Characterizing customer groups for an e-commerce website

Abstract

In conventional commerce, customer groups with similar interests or behaviours can be observed. Similarly, customers in E-commerce naturally form groups. These groups allow the organization to provide quality of service (QoS) and perform capacity planning. From a system point of view, overall server performance can be improved and resources managed considering customer session behaviour.Previous studies have grouped customers using clustering techniques. Different data metrics have been selected as criteria for grouping, in order to analyze different problems. The limitation for these approaches is that problems are analyzed separately. In order to manage an E-commerce server well, we must analyze many related problems comprehensively rather than separately. For example, we would like to know the impact on resource usage when optimizing revenue. Thus, we must understand the differences and similarities between session groups chosen by different metrics. This paper characterizes customer groups for an E-rental business and compares customer groups created according to different criteria including services requested, navigation pattern and resource usage. A significant finding of this study shows that using each of the three criteria independently yields roughly similar results, since customers looking for similar services tend to have similar navigation pattern as well as similar server resource usage. Thus, it is sufficient to group customers in only one of these ways. Grouping customers by services requested is suggested since this method yields relatively better results and is simple to implement.

Practical Aspects of Log File Analysis for E-Commerce

“…The click-stream analysis is often combined with segmentation or clustering methods to determine different customer profiles [4,2,5,3]. Discovery of meaningful usage patterns characterizing the browsing behavior of Internet users realized by applying data mining techniques is called Web usage mining.…”

“…In e-commerce environment such analyses have been performed at multiple levels, including the lowest, protocol level (corresponding to HTTP requests), the application level (corresponding to Web page requests or business-related Web interactions), and the user level (corresponding to user sessions) [1][2][3]. From the online retailers' point of view the application and user level analyses are of the highest practical value because understanding the way in which customers use the site and navigate through the store, especially in the context of successful purchase transactions, may lead to better organization of e-commerce service and more efficient business decisions.…”

The Gugubarra Project: Building and Evaluating User Profiles for Visitors of Web Sites

“…In most research done until now, Web users are clustered by their click streams or by their visited pages [4], [12], [13]. By using the non-obvious profiles approach we have the possibility to cluster Web users by looking at the content of the Web pages, and the users' interests in several topics related to the pages.…”

Clustering subjects in a credential-based access control framework

“…This method can be considered mostly in subscriptionbased systems, in that it requires the server to store the subject credentials. grouping of subjects according to their interests (Middleton et al, 2004;Wang et al, 2004;Xie and Phoha, 2001) and their credentials. This grouping is inspired from the ideas proposed in earlier research efforts related to Web clustering (Baldi et al, 2003;Chakrabarti, 2003;Jain et al, 1999;Jeng et al, 2002).…”