Data Quality Sucks, Let's Just Get Over It

Data quality on the internet absolutely sucks. And there is nothing you can do about it. At least for now.

Kaushik, Avinash. Occam's Razor (2006). Articles>Web Design>Audience Analysis>Log Analysis

Log Analysis - A Brief Overview

Log files are text files which can range in size from 1KB to 100MB, depending on the traffic at a given a web site. Webmeisters measure traffic by the number of hits or accesses their site receives in a duration of time.

Rubin, Jeffrey. Florida State University (1996). Articles>Web Design>Audience Analysis>Log Analysis

Tracking Your Users in the Access Logs

Most server log analysis applications on the market simply present usage information grouped by date with sub-groupings like daily averages and top downloads by file size. While this can be useful, it doesn't begin to touch the range of information available to be gleaned from the logs with a little creativity.

Hoyt, Philip. evolt (2005). Articles>Web Design>Audience Analysis>Log Analysis

Unsuspected Correlations Are Sweet!

Tracking web usage with a one dimensional mindset (or in a silo) means that you will end up missing so much of the picture.

Kaushik, Avinash. Occam's Razor (2006). Articles>Web Design>Audience Analysis>Log Analysis

Using Your Web Stats for SEO: Search Marketing Analysis from Web Stats

Last week, Jennifer covered the basics of web statistics and what they should mean for you. Now that you have a fairly good handle on what all these statistics mean, how do you put them to work for you? These concerns are answered in this article.

Sullivan Cassidy, Jennifer. SEOchat (2005). Articles>Web Design>Audience Analysis>Log Analysis

Web Analytics: Insights From the Front Line, Part 2

2008 will see a more serious attempt to get Web analytics to become a part of business analytics. We're still a silo in most companies (data and people). We'll see more collaboration and innovation in helping Web data become a core part of the company data to truly give end-to-end visibility (and maybe the holy grail of multichannel analytics/impact).

Mason, Neil. ClickZ (2008). Articles>Web Design>Audience Analysis>Log Analysis

Web Site Stats: A Look Behind The Numbers

In the dot.com boom of the 1990s, an electronic goldrush began as companies flocked like new age prospectors seeking to plant their stake in this digital revolution that has today transformed the ways companies communicate and do business around the globe. Because the web is becoming a viable communications channel, it's important that communications professionals understand how the content they're putting up on a web site is delivering to users the kind of value that is realizing a return on their investment.

Gannon, Joseph P. Communication World Bulletin (2003). Articles>Web Design>Audience Analysis>Log Analysis

Web Statistics: The Truth is in There  

In this study, we assessed and restructured Web server log statistics to analyze our customers’ use of a large-scale Internet library. We formulated questions about how these users might be accessing and navigating the information, then developed our own tools to sort and gather relevant statistics from the log files. We discuss specific successful procedures as well as limitations of the methods. Some of our findings may result in further redesign of the Web site. We also identify areas of interest for further research.

Hood, Teresa L., Linda Jorgensen and Leo J. Smith. STC Proceedings (1998). Articles>Web Design>Audience Analysis>Log Analysis

Analyzing Your Traffic  

Discover your site’s findability triumphs and tragedies with traffic analysis systems.

Walter, Aarron. Building Findable Websites (2008). Articles>Web Design>Audience Analysis>Log Analysis

Web Log Analysis

Getting to know your audience is key to designing a successful website. Because your audience may be spread around the world, learning about the users of your site may be quite a challenge. Even if you think you have a pretty good idea of who your audience is, in many cases, there's a lot of information that you won't know--for example, what browsers your users are using, whether or not they are connecting from on or off campus, or what pages they find most useful.

Novogrodsky, Seth. University of California Berkeley (2000). Articles>Web Design>Audience Analysis>Log Analysis

What's Important to Measure on Your Website?

Websites are very measurable. However, reams of data can be time consuming and confusing. The knack is to know what is really important to measure. This includes the following: reader actions; reader numbers; most and least popular pages; subscribers; external links; search keywords; page size; broken links and malfunctioning processes.

McGovern, Gerry. New Thinking (2003). Articles>Web Design>Audience Analysis>Log Analysis

Are You Using the Wrong Web Metrics?

Do you base success on measuring the volume of visitors and page impressions? Such measures may in fact reflect the failure--rather than the success--of your website.

McGovern, Gerry. New Thinking (2006). Articles>Web Design>Audience Analysis>Log Analysis

Measuring User Motivation from Server Log Files

Estimating user interest and motivation by just counting page requests from a World Wide Web server log (or "hits") provides a distorted metric of user activity. Some of the reasons why this metric is unreliable are that the path dependent nature of hyperlink usability treats index and navigational aid pages as equal to the goal, because differenes in web browsers can determine how effectively users can percieve content and navigational alternatives, and because the poorly designed structure and content of the documents themselves can inhibit users from finding what they are looking for. This paper proposes that measures of how much time users spend looking at a page are better estimates of user interest than page hits, providing simple human factors principles have been applied. An extended example of how this method might be used to collect and analyze data is also included. The types of decisions that can be made by authors and system administrators based on a time-based metric of user interest is summarized.

Fuller, Rodney and Johannes J. de Graaff. Microsoft (1996). Articles>Web Design>Audience Analysis>Log Analysis

Why Web Usage Statistics are (Worse Than) Meaningless

Web usage statistics, such as those produced by programs such as analog cannot be used to make strong inferences about the number of people who have read a website or webpage. Although those who compile these statistics usually try to make this clear, people still insist on misusing them to make overly strong inferences. Attaching meaning to meaningless numbers is worse than not having the numbers at all. When you lack information, it is best to know that you lack the information. Web statistics may give the user a false sense of knowledge which can be worse than being knowingly ignorant.

Goldberg, Jeffrey. Goldmark (1995). Articles>Web Design>Audience Analysis>Log Analysis