INTRODUCTION

In Part One, we discussed the “information value proposition” of hits – page views – visits – unique visitors. We will be looking at a sampling of data from one of our clients. Since web analytics data can be considered business confidential information, we’ve masked portions of the information presented so that our client’s interests are protected.

Your approach to web analytics should be identical to how you manage other business processes. Identify your objectives before you begin.

What are your site’s business objectives? Who are your customers? Is your site designed to be strictly informative or do you interact with your customers in some fashion? Once your formulate your goals, it will be easier to articulate your web analytics strategy.

Our Case Study

Our client markets specialized telecommunications equipment and services to a niche market. Their goal is to provide information to current and prospective customers, enable subscriptions to informative newsletters, and provide an interactive platform for exchanging best practices.

We will be using an open source log file analysis tool called Webalizer. It’s available to run on a variety of platforms and produces actionable reports. We’ll take this data and run it through the first two steps of our four-step process.

Step One – Gathering Data

Our client uses one of our Linux/Apache hosting packages to host their web site. As such, they have easy access to their raw log files which are necessary to get a good picture of how their web site is performing. The illustration below depicts some of the log file entries for our client. Click on the image to get a better view.

Step Two – Making Readable Data

By itself, the log file is relatively useless for determining web site success. Web sessions start with the customer’s request to the server for the web page. That is recorded on a single line in the log file. The server sends the page data, style sheet, and all images needed to construct the page to the customer’s web browser for assembly. Each item sent to the browser results in a “hit” being recorded to the log file. In the example above, one page view results in 9 hits.

We will use Webalizer to process the log file and turn the raw data into a presentation that is easily readable. Webalizer will break down each line of the log file and aggregate it into page views (Pages), visits (Visits), and unique visitors (Unique User Agents).

The graph below (click on it to enlarge) depicts the data transferred, pages viewed, visits, and hits.

The graph below depicts the activity of the client’s site on an hourly basis. You’ll notice that the graph is skewed to the right, we’ll discuss analysis in Part Three.

CONCLUSIONS

So far, you’ve established your web site goals; obtained the raw data and a free program to analyze it; and have begun to process the raw data into actionable information. You should feel more empowered about this aspect of business intelligence.

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INTRODUCTION

When you spent the time and money to put up your web site, did you have any idea as to how well it would be received? Are you meeting those goals? Did you get the return on your investment you expected? If you can’t answer these questions, then stick with me as we take a look how to find the information you need.

We’re going to be looking at measuring web site activity as an indicator of return on investment (ROI). There are four basic steps to this process: gathering data, making the data readable, interpreting the data, and formulating a plan of action. This article will cover the first three tasks and a concluding article will help you devise a plan of action.

The Lingo

One thing I want to caution you about is the terminology. Don’t let the word “hits” fool you. Someone who tells you that their web site receives several thousand hits doesn’t understand web site analysis. A “hit” can best be described as the request that your client’s web browser makes to the web server to retrieve the complete web page. A single web page can result in several “hits” being recorded to the web server’s log files.

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