Web analytics systems have been around for almost 10 years, yet the majority of online businesses fail to make use of them. It has only been in the last year or so that people have even felt the need to measure what is happening in their sites. My own research has led me to conclude that the most common reason people want web analytics today is not to improve their business, but to give their boss (or client) a report.
While people are starting to get the numbers, almost no one is actually doing anything about them. This was an issue much discussed at Jim Sterne's Emetrics Summits in 2005. By the 2006 summit most attendees expected things would have moved on, but we were all surprised to see that nothing had really changed a year later. In my view this is just plain nuts.
"Navigating by sight alone was adequate when man began sailing along the sea coasts, but once men set out across the oceans new tools were required to navigate successfully. Those without the right tools drowned."
-- Bryan Eisenberg, "The Guide to Web Analytics" (2002, Future Now, Inc.)
During the early stages of development, the emphasis in web analytics was on "actionable metrics," numbers which we could use. Network engineers built early web analytics systems, and these systems swamped marketing and sales people with useless information.
Those days are gone. Most web analytics systems today will focus attention on the key business metrics and present them in an easy-to-understand format. However, it's not enough just to have the numbers. We have to use them. On the other hand, in order to use them, people have to know how to use them.
What's the web all about?
Knowing how to improve online performance requires a clear understanding of what's going on in the web.
First and foremost we need to understand what the web is about. Please read this carefully: People click on words.
That's the essence of the web. Flash, banners, graphics, page design; these are all enhancements designed to encourage people to click on words (hyperlinks). Online sales and marketing is little more than a set of processes aimed at getting people to the ultimate click-- the "submit" button on the credit card processing form.
Web analytics systems tell us what's being clicked on and who's doing the clicking. Our job is to work out why and what to do about it.
In order to understand what web metrics mean we need to understand the processes our visitors move through during an online sale. First people have to discover our site. When they arrive they briefly scan the site to see if it's what they're looking for. If it is they interact with it (read pages, click links, use search facilities). Finally, if we're lucky, they will convert into a sale (this is called an "acquisition").
These four phases of discovery, scanning, interaction and acquisition are covered in more detail in my article "Framework for Performance Assessment." Each phase can be analyzed for costs, returns and performance. Multiple metrics are available to assess each phase, and these can be used to improve the performance of that particular phase.
A website is not a thing
The critical fact to bear in mind throughout all of this is that these phases all consist of the same thing-- presenting a series of web pages to someone. It's not possible to get a sale with a single page. When someone has finished looking at one page they have to decide whether to click on a (single) hyperlink in that page or leave. They can't click multiple hyperlinks, and they can't sit there passively forever. They must actively choose one action. They must exercise choice.
A website is composed of web pages. On each page the visitor makes a conscious decision whether to click or leave. Each page is thus a sales pitch, and every page view ends in success or failure. People do not come to your website-- they come to your pages. They will probably not see all of the site, and they can move from one site to another without noticing if you design it that way. Thus a website is an abstract concept.
It's fine to fiddle with ads, tuning delivery and audience mix, but this is of little use if the site is letting you down. Having a great website is the secret to a successful online business. The best ads in the world will achieve nothing if people won't enter the site.
Analyze page by page
This means you have to think of your site not as a black box, but as a sequence. We can think of it as a conversation. Your site says something to the visitor and the visitor either replies "tell me more" or leaves. Web analytics is about finding out what the site said to whom, and how they reacted. Applying web analytics means changing the site as a result of what you discover.
The number of people who are changing their site in response to what their web metrics are telling them is so low as to be un-measureable. We need to cease designing sites in a way that makes it difficult or costly, or even a skilled job, to change them. Once the site is built, that is not the end of the matter. While most organizations recognize the need to change content, and therefore have content management systems, few of them drive this with structured processes or any form of analysis.
Rather than examine the site's overall conversion ratio, each page needs to be analyzed individually. After all, we don't change the site-- we change individual pages. Obviously we can't look at every page, but luckily we don't need to. Certain pages are critical: entry pages, major exit pages, forms, shopping carts, and a few main content pages that are directly involved in a specific marketing or sales process. These are the pages that need attention. They may vary from month to month, but it's usually pretty obvious. If we launch a new product, and the page listing for that product suddenly becomes our biggest exit page, we know it needs attention.
It's rarely informative to examine a page's overall stats. A page will work differently for different audiences, and averaging this will only obscure what we need to know. For example, a "special offers" page on an airline site may work wonderfully for people searching for "cheap deals" and yet be a disaster for people searching for "first class flights." If the "first class flights" visitors form only a small percentage of the visits, losing them may not even drag the stats down to a noticeable degree.
The best method is to break the audience performance down according to their sources. Sources tell us what people were looking for. People come to every website looking for something specific. They will stay in that site only while they believe it can satisfy their goals. If we know what led them to the site in the first place we understand their ultimate objective. This is easiest if they came from a search engine, because we can see what phrase they used. That's their goal. The most common reason they leave a site is that they ceased to believe the site could satisfy their goal. The next most common reason is that, while they still believed the site could satisfy their goal, the amount of effort involved became too high. For this second group we need to see which page (or pages) they came from within our site before they exited.
Leverage the greatest effect for the least effort
It's easy to be overwhelmed doing this form of analysis-- there's so much data. Even a small commercial site will have several thousand different search phrases generating visitors, and hundreds of different paths. If we think about it, this is similar to the web. There are millions of websites, and billions of links between them.
So how do we manage to avoid being overwhelmed on the web? The answer is simple-- start with a goal. Follow the scent of that goal and ignore everything else. The same thing applies in web analytics.
Before you start a session of web analysis, decide in advance what you want to know, then follow the scent. This means you may have to allow for discovering the unexpected, and some associative thinking. For example, if you've just launched a new PPC ad, what's the bounce rate on its landing page? How many of the people who click through from it make a purchase? If most click through, but few buy, on which page do they drop out? How does the drop-out rate on this page compare with that for people from other ads?
If you're not sure where to start, start at the back of the conversion funnel-- the shopping cart and the credit card submission forms. Improving the performance of these pages ripples back through the entire system, improving the ROI of everything which has gone before. In addition, since these are a small number of pages, you'll get the greatest impact for the least work.
Web analytics are there to be used, not just so we can make pretty charts for our reports. Using them means changing the site in response to the things you discover. A website is composed of many pages, each of which can be analyzed and tuned individually. This means we should not consider the launch of the site as the end of the design process, but as the beginning.
A website should not be seen as a static monolithic object, but rather as a dynamic process composed of many components. The purpose of web analytics is to examine and improve the fit between each component and the visitors who view it.
Brandt Dainow is an independent web analytics consultant. Read full bio.