If you selling products and services via web channel you may consider analyzing who is visiting your web site and how do people who buy differ from thos that don’t, and out of those who buy - what is their clickstream sequence and navigational pattern.
Each customer's action on a website generates data, and not just high-level interactions such as buying something but also something as simple as using a search engine or navigating through a site. All these interactions between digital service providers, and the consumer can be recorded, and stored in digital databases. These large data sets contain information helpful to business marketing strategies, both - for retrospective analysis, as well as for data-driven forecasting.
Organizations have typically invested large amounts of money into developing their web sites and web strategy and they would like to know what return they are receiving on their investment. Most sites use hits and page views as measure of success of the web site, which clearly is not going to answer their questions. A website is commonly used for:
-Providing product/company information
-Providing customer support
Typical questions that an e-retailer needs to answer are:
- How to increase browser to buyer conversion rate?
- How to increase web retention rate? (Defined as ratio of number of browsers who return to the web site within certain window of time to the total number of browsers.)
- How to reduce clicks-to-close value? (Smaller number indicates that customers are finding easier what they looking for. To reduce this value personalization of web services is a right approach.
- Does the web site design satisfy the needs of various customer segments?
Using page hits will NOT provide answer for any of these goals. Current traffic analysis tools are geared at providing high-level predefined reports about domain names, IP addresses, browsers, cookies and other machine-to-machine activity. These server activity reports simply do not provide the type of bottom-line analysis that e-tailers, service providers, marketers and advertisers in the business world have come to demand. These software packages (i.e., web analysis tools) originated from the need to report on the activity of the web server and not on the activity of the user.
Web mining may be subdivided into:
- Web-content mining
- Web-structure mining
- Web-usage mining.
- User profile data
Web-content mining is the mining of Internet pages, common in the next generation of XML/RKF-based search engines/Web spiders.
Web-structure mining is the application of data mining to reconstruct the structure of a Web site or sites.
Web-usage mining is mining of log files and associated data from a particular Web site to discover knowledge of browser and buyer behavior on that site. User profile data, such as demographic information about the users of the web-site, registration data and customer profile information can provide valuable information of its customers, and can be platform for segmentation and profiling. Web-usage mining is what is widely understood to be web mining and it is main subject of this introduction.