http://tc.eserver.org/dir/Log-Analysis A listing of the most recently indexed works about Log Analysis in the field of technical communication. en-us Copyright (c) 2005-08 by the EServer. All rights reserved. tclib-editorial@eserver.org (TC Library Editorial Board) webmaster@eserver.org (Geoffrey Sauer) http://tc.eserver.org/images/newlogo.gif http://tc.eserver.org/dir/Log-Analysis http://tc.eserver.org/35112.html http://tc.eserver.org/35112.html By seeing all of the available data in one chart, associations, patterns and conclusions can be drawn simply by comparing the relationships as they are presented. This is something that I learned from Edward Tufte. http://tc.eserver.org/34660.html http://tc.eserver.org/34660.html Are there algorithmic ways of determining the health of a Wiki? There are likely a number of different patterns of healthy Wikis and, more importantly, healthy Wiki-based communities. If we can identify and visualize these patterns, we can apply these analytics to: understand the patterns of interactions in a healthy community; aid the community to use the Wiki more effectively; and encourage developers to facilitate these patterns in the tool itself. http://tc.eserver.org/32985.html http://tc.eserver.org/32985.html 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. http://tc.eserver.org/32986.html http://tc.eserver.org/32986.html As a specialist in the user, you gain knowledge through observation and direct questioning of individual users. Now, you can add to that insights gained from data pulled during their actions on the site. By looking at this information, you will get a fuller picture of user behavior, not in a lab, but in the true user environment. http://tc.eserver.org/32987.html http://tc.eserver.org/32987.html 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. http://tc.eserver.org/32988.html http://tc.eserver.org/32988.html 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. http://tc.eserver.org/32989.html http://tc.eserver.org/32989.html 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. http://tc.eserver.org/32990.html http://tc.eserver.org/32990.html 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. http://tc.eserver.org/32756.html http://tc.eserver.org/32756.html Discover your site’s findability triumphs and tragedies with traffic analysis systems. http://tc.eserver.org/32608.html http://tc.eserver.org/32608.html Your web server archives the information needed to generate these numbers and many others. The raw data is stored on the server in what is known as a log file. The statistics referenced above are best accumulated through the use of a log analysis program to convert your hard-to-read server log files into an understandable format. http://tc.eserver.org/32364.html http://tc.eserver.org/32364.html This paper describes a tool for log file recording and a method for quickly and easily analysing human-computer interaction with mobile devices. The tool logs screenshots and quantitative interaction data, such as number of clicks and timestamps. The analysing tool provides the ability to evaluate the interaction sequences and to export an MS Excel®-sheet for statistical analysis. To evaluate the tool, a usability study was conducted comparing the effectiveness of this tool in the laboratory and in the mobile context. Findings show that the tool is the first step toward a very effective, unobtrusive analysing method for user interaction in the mobile context. Combined with debriefing methods, it would be an optimized way for usability testing with mobile devices. http://tc.eserver.org/32270.html http://tc.eserver.org/32270.html is paper presents early findings of a unique analysis that related questionnaire data to site usage as recorded in the transaction log reports of ScienceDirect, for the same people. Its focus is the differences in the online behaviour of three types of navigational users: those accessing the site via a gateway (either via a reference hyperlink or subject search facility), those using the on site search facility and those employing menus. Towards this end 16,865 sessions were analysed and grouped by navigational entry and compared over three types of online behaviour: the viewing of articles in press (AIP), the number of different journals viewed in a session and the viewing of old material. A strong association was found between form of navigation and behavioural trait. Those using menus were more likely to view AIPs, while those using the search facility were more likely to view a greater number of different journals and were more likely to view older material. This supports a hypothesis proposed by Nicholas et al. (2006) that use of the online searching facility increases the visibility of material irrespective of journal and age and results in a greater use of older material and a more diverse journal use compared to other online and off-line information retrieval methods. Although research has been undertaken on the different strategies that users employ to navigate and find their way around a collection of content (e.g. a digital library), this we believe is the first time the effect of different navigational strategies on outcomes (for example, what is viewed) has been