Promoting where, when and what? An analysis of web logs by integrating data mining and social network techniques to guide ecommerce business promotions

Abstract

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.

Using association rules to assess purchase probability in online stores

“…The proper analysis may lead to better organization of an e-commerce service and more efficient business decisions. Adnan et al (2011) emphasized that the goal of log file analysis should be gaining an insight into the user browsing behavior and then translating this insight knowledge into foresight knowledge that would help the online seller to adjust their business policies. Better understanding of e-customer shopping behavior makes it possible to reduce customers' product searching time and thus to decrease searching cost by recommending products customers may probably be interested in.…”

“…Two main approaches may be applied here (Chen et al 2004): interval sessions, where each session consists of pages accessed by the same user within a time limit and gap sessions, where each session is a sequence of pages accessed by the same user with pairwise access time gaps below a threshold value. A 30-min threshold has been typically assumed (Adnan et al 2011;Chen et al 2004;Catledge and Pitkow 1995;Stevanovic et al 2011;Suchacka and Chodak 2013). Another important preparation task before the click-stream analysis is identification and elimination of traffic generated by bots which reveal different navigational patterns than human users (Suchacka 2014;Stassopoulou and Dikaiakos 2009).…”

“…Many studies on the click-stream analysis have addressed the problem of user navigation paths and sequential patterns discovery on e-commerce sites (Adnan et al 2011;Kwan et al 2005;Lee and Yen 2007;Shim et al 2012). A key observation has been connected with the fact that visitors to an online store are potential buyers and perform different kinds of operations on different pages.…”

Interaction networks and patterns of guild community in massively multiplayer online games

“…Some work has also been carried out to analyze user online behavior in blogs (Chang et al 2007) and Flickr (Negoescu 2007), a photo sharing online community. Newer studies have also made use of SNA to study large scale online networks (Fazeen et al 2011;Adnan et al 2011;Bhattarcharyya et al 2011).…”

Social opinion mining for supporting buyers’ complex decision making: exploratory user study and algorithm comparison

“…In recent years, intelligent decision supports have been developed for assisting users in efficiently processing various types of product information and enabling them to make more informed and accurate decisions. Some studies have also been done to analyze user logs and transaction data in the Web sites so as to guide e-commerce promotions (Adnan et al 2011;Raeder and Chawla 2011). Among these approaches, the so-called recommender system is a typical example, as it emphasizes on the value of user-generated product content (e.g., ratings/reviews) to determine whether some items would be preferred by a user given that her neighbors rated these items high (Adomavicius and Tuzhilin 2005;Siersdorfer and Sizov 2009). However, this kind of decision system has been mainly oriented to low-value, and frequently purchased products such as books, movies or music, for which the current user can provide ratings or prior history.…”