A Traffic Tracking Analysis Model for the Effective Management of E-commerce Transactions

Authors

Sylvanus A. Ehikioya^{1, *, †,} , Shenghong Lu²

¹Department of Computer Science, Baze University, Abuja, Nigeria

²Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada R3T 2N2

^†

The initial research for this paper was carried out while Dr. S. A. Ehikioya was with the University of Manitoba, Winnipeg, Canada.

^*Corresponding author. Email: ehikioya@gmail.com

DOI

10.2991/ijndc.k.200515.006How to use a DOI?

Keywords

Traffic tracking and analysis; data mining; personalization and recommendation systems; path analysis tree; Web log files; packet monitors; single-pixel image

Abstract

The increasing popularity of the Internet and e-commerce makes online merchants to constantly seek tools that would permit them to attract new and retain old customers. Traffic tracking and analysis tools can help businesses know more about their customers. These tools track visitors’ behaviors on Web sites. The information obtained from Web traffic tracking and analysis can help online merchants target specific audiences with customized products and services. Most commonly used approaches include Web log file, packet monitors, and single-pixel image approach. Each of these approaches has some drawbacks, which limits the types of data it can track or the user environment. In this paper, we propose a tracking and analysis approach, which has fewer limitations and more advantages than the existing approaches. We discuss three different approaches (i.e., improved single-pixel image, JavaScript tracking and HTTP (Hypertext Transfer Protocol) proxy server), which work together to track a user’s activities. In addition to basic analysis, we implement advanced analysis such as path analysis tree and user clustering. Path analysis is pivotal for Web site management and marketing in e-commerce. In modeling the tracking and analysis approach, we used a formal technique to guide quality assurance imperatives.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Sylvanus A. Ehikioya
AU  - Shenghong Lu
PY  - 2020
DA  - 2020/05/25
TI  - A Traffic Tracking Analysis Model for the Effective Management of E-commerce Transactions
JO  - International Journal of Networked and Distributed Computing
SP  - 171
EP  - 193
VL  - 8
IS  - 3
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.k.200515.006
DO  - 10.2991/ijndc.k.200515.006
ID  - Ehikioya2020
ER  -