is recognized and known), b) it exists and is unmeasurable, c) the existing toolset does not include a tool with the ability to take a measurement of factor A such that changes can be made to the system that affect factor A, …

However, the fact that what is measurable (and actionable based on those measurements) is changing is quite true. The horizon of metrics meeting the initial conditions of measurable and actionable is, like the observable universe’s, growing every moment.

And I freely note that I’m a researcher, not a web analyst. I did ask one of our researchers, someone with a long history of lab work, and they offered that the basic premise of measurement is to determine the fundamental characteristics of what is measured.

“It’s pretty simple — collect data, analyze, improve. I love this because of its simplicity and objectivity.” But if the only data being collected and analyzed is based on factors we know we can change then there is no objectivity in the measurement or method involved. The only factors being analyzed are those recognized as changeable in the existing paradigm (the toolset).

Let me offer an example that demonstrates moving from measurable but unactionable to measurable and actionable: We’ve been intentionally collecting meteorological data for about 250 years. There was nothing in that toolset that could affect climate until recently. We also developed the ability to collect meteorological data going back far into prehistory (I can provide references to paleoclimatology if necessary. A quick search of Science provided some 1012 references going back to 1900).

Thus things were measurable. Without being able to measure them we could never have learned a) that they did indeed exist, b) that they were affecting us or c) how to affect them. Thus does our observable (web analytics) universe grow from measurable but unactionable to measurable and actionable.

“…you introduce a level of abstraction on the data. You “dumb it down” introducing bias and subjectivity.” I agree with this to a point and offer Communicating Science to Business and Vice Versa for your reading pleasure. I also recognize that all web analytics solutions offer training and certification on their toolsets.

Are these trainings and certifications offered to insure that all abstraction, bias and subjectivity are removed when a consultant or in-house analyst uses a given toolset? I want to take part in a web analytics tool class where the word “interpret” isn’t used.

“So what happens when you start combining metrics into uber-formulas …? That model breaks, because you introduce a level of abstraction on the data.” This also confuses me. Given a formula that has several elements and each element is known to be valid and measurable then the sum of the elements is also valid and measurable. This is basic mathematical logic (distributivity); if x is true and y is true and conservation of units holds then (x + y) is true. This principle (not by name) is mentioned in this post itself when the subject is RFM.

“First, what kind of return frequency is “often” - two visits? Four? Six? That’s subjective. What is “important” content? The home page? An article? A support document? Subjective again. And what is a “long” time on site — 5 minutes, 10 minutes? Perhaps “long” means any visit that exceeds the average for the site that week?” This logic reminds me of one of my favorite jokes:

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A man wants to know what 2 + 2 is so he goes to a mathematician and asks, “Professor, what is 2+2?” to which the mathematician replies, “How may decimal points do you want it out to?” He then finds a psychiatrist and asks, “Doctor, what is 2+2″ to which the psychiatrist replies, “Interesting. How did your mother treat you when you were two?” He then goes to an economist and asks, “Sir, what is 2+2?” and the economist answers, “What do you want it to be, my boy?”

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I do agree that the types of values you reference need to be defined, qualified and quantified (and my experience is that they usually are by the business model of the organization requesting the metrics to be applied) before being used in any measurement and resultant decision process. This is something I believe Nathan Janitz states in his comment(s). I also believe that to suggest subjectivity is not part of the business world — even when it comes to something as definite as well-defined measurements — is naive. To that point, suggesting that “often”, “important” and “long” implies subjectivity without recognizing that the conversation drawn from was not meant to have any rigor is amusing, hence my offering of the above joke. This goes back to the reference to RFM. If the underlying (or “principle”) metrics are valid then the uber-metric is valid. And I also recognize that my paradigm might not be appropriate here. I do think the core concept of measurement (of anything) is scientific in nature. Ms. Debbie Pascoe writes in her Continuing the Discussion with Joseph Carrabis “Anyone who has ever used a tire gauge or a tape measure has employed a scientific calibration method.” Hence I offer Paul Davies’ “A scientific claim is taken seriously only if it can be tested by others in a disinterested (not uninterested) way.”The true test of any measurement is that it can be duplicated when attempted under similar situations with similarly calibrated tools. That concept, I believe, is where true simplicity and objectivity will demonstrate themselves first. After that is achieved, companies that make measurement tools will find a way to make the measurements personally relevant to consumers (see Framing Science). This, I think, hearkens back to Ms. Pascoe’s comments and thoughts.Jim Novo offers “Simple to explain to just about anyone…” and I worry if our decisions on usefulness are going to be based on simplicity of explanation. If so, then everyone who doesn’t understand solid-state physics, magneto-interference pattern imaging, magneto-optical coding, lasers, satellite telemetry, … oh, heck, let’s just go to basic electricity… throw away your digital cameras, computers, pdas, smartphones, stop watching TV, move out of your homes completely, never again use cars, buses, boats, trains or planes, …

