ERIC T. PETERSON
All Blog Posts
Subscribe by Email
Subscribe by RSS
Archive for 'Web Analytics Business Process'
Today I am really excited to announce the publication of our framework for mobile and multi-channel reporting, sponsored by OpinionLab. You can download the report freely from the OpinionLab web site in trade for your name and email address.
This paper builds on our “Truth About Mobile Analytics” paper we published with our friends at Nedstat last year and focuses on both measurement in mobile applications and, more importantly, a cross-channel measurement framework built around interactions, engagement, and consumer-generated feedback.
- Interactions occur in every channel, digital or not. Online and on mobile sites we call these “visits” (although that is a made up word for interactions); in mobile apps the interaction starts when you click the icon and ends when you click “close”; in SMS it starts when you receive the message; on the phone it starts when you dial, and in stores interactions start when you walk up to an employee.
- Engagement is simply “more valuable” interactions. Regardless of your particular belief about the definition of engagement, we all know it when we see it. Online it happens after some number of minutes, or clicks, or sessions, or whatever; in mobile apps it happens when you’ve clicked enough buttons; on SMS it happens when you respond to the message; on the phone it starts when you begin a conversation, and the same is true in a physical store. We say engagement is “more valuable” because without engagement, value is unlikely to manifest.
- Positive Feedback happens when you do a really, really good job. Measuring feedback is a critical “miss” for far too many organizations. Apples “app store” and the value of the star-rating system has essentially proven that there are massive financial differences associated with positive and negative experiences … but most companies still make the mistake of ignoring qualitative feedback altogether.
These three incredibly simple metrics can be applied to every one of your channels, your sub-channels, and your sub-sub-channels (if you like.) When applied you can create an apples to apples comparison between your web, mobile web, mobile apps, video, social, etc. efforts.
Then you can apply cost data, and you’re really in business.
I don’t want to say much more than that but I would really, really encourage you all to download and read this free white paper. When we put something like this out — something we believe has the power to really transform the way everyone thinks about the metrics they use to run their business, and something that has the potential to force dashboards everywhere to be scrapped and started over — we’d really like your collective feedback.
DOWNLOAD THE WHITE PAPER NOW
Thanks to Mark, Rick, Rand, and the entire team at OpinionLab for sponsoring this work. If you’re the one person reading my blog that hasn’t seen their application in action, head on over to their site and have a look.
Years ago Google’s Analytics Evangelist Avinash Kaushik told everyone “data quality sucks, get over it” which at the time was quite the funny and controversial thing to say. Among other things Mr. Kaushik encouraged his readers to “resist the urge to dig deep” to understand data-related problems, to “assume a level of comfort with the data” and to focus more on trends and less on absolutes.
At the time this advice seemed good. Any number of companies were in the midst of switching vendors back in 2006 (a trend that has noticeably declined) and so guidance to not stress out on the differences observed between old system “A” and new system “B” was good, as was his encouragement to spend more time focusing on data quality in key areas (checkout, carts, etc.)
Unfortunately times have changed.
Since 2006 we have seen a slow but steady increase in the prominence that digitally collected data has within businesses of all sizes. Now in 2010, more senior managers, Vice Presidents, and CEOs than ever are incorporating both qualitative and quantitative data collected from web, mobile, and social sites than ever before. Among our clients we have seen a profound shift from “nice to have” to “critical” when it comes to data flowing through Omniture, Coremetrics, Unica, Google Analytics and other systems, and slowly web analytics is becoming an embedded component of business decision making.
While this shift has far reaching implications lately at Web Analytics Demystified we have been looking more closely at how we can help our clients not “get over” the “suckiness” of data quality and actually do something about it. We are doing this for one simple reason: senior leadership doesn’t want a glib response to data quality issues, they want as high a level of accuracy possible and concrete answers for why that accuracy isn’t forthcoming.
Don’t believe me? The next time your boss asks about the quality of the numbers you produce look them squarely in the eye and repeat Mr. Kaushik’s words, “Well Bob the data quality sucks and so you should just get over it, okay?”
When you’re done you can call my friend Corry Prohens to help you find a new job.
The alternative is, of course, to actually pay attention to your data’s quality and work diligently to incrementally improve data collection processes. Rather than be lazy about the very foundation of all of your valuable work (and the high-quality analysis you’re working to drive into the business) you can do a few simple things designed to make your data “less sucky” and thusly more valuable.
And what are those things? Thanks to our friends at ObservePoint we have authored a short white paper on this exact subject! Titled “When More is Not Better: Page Tags” and subtitled “The Dramatic Proliferation of Script-based Tagging and the Resulting Need for a Chief Data Officer” (okay, not my best title, I admit it) the paper outlines the business processes and technologies required to develop a little more trust and faith in your digitally collected data.
The paper is a free download from ObservePoint but you will need to trade some information with them. I can assure you Rob Seolas and his team are fine folks and given that they have a tendency to send out sweet USB devices to prospects there are worse things than having someone from ObservePoint call.
Get your copy of “When More is Not Better: Page Tags” at the ObservePoint web site today!
