If you are a web marketing professional, ask yourself this question: How many times have I made a site design, direction or content decision because of my web analytics? Is the answer countless or almost never? If it s the latter it often is-, then you re probably one of the many people who d like to know what s going on.
Has your organization acquired a nice, shiny probably expensive- web analytics solution in the last year or two? No doubt there was great excitement as it was implemented. But how is it being used? Does it seem like the same old traffic reports you ve always gotten? Surely, you re thinking, there must be a better way. Surprisingly, these same questions and concerned are rife within the web analytics community. And there are, of course, countless theories why the practice of web analytics seems to be bearing so little fruit. Common explanations are: the tools still aren t any good, there isn t enough data to really do analysis against offline conversion, and the marketing organizations don t know how to use analytics. All of these explanations have some truth - but not one really holds up. Analytics tools have improved dramatically with no consistent improvement in analysis. More data is always good, but we re flooded with data not suffering a drought. And while convenient to blame, most marketers genuinely thirst for analytic direction. The deeper problem - one that s painfully obvious to anyone who sees the results from most web measurement - is that most analysis isn t based on any coherent view about how web analytics should be done. Without a consistent, conceptually powerful method for doing web measurement - many practitioners flounder. The simple truth is that web analytics as a discipline is very immature. With no central paradigm for how it should be done, the software solutions tend to be an ever widening grab-bag of reports and tools. And the richer the solutions become, the more confused the analysts! What s needed is a framework for how to do web analytics - and it has to more than a set of platitudes like measure to conversion or measure by customer segments. These platitudes beg the real questions: how do I do it, when I do it and how does it apply to my business. To be useful, a web analytics framework must provide: An easily understood way to think about web analytics A documented method to guide practitioners to consistently useful analysis A way to integrate web analytics into the rest of the business A common language to make web analytics comprehensible throughout and across organizations. There is such a beast. Based on the simple idea that each piece of a web site has a particular function and that it s effectiveness in this function can be measured by using statistics that are highly-tailored to its role, Functionalism provides a generalized framework for integrating analytics into the business and design process. To implement Functionalism, you start by classifying the important pages on your site according to their role. The methodology includes a rich set of page types that can be easily identified on most sites: Router Pages move visitors to appropriate sub-content-, Convincers get visitors ready to buy-, Closers get visitors to pull the trigger- and Re-Assurers don t worry, it s okay pages like privacy-. Part of the beauty of the scheme, conceptually, is that it gives everyone involved with the site a clear and formal way to describe why a page was built and what they want it to accomplish. But Functionalism doesn t end there. Each page type comes with a set of KPIs Key Performance Indicators- for how well the page is performing its intended function. So the analyst gets a ready made template for measuring the success of any particular page on almost any kind of web site. That wouldn t be much good if you can t implement the KPI s in your measurement solution. You can. Functionalism was developed to work with two of the most popular web analytics solutions HBX and SiteCatalyst- and it includes KPIs that can be measured in almost any solution. Functionalism provides a framework in which you can agree why a page is being built, what its outcomes should be, how to measure if it s successful, and how it compares to other similar pages on your site in terms of its effectiveness so you can decide which ones to try and improve. Measure for measure, it s a better way to do web analytics. Source: line56.coma>