But (!!!) if the decisions on usefulness are to be based on simplicity of use? That’s a completely different story. My experience is that most people don’t care (or even care to know) how complex something is “under the covers”, they just want to know that “when I do this, that happens” and even more accurately “when I do this, I get what I want” (this is the history of technology concept I often share (see The Long Tail, Part 1, Rocks, Hammers, Competition and How People Get Left Behind or VerizonWireless’ 20 year plan)).

Bravo to some and not all of what Steve Jackson adds. In the end, any metric that provides value will survive, even if that value is simply that something is being measured.

I also appreciate Nathan Janitz statements. His statement “…you also can’t catch a mouse without looking at the right information….all of the right information” is, I think, exactly to my point.

Allow me to quote Joe Tragert, Director, Market Development, EBSCC Publishing, ‘The question isn’t ‘Which mouse trap works better?’, it’s ‘Did we kill the mouse?’” You can have all the metrics in the world available to you. And if your goal is still unachieved? Or if none of the available metrics address your goal? Then it is time to either give up your goal or create new and valid methods of measurement that help you achieve that goal.

Proving that Serendipity is doing it’s job, I’ve had in my mind that it’s time to return to these thoughts and several people contacted me to find out if I was going to return to this blog.

Okay. Into the deep end first.

My time away has been due to busyness. Perhaps some readers have heard, NextStage Received its first patent on its Evolution Technology. For years we’ve been intentionally below the radar, now we seem to be becoming a recognizable object rapidly approaching from the far horizon. Now that we’ve left nap-of-the-earth flying I’m able to discuss things more openly, me thinks, hence some of my responses now and in the future.

Are the visitors happy?

One of the things I did while I was away was talk with a few people (about 100 so far) about what I’ll call The Purpose of Web Analytics. I did this research because of something I wrote in this thread above, “…all these analytics are worthless unless they create happy, satisfied visitors, yes?”

I’ve talked with upper management in education, politics, at national telecoms, financial institutions, transportation, recreation, … a pretty diverse group. Most of them were involved in marketing products or services or some other form of gaining marketshare. None of them were web analysts or involved in web analytics except that they received reports and were expected to act upon them. None of them were particularly happy about being made accountable to a system that (they believed) wasn’t measuring … and here’s where the challenges really made themselves known.

What was being measured? Lots of money was being spent and lots of people were being told that the measurements mattered and as one fellow explained, for the amount of money they were spending they expected some consistency.

“What do you mean by consistency?” I asked.

He pretty much didn’t know. He and those with him said lots of things and it could be distilled to a general dissatisfaction that there wasn’t a single model that they could consistently use and derive actionable meaning from. The dissatisfaction grew geometrically when the discussion got into executives making decisions based on sales presentations rather than a given product’s specific informational abilities.

At one point I leaned towards a speaker and quietly said, “Remember, Joseph friend,” and everybody laughed because the tension in the room was broken.

I reference these anecdotes because one of my original hopes for this platform was an increase in understanding and acceptance of some mutual goals regardless of discipline or tool platform.

In the end, doesn’t it all come down to “…all these analytics are worthless unless they create happy, satisfied visitors…?”

If I can’t act on it, it doesn’t exist

The next item I wish to thread into this discussion comes from an online conversation I had with Critical Mass’s Christopher Berry about why web analytics seems to be a harder sell in Canada than in the US. You can follow my side of the conversation in Canadian Based Business Differences — Responding to June Li, Christopher Berry and Jacques Warren, Responding to Christopher Berry’s Vexing Problem, Part 3 post, The Language of Web Analytics - The Hard(er) Sell in Canada, Responding to Christopher Berry’s “A Vexing Problem, Part 4″ Post, Part 1, Responding to Christopher Berry’s “A Vexing Problem, Part 4″ Post, Part 2 and Communicating Science to Business and Vice Versa and links are provided to Christopher Berry’s side on the conversation in those posts. I’ll invite people to pay particular attention to Communicating Science to Business and Vice Versa because (and as Mr. Berry noted) the summation is what counts, “Business is different. Business (me thinks) tends to be more ‘Tell me how to use this’ hence most business proposals and reports start with Christopher Berry’s nuggets then go into explanations.”

