A web analytics client recently conducted some market research to help them better understand the needs of their customers.

What they learned, though not entirely surprising to me, helped to confirm what I see as some of the key issues facing analytics customers today.

Two things are increasingly clear: Firstly, Google Analytics is now by far the dominant analytics tool, with fully 94% of web analytics customers using it on their websites.

However what's also clear is that many customers are using other web analytics products in addition to Google, in some cases products which are expensive and complex, which indicates that even though Google Analytics fills a critical need there are at least some business requirements that it doesn't (or can't easily) fulfill.

What Products Do Customers Use?

The additional analytics products which customers use include Omniture (28%), WebTrends (17%) and Yahoo Analytics (11%), among others. There are a slew of lesser-known (but in some cases up and coming) products, such as CrazyEgg, Woopra and StatCounter.

We also found that 30% of analytics customers are not completely happy with their current analytics products, and a further 5% are actively dissatisfied with them. Clearly there are a lot of users out there who would like some better analytics.

Different Strokes...

For most analytics customers, the analytics products they have are used by a variety of different user communities, including management (78%), marketing (78%), and various technical staff (75%). In view of this, it makes sense that customers would need multiple toolsets, as those tools which are powerful enough for technical users won't always be easily usable by less technical business users.

The Benefits of Web Analytics

Web analytics tools are mostly used for the obvious reasons, including landing page analysis (97%), traffic patterns (92%), and website effectiveness (89%). However the key intangible value customers place on such web analytics are increased conversion rates (61%), enhanced website performance (44%) and better market data (42%).

So clearly the primary business reason that drives their use of analytics tools is to improve their website's performance and get more customers. That's not exactly a surprise, but it demonstrates that the real value of web analytics to most customers is to get more customers, not just more data. That's what "actionable intelligence" really means.

So What?

Putting this all together strongly suggests that there are two classic, somewhat opposing requirements at play here: On one side, the technical users of web analytics need a rich and powerful feature set to create more sophisticated statistical analyses and reports;  whereas business users want a web analytics package that can deliver actionable intelligence in an easily understood format - yet one that still allows them to quickly tease out the key pieces of information to improve their website and ultimately, capture more customers.

So the solution for many customers seems to be to use different web analytics packages to address the different needs of the multiple user communities within their organizations. And the companies that do so, are happier overall with their web analytics.

 

So, you have a website. Your site is accessible from various search engines and other linking websites on the internet. Visitors are finding your site and viewing your content. But how do you know your website is performing optimally? More specifically, is your website doing exactly what you want it to do, as effectively as possible? It's a fairly basic question but, judging by how difficult it is to navigate some websites, it's an often overlooked consideration.

Any website is inevitably created with certain goals in mind, tacit as they may be. For example, at a minimum it suggests the goals that someone actually views your website and understands basic information derived from its content. A website created for a company or organization likely has specific core goals related to business functions. Often companies and organizations have a lot at stake based on their websites' performance of these goals. For example, the viability of a company which only takes sales orders online depends on the viewability, content, layout, and order-placement functionality of the company's website.

 

Because so much is often at stake regarding website functionality, a process has evolved for evaluating website performance. Web analytics is the process of quantitatively measuring the number of visitors, performance, and overall effectiveness of web sites. Utilizing analytics tools – typically software placed on an organization's web server – web analysis provides quantitative measurements, called “metrics,” about the site's accessibility, design, content, and efficacy. Use of these metrics allows a series of specific, measurable, business-driven performance goals to be specified for any website. Performance against these goals can then be used as the basis for business decisions about the website and, sometimes, even the business itself.

Web analytics tools generate a series of statistical measures (for example, summaries and averages) about how the website is being accessed and navigated. Generally speaking, the purpose of web analysis is to create actionable information gathered from two realms of focus: external and internal. The statistical measures related to how users a website is accessed, linked to, located by users, or related to the internet generally is external web analysis. All other statistical measures and analyses related to how a website is navigated, its content and functionality as viewed by visitors, how long visitors view specific pages, why they leave the site, and how performance goals are being met within the website itself is internal web analysis.

