Title: Segmenting online consumers using K-means cluster analysis

Authors: Neha Jain; Vandana Ahuja

Addresses: Jaypee Business School, Jaypee Institute of Information Technology, India ' Jaypee Business School, Jaypee Institute of Information Technology, India

Abstract: Internet users have several characteristics that differentiate them from other online users, so the aim of this research study is to segregate online consumers into diverse consumers segments on the basis of their online shopping behaviour. In this paper, the diverse stages of the consumer decision making process have been discussed and this study explores only three important phases of consumer behaviour in impacting the pre purchase decision (need recognition, information search and evaluation of alternatives). Data was collected from a well defined sample of 1,014 respondents who had an active internet usage rate. K-Means cluster analysis was used to extract four consumer segments - cognizant techno strivers, conversant appraisers, moderate digital ambivalents and techno savvy impulsive consumers. Classifying consumers into well defined segments can aid marketing in developing a more streamlined and focused consumer targeting process.

Keywords: online shopping; consumer behaviour; online consumers; consumer purchase process; k-means clustering; cluster analysis; consumer segmentation; online buying; need recognition; information search; alternatives evaluation; consumer targeting.

DOI: 10.1504/IJLEG.2014.068274

International Journal of Logistics Economics and Globalisation, 2014 Vol.6 No.2, pp.161 - 178

Received: 09 Apr 2014
Accepted: 20 Nov 2014

Published online: 23 Mar 2015 *

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