My research is convincing me that (what I recognize as traditional) web analytics is going to be losing its authoritative power in the coming years. I think web analytics (and yes, this does go back to my original hopes for this blog) will evolve (just as anything will if it is going to survive in a given changing environment). What will it do and look like? I have some ideas, of course. Just ideas at present, though. More things to research before putting down on paper (or in a blog) at present.

This does tie into my comment re Avinash Kaushik’s “…we shouldn’t use ill defined engagement metrics as a proxy for something solid like a sale.” I’ve been an oft-times unwilling father-confessor to businesses frustrated by ill-defined metrics of any kind and wanting something that is justifiable a) financially, b) scientifically, c) arithmetically (forget mathematically) and d) produces some kind of “do A, get B”, “this-equals-that” link between action and outcome.

The comment I love about this is “If I can’t act on it then it doesn’t exist”, ie, it’s noise, a distraction at best and something best ignored. This was a wonderful statement used in a business practices discussion.

I’d really enjoy being involved in a web understandability/measurement/future usability discussion that has as its theme “If I can’t act on it then it doesn’t exist.”

“To measure and analyze on and offline behavior and then try to predict who to market to by figuring out what they think is not doable with one tool or one metric.”

I responded earlier to this comment. People who attended either the Toronto ‘08 or SF ‘08 eMetrics conferences are probably well aware by now that NextStage has patented a technology that can determine how someone is thinking through any programmable device. I won’t go deeper into the topic here except to offer a comment I posted on Jim Novo’s blog about the {C,B/e,M} matrix and its use in marketing and analytics.

Picking up where I left off with Jim Novo’s comments in this thread…

I finally had an opportunity to read Jim Novo’s Measuring Engagement and its related Framework for Engagement posts. I truly enjoy Jim’s writing style and the points he makes.

I especially enjoy and appreciate his referencing Relationship Marketing because it places people center stage. Understand people and you can both understand and predict what they’ll do. Watch only what people have done and you can only understand their actions in a specific historical context, you can only predict what they’ll do when the confluence of events that led to their original actions repeats itself. Exactly (and don’t hold your breath). Relationship marketing works at the question “…all these analytics are worthless unless they create happy, satisfied visitors, yes?”

Jim writes “The challenge with this model - and probably why it isn’t more widely known - has been the data, it’s a very analysis-intensive model…”. Yes. Agreed. If Jim (or others familiar with these concepts) is reading (or perhaps at the next conference we meet at), I think this is where being able to substitute cognitive heuristic models makes sense (see Liberation and Heuristics or Responding to Christopher Berry’s “A Vexing Problem, Part 4″ Post, Part 1. I’ve also written elsewhere that I often wonder why more businesses don’t make use of cognitive heuristic models).

For example, I’ve recently been applying heuristic models to helping adult second language learners increase their language acquisition abilities. That’s a traditionally very tough nut to crack and (so far, anyway) I’ve been able to isolate neural activity that tends to make adult language acquisition challenging. Example 2, using heuristic models in the above grew out of learning which heuristic models are used (non-consciously, of course) by which personality types in their decision making processes. This non-conscious heuristic model selection process is being integrated into NextStage’s Rich Personae. These and some other areas of my studies are intensely data-rich models that can be reasonably simplified via cognitive heuristics.

I also strongly like your concept of dis-engagement, although I tend to use a methodology that incorporates “satisfaction” into the scaling system (see Meet Online Engagement’s Little Friend, Satisfaction. I shared that the complete form of this during a discussion at the SF ‘08 eMetrics. It looks something like the figure on the right.

Some definitions to help in understanding; the x-axis is Engagement and is a measure of the amount of pleasure or pain an activity is giving you. If something is giving you either pleasure or pain to any degree your attention is focused on it, hence you are engaged by it according to the definitions documented in Attention, Engagement and Trust: The Internet Trinity and Websites. The y-axis is Satisfaction and is a measure of acceptance and rejection of some internal state and/or external event.