Most web analytics tools (e.g. Google Analytics) are very effective at external analysis, however very few provide an adequate set of internal web analysis functionality, so it’s important to select a web analytics tool (or a combination of tools) that excel at both. For example, with a typical e-commerce site it’s critical to measure and analyze the number of visitors who leave the site either because they can’t find what they want to buy, or because they get frustrated with the buying process. With careful analysis, the reasons for this visitor “friction” can be determined and its impact significantly reduced (and measured quantitatively).

Once organizations define clear performance goals for their websites, web analysis can be extremely effective in not only assessing website performance and functionality, but also wider considerations like company marketing campaigns and programs. Even non-commercial websites may set specific and tangible goals for its visitors (such as user registrations, surveys completed, views of relevant information etc.)

The key to effective use of web analytics is establishing clear goals for the website and accurate methods of evaluating performance – especially as they relate to business functions. There's little benefit in collecting wide-ranging metrics that are irrelevant to the desired performance of the website. For example, web analysis that focuses on how visitors link to the site but ignores the sequence of pages viewed, and the steps a visitor performs, to successfully place an online order, misses the mark. Successful web analytics depends on integrating the relevant external and internal metrics as they relate to clearly defined goals.

 

Here’s a scenario I come across much more frequently than I want to:  In a meeting discussing a new marketing campaign at a medium sized corporation, a typical marketing manager will say something like “We should put the call to action above the fold and to the right of the graphic”. My response (other than a silent internal sigh) is always along the lines of “How do you know that’s better?”

All too often their answer will come down to one of three things, either “I read somewhere that…”, or possibly “At my last company we…”, or my favorite, “Our customers want…” All of these answers are bogus, probably wrong and quite possibly harmful to the potential success of the campaign and also the company’s business. If they were really being honest, in most cases what this person should really say in response to my question is “I don’t know.”

The reason is that many otherwise smart people don’t use hard data to verify their assumptions. They don’t test those assumptions by running comparisons of the different possible approaches, or they don’t use the data they collect usefully, and they almost always don’t know their customer audience as well as they think they do.

The reality is that people are fickle and unpredictable, particularly customers, and particularly customers on the Web. What worked for one company and one product might not work for another; what worked last month might not work today; and how you think users will behave is not always intuitively obvious. That’s why it’s critical to always test, measure, and analyze any assumptions you make about your customers.

Of course in reality there are certain general principles about website layout and campaign management which hold true in most situations, most of the time (particularly with large, well-defined audiences), and experienced web marketers and designers know this. But it really doesn’t hurt to be sure. More than once I’ve seen seasoned web professionals eat their words when an analytics report disproved their pet theory… They didn’t know what they didn’t know, and some cases what they didn’t know can (and does) hurt them.

Part of the problem is that most marketing folks aren’t scientists and aren’t trained in the use of the scientific method, so the idea of constructing an experiment to determine the best ways to persuade their customers either doesn’t occur to them or is sufficiently daunting a task that they don’t know how to approach it effectively.

Another, bigger problem is that many of the software tools out there supposedly designed to help users test and measure website results are complex and ugly, and are really aimed at highly-skilled technical users with statistical and programming expertise (not a description of the average marketing manager). If it isn’t easy and obvious to obtain the analytics you need to make good decisions, then you’re probably making bad ones.

What’s needed in many companies and marketing departments is a mindset that it’s great to be creative and think outside the box, but always test (design A/B tests for all your assumptions), measure (using web analytics), and analyze (use analytics software that’s easy to use and presents information in a digestible form). Most importantly, use the analyses you create to drive your collective marketing and website decision making, so you’re always on solid ground and maximizing the effectiveness of the dollars you spend on both.

Because otherwise what you don’t know can hurt you… and it will, if you’re not careful.