Web stats analysis is one of the most important feature of search engine optimization often overlooked by medium or small businesses however web stats analysis shouldn't be ignored at any cost .
Website analytics is the study of the behaviour of visitors of any given website. In a commercial context, web analytics especially refers to the use of data collected from a web site to determine which aspects of the website work towards the business objectives; for example, which landing pages encourage people to make a purchase.
Data collected almost always includes web traffic reports. It may also include 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. This data is typically compared against key performance indicators for performance, and used to improve a web site or marketing campaign's audience response.
Team Anewraag specialize in setting up, maintain & report based on any of the analytics software available.
Web stats analysis is one of the most important feature of search engine optimization often overlooked by medium or small businesses however web stats analysis shouldn't be ignored at any cost, as web stats analysis provides the complete information about all the visitor activities happening on a website. Our comany provide website analytics services to solve the web stats analysis problem for any small or medium business.
|Google Analytics (Urchin)|
There are two main technological approaches for collecting web analytics data.
The first method - logfile analysis, reads the logfiles in which the web server records all its transactions.
Web servers have always recorded all their transactions in a logfile. It was soon realised that these logfiles 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, 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 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 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 log file 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.