The rapid development of the internet introduced new trend of electronic transactions that is gradually dominating all aspects of our daily life. The amount of data maintained by websites to keep track of the visitors is growing exponentially. Benefitting from such data is the target of the study described in this paper. We investigate and explore the process of analyzing log data of website visitor traffic in order to assist the owner of a website in understanding the behavior of the website visitors. We developed an integrated approach that involves statistical analysis, association rules mining, and social network construction and analysis. First, we analyze the statistical data on the types of visitors that come to the website, as well as the steps they take to reach and satisfy the goal of their visit. Second, we derive association rules in order to identify the correlations between the web pages. Third, we study the links between the web pages by constructing a social network based on the frequency of access to the web pages such that two web pages get linked in the social network if they are identified as frequently accessed together. The value added from the overall analysis of the website and its related data should be considered valuable for ecommerce and commercial website owners; the owners will get the information needed to display targeted advertisements or messages to their customers. Such an automated approach gives advantage to its users in the current competitive cyberspace. In the long run, this is expected to allow for the increase in sales and overall customer loyalty.