investigated. http://tc.eserver.org/32272.html http://tc.eserver.org/32272.html This paper argues that metrics can be generated from search transactional web logs that can help evaluate search engine effectiveness. Search logs from the BBC website were analysed and metrics extracted. Two search metrics — the time lapse between searches and the number of searches in a session — were developed to see whether they could measure search success or satisfaction. In all, 4 million search statements by 900,000 users were evaluated. The BBC search engine possessed a number of functional attributes which sought to improve retrieval and these were subjected to the two metrics to help determine how successful they were in practice. There was some evidence to support the proposition that the search outcome metrics did indeed indicate the effectiveness of engine functionality. The authors argue that this result is significant in that the identification of search outcome metrics will pave the way for assessing the effectiveness of site specific search engines and a greater understanding of the effectiveness of search engine functionality. http://tc.eserver.org/31545.html http://tc.eserver.org/31545.html 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. http://tc.eserver.org/31409.html http://tc.eserver.org/31409.html 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. http://tc.eserver.org/30883.html http://tc.eserver.org/30883.html 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. http://tc.eserver.org/30884.html http://tc.eserver.org/30884.html 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. http://tc.eserver.org/30879.html http://tc.eserver.org/30879.html In many companies Web and Web analytics have been a silo that someone else is taking care of. Web sites are becoming the most important customer touch point and the most important revenue generator, even for businesses that are not first of mind. http://tc.eserver.org/30880.html http://tc.eserver.org/30880.html 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). http://tc.eserver.org/30881.html http://tc.eserver.org/30881.html Tools to build an effective Web measurement strategy on a tight budget. http://tc.eserver.org/30878.html http://tc.eserver.org/30878.html The goal of the Trinity mindset is to power the generation of actionable insights. Its goal is not to do reporting. Its goal is not to figure out how to spam decision makers with data. Actionable Insights and Metrics are the uber-goal simply because they drive strategic differentiation and a sustainable competitive advantage. http://tc.eserver.org/30877.html http://tc.eserver.org/30877.html Tracking web usage with a one dimensional mindset (or in a silo) means that you will end up missing so much of the picture. http://tc.eserver.org/30868.html http://tc.eserver.org/30868.html 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). http://tc.eserver.org/30866.html http://tc.eserver.org/30866.html Data quality on the internet absolutely sucks. And there is nothing you can do about it. At least for now. http://tc.eserver.org/30865.html http://tc.eserver.org/30865.html Is doing Path Analysis a good use of time? In my humble opinion the answer is a rather emphatic no, except for one exception (which I'll discuss below). Almost always Path Analysis tends to be a sub optimal use of our time, resources and any money that is expended on buying tools that do 'great' Path Analysis. http://tc.eserver.org/30864.html http://tc.eserver.org/30864.html Bounce rate is a beautiful way to measure the quality of traffic coming to your website. It is almost instantly accessible in any web analytics tool. It is easy to understand, hard to misunderstand and can be applied to any of your efforts. http://tc.eserver.org/30867.html http://tc.eserver.org/30867.html Perhaps there is no other single metric that is abused as much as conversion rate, none that is perhaps more detrimental to solving for a holistic customer experience on the website because of the company behavior it drives. http://tc.eserver.org/30439.html http://tc.eserver.org/30439.html 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. http://tc.eserver.org/30226.html http://tc.eserver.org/30226.html 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. http://tc.eserver.org/30059.html http://tc.eserver.org/30059.html 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. http://tc.eserver.org/29624.html http://tc.eserver.org/29624.html 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. http://tc.eserver.org/28380.html http://tc.eserver.org/28380.html ウェブサイトへのアクセスログを線形グラフにするだけでは、データの大切な部分を見落とすことになりかねない。ときには、一歩進んだグラフ化にもやってみる価値があるものだ。 