I believe what you are referencing as “dis-engagement” is what we recognize as the slide from high acceptance to “0″ acceptance. Note that this is not rejection (as rejection is an active negation of acceptance) it is a lack of acceptance. I appreciate that the difference might be subtle and I believe that difference is significant. Rejection — the active negation of acceptance — can be thought of as someone pushing something away. Zero-acceptance is the point where one can “take it or leave it” and the internal state and/or external event does not have any value assigned to it, hence doesn’t register strongly in the mind/brain.

Mapping this figure to real world experience, you always want visitors/consumer/etc to be in the first quadrant (where the green curve is). People are both positively engaged (they like what’s going on) and positively satisfied (they accept it gladly). Depending on what you’re selling you may or may not want people in those other quadrants. The second quadrant (bottom yellow curve) indicates someone focusing on painful experiences or information, the fourth quadrant (top yellow curve) indicates someone who finds pleasure in painful experiences or information. The third quadrant (red curve) is where visitors/consumers/etc often end up and marketers/businesses don’t want them to be — the former are actively psychologically and physically moving themselves away from a business/product/service.

I’ll offer that the above is also a reasonable representation of your:
1. Define / Measure Engagement – any way you want to, as appropriate for your business; whatever activity or combinations of activity you feel appropriate
2. Measure dis-Engagement – the absence of Engagement, as in the visitor / customer stopped doing whatever it is you define as Engagement for your business model

I think where the image above (and the math behind it) adds real value is with your “3. Take some kind of Marketing or Service action to slow or reverse the dis-Engagement with dis-Engaging folks” because it provides enough information to know how, exactly, visitors/etc are “right now” interacting with your marketing information.

I also agree whole-heartedly with your statements about predicting “dis-engagement”, etc. I would love to see the data you used in your example and apply it to the above. I’m willing to bet that satisfaction/acceptance was the real driver (and I won’t get into the depths of group satisfaction/acceptance states here (really, Joseph? You’re going to leave something out? Whatever for?)). I did get a kick out of your graph of email response rates falling over time. It was very similar to the results we found in our research on how to design an effective email newsletter. Bravo! I always love it when our findings match others’. Gives me hope we’re doing something right.

<ASIDE>
For what it’s worth, much of the rest of what you’ve written in your post is so close to what we learned in our email newsletter research that the overlap is astounding. Not surprising, I guess, as you’re listing an email-based experiment. It would be interesting to learn what else the rules we discovered pertain to. Let me know if would like to explore this.
</ASIDE>

You also list an implication about sending different messages to different segments. Yes, agreed. I believe the above allows for much more targeted and action-driven messaging (based on much of what I’ve shared above).

Perhaps, in the end, we’ve derived nothing more than a simplified mathematical model (complete with suggestions for better outcomes) of Relationship Marketing?

Whoosh!

Took me two days to put the above together folks. Sorry for the delay. More to follow. Soon.

Promise.

Despite being a newly minted Omniture customer I constantly find myself somewhat out of sync with the announcements the company makes about being the “first ever” and “industry’s only”. Usually I chalk this confusion up to their being excellent marketers and sales people and my being, well, not an Omniture employee. But last week I read a press release from the company that had me wondering if I’ve been working in the same industry as the nice folk from Orme.

According to this very well written press release, “Omniture Fusion(TM) accelerates time to action for their customers” and is “a new industry implementation methodology that provides customers with an explicit roadmap for taking action on their analytics data, allowing them to improve their business in weeks if not days.” The press release goes on to say that “Omniture Consulting’s Fusion methodology takes a proactive, customer-centric approach by conducting a thorough business assessment that helps to determine vertical key performance indicators and business objectives” and that “Omniture Consulting formed the Fusion methodology in 2007.”

Now, I’m not in Utah this week and so I can’t just find Matt Belkin or one of his guys and ask, but there absolutely has to be something I’m missing about Omniture Fusion(TM). I mean, doesn’t every vendor use this exact same methodology when they implement a for-fee analytics solution? The exact same implementation strategy I wrote about in my now classic book Web Analytics Demystified back in 2004?

I have to be missing something … so the conversation I’d like to have here in the Future of blog is about the future of how the technology is implemented into the business, not just he nuts and bolts of the web site.

