Web Analytics is the measurement, collection, analysis and reporting of internet data for purposes of understanding and optimizing web usage.
Web Analytics is not just a tool for measuring website traffic but can be used as a tool for business research and market research. Web analytics applications can also help companies measure the results of traditional print advertising campaigns. It helps one to estimate how traffic to a website changes after the launch of a new advertising campaign. Web analytics provides information about the number of visitors to a website and the number of page views. It helps gauge traffic and popularity trends which is useful for market research.
There are two categories of web analytics; off-site and on-site web analytics.
Off-site web analytics refers to web measurement and analysis regardless of whether you own or maintain a website. It includes the measurement of a website’s potential audience (opportunity), share of voice (visibility), and 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 web site or marketing campaign’s audience response.
On-site web analytics technologies
- Log file Analysis: It reads the log files in which the web server records all its transactions.
Web server log file analysis
Web servers record some of their transactions in a log file. In the early 1990s, web site 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 web site 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 rudimentary.
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 analyzers responded by tracking visits by cookies, and by ignoring requests from known spiders.
The extensive use of web caches also presented a problem for logfile 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. Cookie acceptance rates vary significantly between web sites and may affect the quality of data collected and reported.
Collecting web site data using a third-party data collection server (or even an in-house data collection server) requires an additional DNS look-up by the user’s computer to determine the IP address of the collection server. On occasion, delays in completing a successful or failed DNS look-ups may result in data not being collected.
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. Also, this method can lead to slightly lower reported traffic levels, since the visitor may stop the page from loading in mid-response before the Ajax call is made.
Advantages of page tagging
The main advantages of page tagging over logfile analysis are as follows:
- Counting is activated by opening the page (given that the web client runs the tag scripts), not requesting it from the server. If a page is cached, it will not be counted by the server. Cached pages can account for up to one-third of all pageviews. Not counting cached pages seriously skews many site metrics. It is for this reason server-based log analysis is not considered suitable for analysis of human activity on websites.
- Page tagging can report on events which do not involve a request to the web server, such as interactions within Flash movies, partial form completion, mouse events such as onClick, onMouseOver, onFocus, onBlur etc.
- The page tagging service manages the process of assigning cookies to visitors; with logfile analysis, the server has to be configured to do this.
- Page tagging is available to companies who do not have access to their own web servers.
- Lately page tagging has become a standard in web analytics
Tag Capturing Tools used for Testing
There are several different tools that can be used to debug web analytics tags. There isn’t one correct tool to use; it’s more a question of user comfort/preference of any given tool. Some of these may vary by browser.
Multiple Browser Support:
- Omniture Digital Pulse Debugger (see Omniture Blog post)
- Fiddler (see http://fiddlertool.com )
- WASP (Web Analytics Solution Profiler)
- FireBug (Mozilla Add-on Directory)
- OmniBug (requires FireBug, http://rosssimpson.com/dev/omnibug.html )
- Internal debugger