If you’ve read the paper I welcome your comments. While we recognize that few companies are going to appoint a Chief Data Officer to manage their digital data quality we hope that our readers understand that the point is not the job title but rather the work the associated work. Our thesis is that as the push towards digital continues those companies who have (and can communicate) a high-level of trust in their data will gain a competitive advantage, and in a world where the competition is always only a click away, who doesn’t want every advantage they can create?
When John was with Forrester Research last year he had the opportunity to do some work for Google that published some pretty bold claims. Among these was his reporting that “a staggering 53% of enterprises surveyed currently use a free solution as their primary Web analytics tool, and 71% use free tools in some capacity (PDF from Google). At the time I commented:
“When [John] 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.”
Increasingly my new partner is looking like some kind of prescient seer, although perhaps not for the reason some of you expect. Without a doubt Google is pushing hard to improve their analytics application, and by nearly all measures they are doing a phenomenal job. As I said back in November I personally believe their “Analytics Intelligence” feature is brilliant, and I have little doubt that we’ll continue to see little improvements here and there over the coming year.
But as much as I love Google Analytics for what it does, I am also willing to be honest about what it does not do and what it is not. Google Analytics alone is simply not enough for truly sophisticated web analytics.
Despite John’s findings at Forrester, and despite the fact that Google Analytics is easily the most widely deployed web analytics solution ever built, there are clearly limits to what Google Analytics is capable of today. What’s more, there is nothing wrong with having limits … what is wrong is trying to be all things to all people, which is what this post is really about.
At Web Analytics Demystified we have been talking over the last six months to an increasing number of companies that are considering dropping their historical vendor, almost always in favor of Google Analytics. And at Web Analytics Demystified we don’t do that much work with small, mom-and-pop shops … these are global organizations, name brands, and market leaders in their respective categories. Most of these companies are spending well-over $500,000 per year on analytics technology, and a handful are spending double that.
What’s more, all of these companies have multiple dedicated resources for web analytics. These companies, in many cases, are freaking awesome at putting their tools to work, and in all cases understand that web analytics is a “people” thing, not a “technology” thing. So what the heck is driving them towards Google’s waiting arms?
It turns out there are limits to the amount your average business user is willing to invest in learning web analytics tools. As more companies begin to truly take a strategic approach towards web analytics, many of them are realizing that their business users are simply not “getting” the fee-based solution they’ve invested so heavily in. The business users have more or less found their limit, and hit the wall, and are balking at the amount of time it actually takes to learn and become proficient with these tools.
Apparently in an effort to further differentiate themselves from Google Analytics, the paid guys have inadvertantly made their technology so complex that few people in the business are actually willing to use it.
Having worked at WebSideStory in the past I have to admit I cringed when one business user complained that [market leading vendor X] “was just too complicated” and that she “really, really missed using HBX because it was simple.” But this is a story we are hearing over and over and over … to the point where I am having to revise my entire opinion about Google Analytics place in the true Enterprise, which I’m happy to do …
A problem with the wholesale shift to GA arises when we go to the dedicated analysts and consulting teams who actually do get and use the paid solutions, pretty well in most cases. Suggesting to them that they might try and get by solely on Google Analytics is kind of like telling LeBron James that he needs to do his job with only one leg, one arm, and a blindfold on if he wants to keep playing ball. He’d probably still be able to drop 30 on the Knicks, but he certainly wouldn’t be happy about it.
I don’t personally know a single analyst worth their weight in salt who would be happy and willing to standardize completely on Google Analytics, at least not today. Despite awesomeness galore, the decreasing list of things that GA doesn’t do is pretty important to these sophisticated users. True visitor-level segmentation, real flexibility in reporting on custom data, the ability to define custom metrics and dimensions, true data integration … it’s not an infinite list, but it’s still pretty long by my estimation.
And while the list of third-party applications to provide additional functionality to Google Analytics — for example ShufflePoint and their wonderful use of the GA APIs — we have still not seen a solution emerge that confers all of the necessary functionality that “professional” web analysts need to do their jobs well. In my experience there is nothing worse than knowing how to answer a question but not have the tools in place that you need to make the necessary connections.
Notice that I’m not saying that the alternatives to Google Analytics necessarily have these features. You don’t have to spend much time following the Web Analytics Forum or the Twitter #measure tag to see complaints about how hard it is to do stuff that should be pretty simple. But for the most part this rich functionality can be found in the add-on ad hoc exploration tools (e.g., Omniture Insights, Coremetrics Explore, Webtrends Visitor Intelligence, Unica NetInsight, etc.), and it turns out that when you’re competing on web analytics these features are pretty important.
So what am I saying, and what did I mean by “bifurcation” in this post’s title?
I believe that we are about to see an increasing number of companies in the coming year drop their paid vendor’s “basic solution” in favor of Google Analytics and, at the same time, seriously consider adding their vendor’s high-end offering. More specifically thanks to advances in “universal” (sic) tagging, the increasing cry from business users to “get us something simple that we can use”, and the true and present need for experienced operators to have a robust data exploration tool at their disposal, I think we’ll see an increasing number of “Google Analytics + Omniture Insights” implementations.