http://tc.eserver.org/28049.html http://tc.eserver.org/28049.html 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. http://tc.eserver.org/27938.html http://tc.eserver.org/27938.html The relative popularity of a site's pages, the number of visitors referred by other sites, and the traffic from search queries continue to follow a Zipf distribution. http://tc.eserver.org/27680.html http://tc.eserver.org/27680.html 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. http://tc.eserver.org/27379.html http://tc.eserver.org/27379.html Interface designers today are swirling within a blizzard of data. How many types of user data does your Web team collect? http://tc.eserver.org/26491.html http://tc.eserver.org/26491.html 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. http://tc.eserver.org/26492.html http://tc.eserver.org/26492.html Measurement is a crucial part of a successful search marketing campaign, but understanding and using web analytics tools can be daunting. A new book demystifies the process, showing you how to implement your own effective measurement strategies. http://tc.eserver.org/26332.html http://tc.eserver.org/26332.html 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. http://tc.eserver.org/25198.html http://tc.eserver.org/25198.html Is your website structured according to the needs of your users? Does it deliver on your website objectives? Use Web Analytics to redesign it. http://tc.eserver.org/25197.html http://tc.eserver.org/25197.html How to use web analytics in designing web information architecture. http://tc.eserver.org/24292.html http://tc.eserver.org/24292.html 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. http://tc.eserver.org/24268.html http://tc.eserver.org/24268.html 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. http://tc.eserver.org/23810.html http://tc.eserver.org/23810.html 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. http://tc.eserver.org/22667.html http://tc.eserver.org/22667.html 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. http://tc.eserver.org/22560.html http://tc.eserver.org/22560.html What’s the difference between simply measuring page hits and views, and actually converting site visits to sales? Smart use of Web analytics. http://tc.eserver.org/21917.html http://tc.eserver.org/21917.html What do people mean when they talk about 'hits,' 'visits,' and 'visitors?' http://tc.eserver.org/21847.html http://tc.eserver.org/21847.html Find out where your visitors come from. http://tc.eserver.org/21434.html http://tc.eserver.org/21434.html 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. http://tc.eserver.org/21400.html http://tc.eserver.org/21400.html As a specialist in the user, you gain knowledge through observation and direct questioning of individual users. Now, you can add to that insights gained from data pulled during their actions on the site. By looking at this information, you will get a fuller picture of user behavior, not in a lab, but in the true user environment. http://tc.eserver.org/21325.html http://tc.eserver.org/21325.html The following table shows the number of visits that have been recorded in the Useit server logs as coming from search engines and directory services (so-called 'portals') in a one-month period (March) in each of the years from 1998 through 2003. http://tc.eserver.org/21060.html http://tc.eserver.org/21060.html One of the most unusual aspects of data about people and nature is its uneven distribution. Explore the non-normal distribution called bimodal distribution. http://tc.eserver.org/21059.html http://tc.eserver.org/21059.html 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. http://tc.eserver.org/19027.html http://tc.eserver.org/19027.html 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? http://tc.eserver.org/19031.html http://tc.eserver.org/19031.html 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. http://tc.eserver.org/19023.html http://tc.eserver.org/19023.html 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. http://tc.eserver.org/19025.html http://tc.eserver.org/19025.html Server log files are records of Web server activity (or server activity for any digital medium). They provide details about file requests to a server and the server response to those requests. Collecting and analyzing these files can provide: information about who is coming to your Web site; what information they're requesting; their navigation and behavior. What types of data you collect on your server depends on how it has been set up and defined by the technical staff. http://tc.eserver.org/19024.html http://tc.eserver.org/19024.html When analyzing numbers related to the growth of a website, I normally recommend looking at them on a logarithmic scale. The reason is that the Web and the Internet both experience exponential growth. Therefore, Web statistics are better analyzed in terms of growth rates than in terms of linear growth. http://tc.eserver.org/14358.html http://tc.eserver.org/14358.html Interpreting web statistics has been described as “trying to nail Jell-o to the wall.” Web log files trackfile accesses on the server. They do not track users; they do not track interest levels, they do not track success or failure communicating information. Caches “hide” site accesses from the server log and “hits” provide a poor mea sure of interest in particular content. Some people argue that there is really no meaning to server logs other than a measure of server load. However, even with all their flaws, some find web statistics useful in identifying how best to allocate resources in web site development. http://tc.eserver.org/13794.html http://tc.eserver.org/13794.html 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. http://tc.eserver.org/10412.html http://tc.eserver.org/10412.html 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.