Web analytics is now seen as a standard part of the site owner’s tool box and the data it provides has become a staple of web marketing.  

However, the technology and approaches underpinning analytics are moving on, but the market is failing to keep up to speed with these changes. 

For many marketers, web analytics is simply a reporting tool, giving you relatively static information about how many people have visited your site, what pages they visited and how long they stayed there.  

As such, it’s a useful yardstick of your site’s performance, but it’s not helping you to improve or enhance your site specifically.  

Now, however, new analytics approaches are taking a much more nuanced and pro-active approach to user data. By collecting new types of data, and cutting and dicing it in new ways, they’re becoming drivers of optimisation and enhancement, rather than simply after-the-fact reporting engines.

To illustrate this development, it’s probably instructive to take a look at the history of web analytics, how we’ve got to where we are and how site owners can make the most of the latest developments.

Stage 1: Introducing web analytics (1994-2004)

Web analytics has been around for nearly 20 years since the first true commercial Log Analyzer was released by IPRO in 1994. Back then the main goal was to track hits to your website, gathering basic metrics like Pageviews and Visits (or sessions).

As more websites were built, Marketers began to want access to analytics that allowed them to see how their website was performing and basic web analytics reporting was the best source of data. A new industry was developed and began to flourish. 

Stage 2: Polarisation of web analytics (2005-2009)

In April 2005 Google acquired one of the leading web analytics systems - Urchin – which, in November 2005, became Google Analytics. This was probably the biggest milestone in the industry’s history and changed the face of the market.

At the time less than 30% of pageviews were being tracked by an analytics product but, with Google's entrance into the market, this soon changed. They made a best-in-class product and provided it free to website owners, removing any barriers to entry for end users.

It’s now estimated that 90% of page impressions are tracked with some form of analytics, so Google's aim of getting businesses using data to better understand their marketing and web performance definitely worked.

What this free product did to the market was create two new sets of products. On the SMB side you had Google Analytics and opensource solutions like Piwik and, at the higher end of the market, a number of Enterprise class solutions rose to the top – most prominent among them Omniture, Core Metrics and Web Trends.

This polarisation of the web analytics industry did have a number of casualties who could not compete with this new Freemium industry.

Stage 3: Web Analytics cost of storage and processing falls dramatically (2010-present)

Over the last 10 years the ability to collect, store and process data has become more than three thousand times cheaper, driven by the principles outlined in Moore’s and Kryder’s laws.

This has created a new phase of growth in the analytics space, with new technologies and solutions being developed for a fraction of the cost of the more established Enterprise products.

Businesses can now get additional customer insights using new data tools such as heatmapping, surveys, session based analytics and AB testing solutions for a fraction of the cost of a traditional Enterprise web analytics system.

However, this explosion of new tools and approaches also creates a headache for marketers. These new and innovative solutions often sit in isolation to their core web analytics reporting tool, meaning that marketers are being hit with multiple silos of disparate data from non-integrated sources. 

This failure to bring together traditional web analytics and new approaches to web data measurement mean that opportunities are being missed by marketers.  

We still live in a world where Web Analytics is very much seen as a reporting function within a business and the ability to generate genuine, integrated insight to better serve users with more relevant and useful experiences is still not a reality.

Stage 4: Making Web Analytics truly actionable (2012-future)

As we’ve seen, web analytics is still very much perceived as a reporting function. However, with the cost to process big data from websites falling dramatically we believe it is only a matter of time before web businesses better harness this information to create more personalised experiences.

In order for this to happen, data about on-site activity needs to get more structured, better integrated and more focused around the visitor. By creating a unified, user-focused understanding of all types of analytics from your site, you can begin to generate real insights about what your customers want and need.  

Once you have your data in order, intelligence can be applied to it to help identify what best to serve to a visitor or group of visitors - whether that be on-site or off-site with technologies like re-marketing.

The businesses that will win the race online will be the ones that can keep up with their consumers and embrace the data revolution, not just as a reporting tool for management but as a way to uncover new opportunities for better customer experiences.

Ian McCaig

Published 15 October, 2012 by Ian McCaig

Ian McCaig is Founder at Qubit and a contributor to Econsultancy.

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Comments (6)

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Nick Stamoulis

Keep in mind that you can report yourself to death! Analytics are only useful as the person interpreting it and there are two sides to every set of data. You can pull just about any kind of report you want with a good reporting system, but don't let it drive you crazy.

almost 6 years ago

Steve Genders

Steve Genders, Head of On-Line at SessionCam

Interesting article Ian. I was running websites during 'Stage 1' and I remember the pain of getting people to take analytics seriously.
I thought you might have gone on to talk about Visible Web Analytics, that extra dimension that lets website owners actually watch users using the site, and then linking the recordings (or heatmaps) of visitors with the traditional analytics tool. Quite few people are popping up with solutions for this. To plug ours, it's SessionCam, but there are others trying too: Clicktale, Inspectlet, Tealeaf to name just three).

almost 6 years ago


Sarah Alder

Like Steve, I have lived through this history and it was interesting to see it summed up so succinctly. Sadly, I remember my team assessing Yrchin and rejecting it in favour of Web Trends. Hopefully that was a sound decision at the time of making it but it quickly became obvious that Google Analytics was better but we then had to extract ourselves from Web Trends. I also recall great corporate reluctance to rely on something "free", a caution I see repeated now when trying to introduce open source CMS systems.

almost 6 years ago


Adi A

Thank you for your insights, I would add the ability to connect the web analytics data to other data sources in the company to view a full picture of whats going on.

Take for example the funnel of visiting to first and secondary sale then to support etc.

almost 6 years ago


Simon Burton

It is welcome news for Web analytics professionals that the informed use of web analytics is becoming recognised with organisations as a key driver of increased business performance through better campaign optimisation and enhancement, rather than simply a sophisticated reporting mechanism. Web analytics professionals can use this new found recognition to ensure that they get heard, obtain the budgets required and become the superstars they were meant to be all along. I have written more on the subject here: http://goo.gl/9bE6N

over 5 years ago


GP Corsi

I remember when logfiles where all we had to go by for "web analytics" - tagging has sure brought us a long way but as other people here have stated, it's really about the person interpreting the data. That is the key to successful optimization based on analytics, the interpretation and formulation of a hypothesis to base the implementation. Good times =-)

over 5 years ago

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