In my presentation yesterday (thanks to everyone who came!), I mentioned a new definition of web analytics after seeing how lame the definition on wikipedia is:
Web analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage.
It’s not that this definition is wrong. It’s more or less technically correct, but it doesn’t focus on output, value, the weight of the actions associated (measurement, collection, analysis, reporting), and it’s just that it’s not marketable. For the purposes of wikipedia and the fact that the definition has to fit tools, people, an industry, and a practice, well…I can live with it there. But I was disturbed that “analysis” was only one of four verbs that fit the description, so I was hoping for an inspiring definition of “analysis”:
Analysis is the process of breaking a complex topic or substance into smaller parts to gain a better understanding of it.
Fail. I’m equally disappointed in this one. While this may again be true from the analyst’s perspective, I don’t think it captures the output or the value of analysis at all, nor does it accurately describe the true conclusion of analysis. Yes, analysis may consist of breaking things down into smaller pieces, but that is not what helps us understand things. The understanding comes once you begin to realize the ecosystem that is in place: how these various smaller pieces interact and influence each other.
And the real output of analysis is communication in simple terms, not understanding. It’s the spreading of understanding, in our business. It’s wonderful for us (the practitioners) to analyze and understand things, but completely wasted if we’re not able to convey this understanding to others in a variety of simpler languages specific to the audience.
So, here’s my new and improved (at least according to me) definition of web analytics that we analysts should use to market ourselves to executives:
Web analytics is an unbiased discipline that actively finds and validates business opportunities by studying the habits and behavior of users, competitors, and trends in the “big picture”.
This describes what web analytics is in terms of output and value (and process), not just in terms of execution. The reason I don’t like the wikipedia definition is because it does not touch at all on context or purpose, and to me almost comes across sounding like on-demand operational overhead, rather than a proactive, value-generating process. The context/purpose of web analytics is its service to a business: the identification and validation of business opportunities (both for the web and in other areas). I completely disagree with the notion that the output of web analysis is always web-centric, so I see no reason whatsoever to say that the purpose of web analytics is optimizing web usage. The output can be thousands of things from offline advertising, to pricing, to shipping carriers, to CMS re-evaluations, to compensation plans, to organizational charts and workflow and process, and on and on. Most often, the output probably will be web-centric, but defining web analytics as web-centric makes us far less valuable than we are capable of being.
Sorry…sometimes I get a bit over-passionate
It also includes the analysis of competitors, which Avinash has covered in incredible depth (listen to him!), and of course a constant ear on the rail of the big picture. The “big picture” really describes the greater ecosystem of both the Internet and your large-scale business tides. Without paying attention to the fact that the housing market is tanking, that twitter has exploded as a news source (or untamed brand trashing arena) in your industry, or that the price of production at your company has increased 20%, all of this “webby” stuff we bury our faces in all day really doesn’t matter. We are accountable to context, and this “big picture” view is where stuff like @comcastcares comes from, or should come from, at least. Juicy stuff.
Finally, saying that web analytics is for the purpose of improving/optimizing web usage or specific offline changes is really only a small part of the story. As I wrote in a Search Engine Land post, “The Real Value of Web Analytics,” making your site a better, more conversion-prone web asset is a wonderful outcome of web analytics, but the problem is that most companies are completely paralyzed by operational inefficiencies and departments that don’t work as a team. The best possible output of web analytics for most companies is what happens when they watch themselves struggle to actually execute.
Just like how doctors can put a dye in your blood to see valve issues and leaks in your heart, you can watch your company execute and see the issues in your processes and teams, which can teach you how to improve your company. When you can improve operations, you can improve your web site at the speed of light, and annihilate the competition.
Give it some thought. And write some of those thoughts here in the comments!