Setting aside for a moment Ian’s bold prediction, let’s assume that vendors like Omniture, WebTrends, Coremetrics, etc. continue to provide for-fee analytics solutions that need to be integrated into the existing business infrastructure. And let’s also assume, despite Ian’s recent announcement that Gatineau has an automagic integration solution, that Omniture Fusion(TM) Certified Consultants, Omniture’s competitors, and their ilk in the private sector are still required to manage the integration process …

If this is true, what does that process look like in the Future?

Rene, in his typically prescient fashion, has already outlined OX2/LBi’s vision for how integration consulting will happen as our industry matures, citing various specializations. But what if Ian is more right than wrong, and what if the stuff we’re doing today with so-called “Enterprise” software evolves requiring integrations to be held to a much higher standard? What if instead of proprietary data-warehouses and unfortunately thin (or non-existent) APIs we used SAS, Terradata, Oracle and SPSS to understand visitor behavior?  What if the finance organization starts looking for real forecasting information from web analytics, not just crappy estimates based on recently collected data?

I’ll put a stake in the ground and say that nothing changes about the process (either the one Omniture formed in 2007 or the one I first described in 2004 after having implemented for WebSideStory since 2002, whichever you want) but that the work we do becomes increasingly and perhaps insanely complex. Think about the work you do today — whether you’re a vendor like Ian, a consultant like Rene, an industry analyst like John, or those of you who are practitioners — and how it will change if web analytics is not so much integrated into your CMS, but part of the true Enterprise infrastructure?

If this vision plays out, the business objectives become bigger, the KPIs cross channels, and the business assessment needs to be conducted by an entirely different class (or a differently trained class) of individuals.  Instead of well-meaning folks just out of college, the true Enterprise might start looking to global business consultants like Accenture, EDS, and IBM for their web analytics implementations.  Perhaps this is what Accenture was thinking when they bought Maxamine and Memetrics recently, who knows?

Anyway, I suspect Kristi Knight will call me tomorrow and explain what Omniture Fusion(TM) is really all about which will be nice.  But I will still be very interested in what you, the Future Collective, think about where integrations are heading as web analytics continues to grow up.

Eric T. Peterson
CEO, Web Analytics Demystified, Inc.
http://www.webanalyticsdemystified.com

Many of you will have read Rene’s very interesting post on this blog, in which he posits a world where there are only two web analytics tools to choose from: Google Analytics and Omniture. I’ve already commented on the post, and my remarks have been deemed to be of sufficiently high quality to merit an invitation from Eric to post a follow-up post here. I’m honored to do so.

The idea behind Rene’s post was that you don’t have to extrapolate the current rash of consolidation in the industry very far to see a future where there are only two solutions to choose from: Omniture at the high-end, and Google Analytics at the low. He goes on to speculate about the pros and cons of such a situation.

The post and its central idea got me thinking, and I realized that, despite the fact that Rene was only painting that view of the future as a means of initiating a kind of Platonic dialog about our industry, he actually hadn’t gone far enough. My prediction for the future of Web Analytics is as follows:

In three years, there will be no Web Analytics vendors at all.

Pretty bold claim, huh? Well, allow me to finesse that statement a little, and explain what I mean by “Web Analytics vendor”. My definition here is a company that makes the majority of its revenues from Web Analytics software and/or services. Today there is still a reasonable number of companies that fit that mold: Omniture, Webtrends, Core Metrics, Nedstat, IndexTools, and a bunch of smaller guys like CrazyEgg and Mint.

Note that I don’t include Google or Microsoft in this list, since neither of our companies will ever make more than a tiny amount from Web Analytics; for us, Web Analytics is a means to an end, a crucial component in a wider story which involves the selling of online advertising and the provision of software and services to make it easy for advertisers to buy this advertising.

This brings me to the real point of my prediction: In five years, Web Analytics will have been absorbed into other, allied disciplines (or will have absorbed them), so there will be no ‘pure’ web analytics vendors any more. Or, to put it another way:

There will be no Web Analytics vendors, but Web Analytics will be everywhere.

Hence the title of this post. The Web Analytics industry as we know it has reached (improbably) the autumn of its years. In just a few years it’ll be hard to find any company who really pays the bills from direct, old-style Web Analytics projects. But far from dying out, it’ll be easier than ever before to find Web Analytics software - it’ll be everywhere: in your ad server, in your CMS, in your marketing automation/CRM system, in your ad network. Companies will choose the Web Analytics that integrates best with their other systems (really, is a part of their other systems) rather than picking discrete, standalone applications on the basis of functionality.

