Web Analytics: Measuring Success By the Click

As appeared in the Presence Pointers column of the December 2008 issue of “Business Watch” magazine.

Is your Web site successful? Was this year more successful than last year; this month more or less successful than last month? These are important questions to try to answer. If you sell online, success might be based on selling more. This may be a start, but it may not be the entire picture. If you don’t sell online, this may seem even more daunting.

The beauty of the Web is that you can gather and measure more information to answer these and many other questions much easier than you can in the offline world. For those who feature both a brick-and-mortar location and an online presence, there are even powerful ways to connect the dots between the two.

Why Measure?

Measuring site performance is important because it helps understand what works and what doesn’t. It helps determine where to focus efforts and resources. What days of the week or month does the site get the most visitors, and perhaps more importantly, sales? And are the two correlated in any way? What other sites send traffic? Are people finding what they are looking for on the site?

The right tools, approach, and a little planning can even turn your Web site into a tool for measuring the effectiveness of your offline marketing. For instance, which papers or magazines send the most traffic? Is the Sunday paper more effective than any other day?

How to Measure?

There is a myriad of performance tools you can tap into. At the most basic level is log file analyzers and basic Web stats programs. These provide a basic glimpse into various metrics, but for more useful information, you need to step up to a Web analytics package.

Web analytics packages run the gamut from highly complex to rather simple tools, and from several thousand dollars to free. In fact, one of the most popular and fairly powerful tools on the market is offered for free from Google.

The complexities of implementing Google Analytics on a Web site may vary. Most sites though can probably get by with simply copying and pasting in a bit of code into each page (for static sites) or into the page templates (for dynamic sites).

What to Measure?

As you’ve gathered, there is an endless supply of metrics and data points that can be examined. What these are and which ones should be monitored vary from site to site and may depend on whether it is a large site or a small one, ecommerce or otherwise, and so on.

There are some metrics that are universally important though. If you are just getting started with analytics, then these are the best ones to start looking at first. Start by looking at the visitor patterns — what days are high days for traffic versus low days? Then look to see the makeup of how visitors get to your site — what percentage of traffic is direct versus search engines versus other sites (referral traffic)? Then look at which pages get viewed the most? Then look at the keywords that send search engine users to your site.

That should keep you plenty busy for awhile. As you become more comfortable with your analytics package, you can start to drill down into each of these areas (and others) in more detail. You can even find out what keywords drove traffic from the Yahoo search engine on the second Tuesday to a specific page if you’d like.

Web analytics can be extremely powerful and is a tool that every site owner should tap into. Like any tool though, wielded improperly, it can do more harm than good. It’s easy to draw correlations in data that aren’t there. Web analytics tools are less about providing answers, and more about providing information to help answer questions, and often, identify new questions to ask.

Maximizing Measurements

  • Double check any assumptions made from the data collected.
  • Don’t make correlations or connections between data that isn’t there.
  • Use the analytics package and your site to test theories.
  • Pick a metric to track over time, identify the direction you want it to move, then work to make it happen.
  • Always look at the data from different angles and perspectives, looking for patterns and trends.