An e-customer behavior model with online analytical mining for internet marketing planning
Document Type
Journal article
Source Publication
Decision Support Systems
Publication Date
11-1-2005
Volume
41
Issue
1
First Page
189
Last Page
204
Keywords
Customer behavior, Internet marketing, Knowledge discovery, OLAM, Traversal pattern
Abstract
In the digital market, attracting sufficient online traffic in a business to customer Web site is vital to an online business's success. The changing patterns of Internet surfer access to e-commerce sites pose challenges for the Internet marketing teams of online companies. For e-business to grow, a system must be devised to provide customers' preferred traversal patterns from product awareness and exploration to purchase commitment. Such knowledge can be discovered by synthesizing a large volume of Web access data through information compression to produce a view of the frequent access patterns of e-customers. This paper develops constructs for measuring the online movement of e-customers, and uses a mental cognitive model to identify the four important dimensions of e-customer behavior, abstract their behavioral changes by developing a three-phase e-customer behavioral graph, and tests the instrument via a prototype that uses an online analytical mining (OLAM) methodology. The knowledge discovered is expected to foster the development of a marketing plan for B2C Web sites. A prototype with an empirical Web server log file is used to verify the feasibility of the methodology.
DOI
10.1016/j.dss.2004.11.012
Print ISSN
01679236
Funding Information
This work was greatly supported by the University Grant Committee of Lingnan University of Hong Kong (grant code: DR03A5). {DR03A5}
Publisher Statement
Copyright © 2004 Elsevier B.V. All rights reserved. Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Kwan, I. S. Y., Fong, J., & Wong, H. K. (2005). An e-customer behavior model with online analytical mining for internet marketing planning. Decision Support Systems, 41(1), 189-204. doi: 10.1016/j.dss.2004.11.012