Alternative ways to Measure the Effectiveness of Your Intranet Sites

When you measure hits on inter/intranet sites, you are measuring overall volume of usage -- how many times parts of your site have been opened. However, hits don't distinguish between the opening of an entire page or a single illustration. There are many additional ways of measuring usage. However, measuring the "userability" of a site is just as important in order to improve usage numbers. But the first place any communicator should start when measuring the effectiveness of electronic communications is to identify the original objectives for putting something on-line. Conducting some baseline audience research upfront to make sure your electronic solutions will be as effective as possible and then measuring afterward to see if the intended objectives are being met.

Sinickas, Angela D. Sinickas Communications (2000). Articles>Web Design>Intranets>Log Analysis

Analyzing Web-Based Help Usage Data to Improve Products  

This paper describes how user assistance can streamline deliverables and improve product design by analyzing usage patterns from server-based content. We can then base decisions about how to improve deliverables on a thorough understanding of how customers use help content to find information and solve problems. This approach enables user assistance to add more value to both our companies and our customers by creating a three-way dialog between user assistance, the customer, and the product team. It also broadens the definition of assistance to include helping to design products that people can use without the need for instructions.

Raiken, Nancy. STC Proceedings (2005). Articles>Documentation>Audience Analysis>Log Analysis

The Awesome Power of Visualization 2: Death and Taxes 2007

Visuals that provide insights come from 1) a deep understanding of the goal / objectives 2) from thinking beyond what standard trend lines or stacked bar graphs can provide. Something non-normal to grab attention and yet communicate insights (sort of already contain recommendations and action items and not just data).

Kaushik, Avinash. Occam's Razor (2007). Articles>Graphic Design>Technical Illustration>Log Analysis

Benutzertests durch Spurenverwertung  

In most cases a technical writer cannot do any user tests. If you have access to the user log of a web server you can derive quite interesting facts like how often and how long a specific page was viewed and how the surfers navigated.

von Obert, Alexander. Techwriter.de (2003). (German) Design>Web Design>User Centered Design>Log Analysis

Bimodal Distributions Contain Clues

One of the most unusual aspects of data about people and nature is its uneven distribution. Explore the non-normal distribution called bimodal distribution.

Allen, Cliff. Allen.com (2001). Design>Web Design>Statistics>Log Analysis

Building a Data-Backed Persona

Incorporating the voice of the user into user experience design by using personas in the design process is no longer the latest and greatest new practice. Everyone is doing it these days, and with good reason. Using personas in the design process helps focus the design team's attention and efforts on the needs and challenges of realistic users, which in turn helps the team develop a more usable finished design. While completely imaginary personas will do, it seems only logical that personas based upon real user data will do better. Web analytics can provide a helpful starting point to generate data-backed personas; this article presents an informal 5-step process for building a 'persona of the people.' In practice, outcomes indicate that designing with any persona is better than with no personas, even if the personas used are entirely fictitious. Better yet, however, are personas that are based on real user data. Reports and case studies that support this approach typically offer examples incorporating data into personas from customer service call centers, user surveys and interviews. It's nice work if you can get it, but not all design projects have all (or even any!) of these rich and varied user data sources available. However, more and more sites are now collecting web analytic data using vendor solutions or free options such as Google Analytics. Web analytics provides a rich source of user data, unique among the forms of user data that are used to evaluate websites, in that it represents the users in their native habitat of use. Despite some drawbacks to using web analytics that are inherent to the technology and data collection methods, the information it provides can be very useful for informing design.

Wiggins, Andrea. Boxes and Arrows (2007). Articles>User Centered Design>Personas>Log Analysis

Configure Web Logs in Apache

Traffic statistics have a huge impact on a Website's success, and Apache provides one of the most powerful and flexible logging features available today. Blane explains the nitty-gritty of configuring Apache Weblogs in this handy how-to.

Warrene, Blane. SitePoint (2004). Design>Web Design>Audience Analysis>Log Analysis

Correlating Web-User Data

The statistical term 'correlation' has found its way into popular business language. Often, though, no measurement of correlation has actually taken place. That's too bad. Because there's probably a correlation between measuring correlation and increasing revenue.

Allen, Cliff. Allen.com (2001). Design>Web Design>E Commerce>Log Analysis

Correlating Web-User Data

Everything served to a visitor -- from the first page through marketing, sales, and product fulfillment -- generates data about the customer. Web marketers can tap into this 'free' source of profile data for just the cost of converting existing data into a format that can be used by a data-analysis program.