- I am not saying that all companies will drop their paid vendor in favor of Google Analytics, mostly because “all companies” never do anything;
- I am not saying that all companies should drop their paid vendor in favor of Google Analytics, or even that companies should drop their paid vendor at all, especially if you have a pretty solid web analytics strategy in place;
- I am definitely not saying that I believe companies can manage a sophisticated web analytics operation using Google Analytics alone, although this statement hinges on the definition of “sophisticated”;
- I am not evangelizing for Omniture Insights, even thought I used to work at Visual Sciences and continue to use OI thanks to the good graces of Omniture/Adobe;
- I am not evangelizing for Google Analytics, even thought I do think the GA team has made amazing advances over the last 12 months;
The final caveat is that I am only using Omniture Insights in the description below as an example — you can substitute any of the solutions I listed above just as easily, or even use SAS if you’re ready for the coming revolution in web analytics. Heck, if you’re super-motivated, you can take Hiten Shah of KISSmetrics suggestion and build your own clickstream data warehouse and analyze the results using Tableau.
Regardless of the technology you choose, the bifurcated solution looks kind of like this:
- For your business users you simply do an awesome job implementing all the great new functionality present in Google Analytics;
- At the same time you add whatever other information you need to pass to OI, either because GA can’t handle it or you’ve filled all your custom variables;
- In OI you transform the data to match what GA is doing as closely as possible, knowing full well the data will never match because of GA sampling and the reality of our industry;
- In OI you add to the data with whatever you need, either via transformation, lookup tables, custom metrics and dimensions, whatever …
With these technologies in place you now have two things:
- A very appropriate solution for your internal business users, one they will likely embrace thanks to it’s simplicity, it’s beauty, and it’s Googliness;
- A very powerful solution for your web analysts that is largely based on what your business users are looking at.
The way the solution set works practically within the business:
- Business users get training on Google Analytics, which is surprisingly easy to provide, and if you’re big enough the rumor is that Google’s own evangelist will come visit with you (fun!)
- Business users get used to the idea that the numbers in GA are not 100% perfect, especially in high-volume situations where GA is sampling;
- Business users follow the age old advice to “manage based on trends” and use some of the slickness that is GA to identify problems and opportunities;
- When the business finds something interesting they ask the analytics group to work with them to look more closely and provide analysis (not reports);
- When the business needs “more accurate” numbers they ask the analytics group to provide reporting from the complete set of data (normal accuracy and precision caveats still apply);
- With their newly gained free time, the analytics group can become more of a proactive analytics service organization and less of a barrel full of “report monkeys”;
Yes this involves some internal education, but c’mon people, all web analytics involves internal education. You’ll need a clear explanation about the “goodness” of the GA data in high-volume (e.g., sampling) situations; you’ll need to provide training on Google Analytics, but there are some amazing people out there who can help you; and you’ll need to manage two vendor relationships … although if John’s data from Forrester is correct, 71% of you are already doing that!
Clearly this solution is not without risks, but from where I sit, I am having more and more trouble putting together a viable and workable alternative. Web analytics is becoming an increasingly critical function across the Enterprise and awareness of the solution set is bubbling up more rapidly than ever. As this happens, an increasing number of internal stakeholders are starting to ask for direct access to web data.
But the fee vendors, again for pretty good and obvious reasons, have evolved their base solutions to an unprecedented level of complexity, especially when you look across many vendor’s “complete” base solution (e.g., Webtrends 9 plus the requirement to use Webtrends Live for a lot of stuff, Omniture SiteCatalyst plus Omniture Genesis, etc.) Nobody is blaming them for the push upstream … especially since nobody I know could think of an alternative to differentiate their solutions from the 8,000,000 lb gorilla that Google Analytics has become.
At the end of the day in many, many cases you end up with business users frustrated by their inability to effectively and efficiently self-serve, and analytics professionals frustrated by the amount of time they spend pushing out basic reports. Quickly the situation becomes what is politely described as “inefficient” or, in more colloquial terms, FUBAR. You choices are then to A) lump it and suffer or B) do something about it.
I’m not a big fan of suffering.
The bifurcated solution, if you think about it, is actually pretty awesome. You get the best of both worlds, and one of the solutions costs you nothing and so (hopefully) frees up budget to hire more and better people to manage your web analytics efforts. I’d rather see a company put $500,000 equally towards an ad hoc analysis engine and smart people to run it than the case I see too commonly today where the lion’s share of that $500,000 going directly to buy a solution that is not meeting the needs of the business.
What do you think?
If you’re a company of any size and history of investment with any of the big U.S. or European vendors, and if you’ve been considering something similar, we’d love to hear from you. While we’re already providing guidance to some pretty large clients making this move we are always eager to collect additional data as an input to our thinking. We’re also happy to hear from consultants and vendors who have a clearly vested interest in the outcome I’ve described. And yes, if you need to bitch at me for suggesting that Google Analytics is anything short of manna from heaven, I suppose I’ll approve those comments as well.
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:
- 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 …
- 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.
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:
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!