User Segmentation Based on Finding Communities with Similar Behavior on the Web Site

Web log analysis can be helpful in gaining information about the usability of the web site, web performance, for marketing purposes, or for development of business intelligence tools in e-commerce systems. User segmentation is one of the problems solved in marketing and e-commerce sphere. Various software was developed to support web analysis. However, most of them provide only information through the tools based on statistics. User behavior and interaction with the web site is usually presented by measurement of click through rates, or by identification and sometimes visualization of popular paths only. User segmentation for further analysis (e.g. campaign analysis in marketing, web recommendation, web usage optimization) is usually allowed with the manual selection (often with variable setting). In this paper is presented the automatic user segmentation (clustering) based on the similar user's behavior on the web site. The user's behavior and behavioral patterns are extracted using process mining techniques; further user segmentation is provided by finding communities with similar behavior through two-step hierarchical clustering.