This is already happening through the acquisitions that are taking place in the industry, and through the strengthening of Web Analytics capabilities in related disciplines. Marketing automation/management companies like Unica and Lyris (formerly JL Halsey) now offer web analytics as part of their offerings. Then there are the analytics capabilities of ad-serving tools like DFA, Atlas Media Console, and 24/7 Real Media, or ad networks like Tacoda and Advertising.com.

Content Management vendors such as Interwoven and Vignette also offer integrated analytics, and, in the case of Interwoven, MVT (through its acquisition of Optimost). Whilst CRM companies like Salesforce.com and Netsuite are adding more and more Web Analytics-like features. And, of course, the big portal/ad services companies: Google, Microsoft, and Yahoo!, each of which has Web Analytics embedded into the overall offering set (Yahoo’s capability is harder to spot, but is there - they acquired Keylime Software for this purpose several years ago). Even consultancies like Accenture are starting to get in on the act, acquiring Web Analytics-related assets like Maxamine and Memetrics.

At the other end of the market, Omniture is continuing to add capabilities which are moving it further and further from its core business of Web Analytics. Omniture will continue to absorb other businesses in related areas until the day comes when web analytics is only a minor part of what the company offers. We’ve already seen them do this with behavioral targeting (TouchClarity) and MVT (Offermatica). I predict that we will see Omniture acquire an ad server in the not too distant future. Why would they not, after all? Their value proposition to sites is that they can run and optimize their online marketing through Omniture’s services; having an in-house ad server would be a tremendous help in providing an all-up view of multi-channel marketing effectiveness (at the moment, Omniture has to reach complicated data-sharing deals with the likes of DFA to get hold of this data, or add cumbersome Omniture tags to ad calls).

As for the other Enterprise vendors - now really just WebTrends and CoreMetrics - they will have to go one way or the other; either acquire new capability to bolster their range of offerings, or be acquired. There’s a possible third way consisting of building very close relationships with some key third parties, to create a virtual version of what Omniture has done through acquisition, but it will be a tough road to travel by comparison.

What all of these developments have in common is that Web Analytics will increasingly become an enabling service which allows a company to provide a wider range of offerings - be it CRM/marketing automation, media planning/buying or content/site management. The “main” business (including consulting) will subsidize the investment in the Web Analytics software.

Which leads me on to my second bold prediction:

In five years, all Web Analytics software will be free.

“What?” you thunder. “Free?” Yes, free. I’ve posted before about what a miserable job it is making a living from Web Analytics. There are a bunch of reasons for this is that Web Analytics on its own is not really an annuity business - sure, most Enterprise vendors charge by the month these days, but there’s no established pattern of repeat business that you can build a truly reliable revenue stream on (this is the point, by the way, where the Enterprise vendors reading this splutter and immediately scroll down to post a rebuttal in the comments). The second main reason is that Web Analytics, for all its current glitz and glamour, is still really a minority sport. It’s a bit like Curling at the Winter Olympics - fun to watch for a bit, but most people get bored pretty quickly.

Much more reliably annuity businesses to be in are media planning and buying, or media representation, or selling your first-party ad inventory, or doing the kind of big-iron, multi-year projects that the likes of Accenture excel at. Those kinds of projects can be worth an order of magnitude more than you’d get from a pure-play Web Analytics implementation. But good analytics is essential to the success of these kinds of projects; so any company worth its salt getting into (or wishing to stay in) these businesses needs to offer quality analytics. The Web Analytics will be a “value add”. And do companies tend to charge for the thing they’re bigging up as the great extra thing that you get by working with them? No, they don’t. So Web Analytics will be offered as a free, tightly integrated and - and let’s be in no doubt about this - completely essential component of any online marketing-related offering.

So, at the end of all this, am I predicting doom and gloom for the Web Analytics industry? Hell, no. Things are just starting to get really interesting.

Ian Thomas
Director, Customer Intelligence
Microsoft Advertiser & Publisher Solutions
http://www.liesdamnedlies.com

Since the acquisition of Visual Sciences/HBX by Omniture, there have been tremendous discussions about the future of the Industry. Everybody seems more or less to agree that a concentration is taking place which is somehow normal as Web Analytics is a maturing industry.

