Clustering Internet Shoppers: An Empirical Finding from Indonesia

Abstract

The objective of this research is to do market segmentation of internet shoppers based on internet psychographics. There are thirty-eight indicators from six criteria, i.e., "internet shopping is easy and fun", "internet shopping is a hassle", "I don't know how", "fear of financial theft", "like the energy of brick-and-mortar stores", and "internet has good prices and quality". To do clustering, k-means cluster analysis was employed. Result shows that there are eight segments of internet shoppers, namely, shopping lovers, adventuresome explorers, business users, coward shoppers, suspicious learners, fun seekers, technology muddlers, and shopping avoiders. The first five segments are considered to be more likely to purchase products online, i.e., the online shoppers; while the rest three are the non-online shoppers. The ANOVA test confirmed that the eight segments were appropriate since it created more differentiated and consistent clusters. This research is expected to give a contribution both to the theoretical and empirical literature on customer segmentation where different marketing strategies could be generated for each segment.