Allen, Cliff. ClickZ (2001). Articles>Usability>Web Design>Log Analysis

Data Mining and Predictive Analytics, Part 1

The cluster analysis process looks for groups of visitors in the data, where the people within the groups have something in common but the commonality is different from group to group.

Mason, Neil. ClickZ (2007). Articles>Web Design>Research>Log Analysis

Data Mining and Predictive Analytics, Part 2

In part one of this series, I examined visitor segmentation, a data-mining technique. Now, let's look at how data mining can be used to understand important visitor behavior over time.

Mason, Neil. ClickZ (2007). Articles>Web Design>Research>Log Analysis

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

Data Visualization of Web Stats: Logarithmic Charts and the Drooping Tail

Using a linear diagram to plot data from website traffic logs can lead you to overlook important conclusions. Sometimes advanced visualizations are worth the effort.

Nielsen, Jakob. Alertbox (2006). Articles>Web Design>Technical Illustration>Log Analysis

Don't Be a Slave to the Web Stats

Web stats are a tool and you need to know how to you that tool. Otherwise, you aren't accomplishing anything. At the very simplest level, your web stats should help you to figure out this overused business truism: 'Do more of what works. Do less of what doesn't.' But if you really want to derive value, you need to delve deeper. You need to understand what the numbers are telling you.

Improving Customer Experience (2006). Design>Web Design>Assessment>Log Analysis

The End of the Hit Parade

Once upon a time, if it was on the web, it was good. If it did tricks, so much the better. And how did a company know if its website was really good? Of course, by measuring traffic. The more traffic, the better, right?

Jaleshgari, Ramin. CIO Magazine (2000). Articles>Web Design>Usability>Log Analysis

Guidelines for Web Data Collection: Understanding and Interacting with Your Users    

The global growth of the World Wide Web challenges technical communicators to reconsider the methods we use to create designs that meet the goals and needs of our users. This article focuses on taking advantage of the Web's potential for interactivity between designers and users. It offers strategies for getting data from users of Web sites and using it for two main purposes: (1) analyzing audience and patterns of use to support continuous redesign, and (2) building a relationship or sense of community on a Web site.

Ramey, Judith A. Technical Communication Online (2000). Articles>Web Design>User Centered Design>Log Analysis

How the Web Works

A short essay about what one can and can't discern from webserver log file analysis, which involves a tutorial on how HTTP requests operate.

Analog (2004). Articles>Web Design>Statistics>Log Analysis

How to Determine Monthly Web Site Visitors

If you pay another business to host your Web site, give them a call. Tell them you want monthly traffic reports delivered to you each month.

Costello, Rick. STC Chicago (2003). Articles>Web Design>Assessment>Log Analysis

How to Measure Web Site Effectiveness

Could you justify the expense and prove ROI? Learn how to measure your Web site's effectiveness and answer that question with precision.

Costello, Rick. STC Chicago (2003). Articles>Web Design>Assessment>Log Analysis

How To Quantify the User Experience

How can you quantify a concept as nebulous as user experience? Rob's tutorial shows how you can statistically assess the experience a site provides - a great way to review a prospect's existing site and springboard redevelopment discussions.

Rubinoff, Robert. SitePoint (2004). Design>Web Design>User Experience>Log Analysis

Information Architecture through Web Analytics

Is your website structured according to the needs of your users? Does it deliver on your website objectives? Use Web Analytics to redesign it.

Hurol Inan (2005). Articles>Web Design>User Centered Design>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

Making Smart Use of Web Analytics  

What’s the difference between simply measuring page hits and views, and actually converting site visits to sales? Smart use of Web analytics.

Cummings, Joanne. PDFzone (2004). Design>Web Design>Audience Analysis>Log Analysis

Mazed and Confused

You ask the Web jockeys to pull the latest stats. Hits are growing. Page turns per visit are up. The search button has been getting lots of action too. But before you pass those numbers on to the CEO, think again: The search button's popularity could be a sign that customers can't tell where the site's navigation buttons will take them. Those hits and page turns could be a sign that customers are lost, testing link after link. You don't know because at your company, as at most companies, no one has ever asked customers whether your Web site is easy to use. And what you don't know can cost you.

Kalin, Sari. CIO Magazine (1999). Design>Web Design>Usability>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>Usability>Log Analysis