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Web analytics is the study of online behaviour in order to improve it. There are two categories; off-site and on-site web analytics. Off-site web analytics refers to web measurement and analysis irrespective of whether you own or maintain a website. It includes the measurement of a website's potential audience (opportunity), the share of voice (visibility), and the buzz (comments) that is happening on the Internet as a whole. On-site web analytics measure a visitor's journey once on your website. This includes its drivers and conversions; for example, which landing pages encourage people to make a purchase. On-site web analytics measures the performance of your website in a commercial context. This data is typically compared against key performance indicators for performance and used to improve a website or marketing campaign's audience response. Historically, web analytics has referred to on-site visitor measurement. However, in recent years this has blurred, mainly because vendors are producing tools that span both categories.
In addition, other data sources may also be added to augment the data. For example; e-mail response rates, direct mail campaign data, sales and lead information, user performance data such as click heat mapping, or other custom metrics as needed.
Web servers have always recorded all their transactions in a log file. It was soon realised that these log files could be read by a program to provide data on the popularity of the website. Thus arose web log analysis software.
In the early 1990s, website statistics consisted primarily of counting the number of client requests (or hits)made to the web server. This was a reasonable method initially since each website often consisted of a single HTML file. However, with the introduction of images in HTML, and web sites that spanned multiple HTML files, this count became less useful. The first true commercial Log Analyzer was released by IPRO in 1994.
Two units of measure were introduced in the mid-1990s to gauge more accurately the amount of human activity on web servers. These were page views and visits (or sessions). A page view was defined as a request made to the web server for a page, as opposed to a graphic, while a visit was defined as a sequence of requests from a uniquely identified client that expired after a certain amount of inactivity, usually 30 minutes.The page views and visits are still commonly displayed metrics but are now considered rather unsophisticated measurements.
The emergence of search engine spiders and robots in the late 1990s, along with web proxies and dynamically assigned IP addresses for large companies and ISPs, made it more difficult to identify unique human visitors to a website. Log analysers responded by tracking visits by cookies, and by ignoring requests from known spiders.
The extensive use of web caches also presented a problem for log file analysis. If a person revisits a page, the second request will often be retrieved from the browser's cache, and so no request will be received by the web server. This means that the person's path through the site is lost. Caching can be defeated by configuring the web server, but this can result in degraded performance for the visitor to the website.
Concerns about the accuracy of logfile analysis in the presence of caching, and the desire to be able to perform web analytics as an outsourced service, led to the second data collection method, page tagging or 'Web bugs'.
The web analytics service also manages the process of assigning a cookie to the user, which can uniquely identify them during their visit and in subsequent visits.
With the increasing popularity of Ajax-based solutions, an alternative to the use of an invisible image, is to implement a call back to the server from the rendered page. In this case, when the page is rendered on the web browser, a piece of Ajax code would call back to the server and pass information about the client that can then be aggregated by a web analytics company. This is in some ways flawed by browser restrictions on the servers which can be contacted with XmlHttpRequest objects.
Both logfile analysis programs and page tagging solutions are readily available to companies that wish to perform web analytics. In some cases, the same web analytics company will offer both approaches.The question then arises of which method a company should choose. There are advantages and disadvantages to each approach.
The main advantages of log file analysis over page tagging are as follows:
The main advantages of page tagging over logfile analysis are as follows.
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