Characterizing Customer Groups for an E-commerce Website

摘要

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.