Now let’s imagine for a second that this concentration continues and we end up with two solutions. What if all we had at our disposal was Google Analytics as a “basic” free tool and Omniture, the “enterprise” platform, serving the high-end of the market?

How would this landscape affect consultants and practitioners? Would it be a good thing? Or would it be the end of analytics as we know it today? I wanted to open the discussion regarding this topic and know what other people have to say.

Before I go any further, I want to disclaim that as founder and CEO of OX2 that has now joined the LBi Group, we are vendor independent and we thus have partnerships with many Web Analytics vendors including Omniture (through their certification program) and Google Analytics (being members of the GAAC program). This is my personal opinion and doesn’t necessarily reflect the official position of LBi.

That been said, let’s start the discussion! Take a moment and imagine the future I just pointed out. If we had just those two vendors how would that affect us?

On the one hand, I can think of positive effects:

• Training would be easier for consultants such as ourselves as we would have fewer tools to support and understand. Nowadays we have to understand how to get the same metric from Omniture (SiteCatalyst / HBX), Google, WebTrends, Unica, IndexTools, … which, to be quite honest, is often a pain with many vendors – not all - as their underlying documentation can be opaque and support doesn’t always seem to understand what we are actually talking about;
• Practitioners could more easily switch jobs as all companies would be using either one or the other tool;
• Other products such as content management systems, emailing systems, other internet related systems would more easily be integrated with the two existing WA platforms. And compatibility costs would be lower. But not only online software, this would be also the case for other tools such as BI or CRM tools, allowing a better understanding of the online activities;
• Having only two WA platforms would also allow benchmarking easier as we all know that putting 2 tools next to one another inevitably gives you grey hairs. This might push for standardisation, which in term would also mean that switching between tools might be easier. But here, I might be dreaming: each vendor has his own tags. Moving from one solution to another, when you’re working tag based will always be a nightmare.

But as everything in life there’s no yin without a yang. Let’s see some negative effects of this situation:

• As for many industries, a duopoly generally leads to a lack of innovation. Certainly if there is collusion at hand and as GA’s pricing model is different from Omniture’s one, they might have a shared interest in locking the market between their solutions. After all, competition is good. Just take a look at how vendors have been competing these past years to release more powerful tools and better functionalities to address the complexity of Web Analytics;
• If Omniture would be the only enterprise solution, prices would remain high while I strongly believe that WA tools will more and more becoming a commodity, putting downward pressure on prices. Don’t forget that a tool is just that: a tool and that you need people and processes in order to use them correctly, which are the most important factors in a WA project. We have customers doing great things with Google Analytics and I’ve seen very poor uses of expensive WA tools. Look also at Office suites, currently you could say that you have two main options: Microsoft and Star Office; Microsoft still sells their software at a very high price and they make margins of over 70%! If there was a real competition I bet that prices would be lower;
• Having just Omniture and Google Analytics wouldn’t/couldn’t suit every need. Not all websites are alike and we see it already today that a single tool doesn’t fit all. Take for example Coremetrics that focuses on retailers and seems to be doing a great job regarding this vertical. Look also at Unica that allows big corporations to integrate easily WA to Campaign management.

My opinion regarding this question is that it wouldn’t be good for the industry if we ended up with just 2 products (I’ve taken Omniture and Google Analytics as they are the two most important tools nowadays, but it could apply to any other). As I mentioned tools are just part of the equation, an essential but not an important part.

Getting back to Omniture and Google Analytics, the first still has some competition at the enterprise level, which doesn’t seem to be the case for Google Analytics. Let’s be honest, even if we are big fans of Ian Thomas, Gatineau hasn’t, for the moment, been able to be a big threat to GA …

For more on my perspective you can read some of my thoughts about the future of the Industry following the Omniture acquisition of Visual Sciences here:

http://webanalytics.ox2.eu/2007/10/27/omniture-pacman-what-future-for-visual-sciences/
http://webanalytics.ox2.eu/2007/10/31/how-the-web-analytics-industry-will-evolve-with-omniture-as-the-green-giant/

So what do you think? Do you have other things to add to the pros and cons? How would you see yourself in this scenario? Do you want to see a two vendor market, kind of like Windows versus Apple, or do you like the diversity of options we have before us today?

I am looking forward to having a spirited and honest conversation about the future of the analytics market and thank Joseph and Eric for giving us all a venue to chat.

René Dechamps Otamendi, CEO, OX2 (part of the LBi group)