Ever since I started working with Web Analytics in 2005, I was getting occasional questions about the “correct” setup of a Web Analytics tool.

In retrospect, the answer I had been giving was not considered satisfactory too often. It mostly came along the lines of: “It depends on what you want to know.” – which got me blank stares at best, and open hostility and hatred at worst.

What I hadn’t taken into account when formulating my answer back then was a perspective expressed by A.N. Whitehead, reading: “Civilization advances by extending the number of important operations which we can perform without thinking about them.”

One of the big mistakes that accompanied my thinking about Analytics – and, as I believe today, the single biggest mistake that every Analyst I have ever met had subconsciously repeated – was the notion that Web Analytics, as a discipline of insight generation, would have its purpose in increasing our degree of knowledge.

But since I’ve come to believe that most companies aren’t run by people with a favour for deep insights, but for comfortable ignorance, I have to revise my original claim.

So my revised answer is: “The correct setup for your Web Analytics tool depends on what you aim to safely ignore from now on.”

Sounds odd? It certainly does. But let me explicate a bit further.

In the old days of Web Analytics 0.8 (pretty much when the first log file analyzers hit the scene), “being found” was a major issue for any website to overcome. Back in the mid-Nineties, we had no Search Engines, just Indices, to get a bit of direction on the web.

The first Web Analytics tools were thus doing nothing else than confirming: “Yes. Your website can be found. Even from remote locations in the Pacific Ocean. Like Vanuatu.”

That was all nice and dandy for a start. But latest with the upcoming dot com boom, there was a huge amount of money flowing into the web. Brands claimed their space, and built their presence during the Nineties. The total amount of Internet users was growing – and so was the number of visitors to these newly created websites.

Over the time, new questions came up, like: “Which of our marketing campaigns was the most successful?”, or, more poignantly: “Did my marketing campaign do any better than that of Marketing Manager X?”. These questions were, at first, tied to content views as well, easily being answered by looking at the same volume metrics as before. But latest when monetary online transactions were introduced, the focus on “Web Analytics that matter” got seriously narrowed down.

Still, the degree to which people responsible for “online success” (in the broadest sense of the word) use Web Analytics figures always was (and still is) tied to support and feed on their underlying expectations.

Web Analytics’ simple implicit purpose is to confirm expectations, relieving them from the need for being spoken out loud: “Yes – you offer products online. People buy from you.” [done through “conversion tracking”], “Yes – people who have bought from you are buying from you again.” [done through “conversion tracking segmented by new/returning visitors”], and: “Yes – people who have visited your website through Facebook are turning into buying customers.” [done through “multi-channel visit-to-purchase funnel analysis”].

Web Analytics’ explicit purpose is to assign values, units and measures to the extent of success. “Purchases from returning visitors grew by 12.1% in the last period”, “Our brand preference index increased by factor 2 in June”, and the ever-so-popular “We were able to grow our Facebook fan base by 1200 recently” is both: happy news, and a quantified measure of success.

And, on a side note: Web Analytics data is as defining the level of ignorance that can be safely considered as “harmless” by an entire department or organization.

In other words: Web Analytics data enables and triggers affirmative communication about a subject matter that doesn’t need any further explanation. No matter whether you understand what a “Brand preference index”, what a “Facebook fan base” is, or how any of these metrics are measured or computed – you do inherently understand that “doubling” something is more impressive than a “12.1% increase”.

So, in any upcoming Web Analytics consultations, I will no longer ask “What is it you want to know?”. Instead I will ask “What is it that you take for granted, and what you want others to take for granted, too?”
Let’s see where that leads us.