Seven Deadly Web Analytics Sins    1    2    3    4    5    6    7

By John Marshall

Deadly Web Analytics Sin #4: Data Overload
Sometimes you really can have too much of a good thing. Too much data can lead to more than a slight headache—it can lead to data analysis paralysis, a condition that occurs when you have more data than you know what to do with or need.

Some web analytics vendors brag on how many reports they have...and that's where analysis paralysis begins. Smart marketers like you know that it's not the quantity of reports that matters, but the quality of the data they provide. Why waste your valuable time trying to decide which report to use or looking at data that may be interesting, but doesn't lead to any particular action? You shouldn't!

Gluttony and Decision-making
In the early days of the Web, when ad impressions were the predominant business model, the key metrics of success were traffic and page views. During this time, anybody with an idea for an online offering that generated traffic could get funding. Thankfully, those days of excess are long gone, replaced by strong business models and sound marketing decisions based on data derived from your web site.

The key to successful web analytics is the ability to make marketing decisions based on the data you're able to mine—the data alone is not enough to make you successful. A tool that provides a hundred reports isn't necessarily inferior to one that provides two hundred reports or more. It just means that one tool may allow you to work smarter rather than harder.

The Uncomfortable Feeling of Report Bloat
Report bloat stems from web analytics software developers thinking that if something can be measured, then it should be measured. Some of the more egregious examples of this mentality include Hits, Server Bandwidth and Top IP addresses.

Parting the Sea of Information
So how do we narrow our focus, letting us closely examine what's important while ignoring the background noise? The easy way is to look for a web analytics solution that cuts through the data clutter for you, like ClickTracks. But if, for whatever reason, you're forced to wade through the sea of information without the benefit of ClickTracks, let's take a look at how to get through some of the background noise:

First, take each report and map it to a business need by asking yourself the question, "What aspect of my online presence will I change with the information I learn here?" If you can't answer this question, whether it be because you can't affect change or you don't know what to change, don't be bothered with the data.

For example, knowing the time-of-day that your site traffic peaks is rarely useful, because you simply can't react fast enough to take advantage of the data. Knowing that traffic peaks at 2pm EST assumes you'll be waiting by your PC each day, waiting to flip a switch that changes something on your site, thereby taking more advantage of that traffic. You'll find that your time is better spent on bigger problems.

Next, try to get a handle on exactly what the report is measuring. For example, seeing referrals from Google could mean more organic search results (usually a good thing) or more paid search results (not as good as organic listings since you paid for them). When you see the number in the report, think how it's computed and therefore what's implied. In the Google example, you'd probably want paid and organic search to be shown distinctly, since it's possible for one to rise while the other falls. Ironically this means more data, not less—but it is data that can be used for making a decision, so it makes sense to include it.

Finally, try and find ways to explore your data dynamically. Even with a starting point of 200 preconfigured reports, you'll have difficulty finding insights that relate to your specific situation. For example, while site page views aren't necessarily valuable, knowing site page views broken out by keyword or some other dimension may yield more usable data. If you think measuring page views can lead to useful decisions about your site, start slicing the data and examine that metric from multiple angles. The analysis paralysis will abate, revealing previously unseen data delights.

Seven Deadly Web Analytics Sins    1    2    3    4    5    6    7