A New User Segmentation Model for E-Government

A New User Segmentation Model for E-Government

Ran Tang (Beijing Jiaotong University, Beijing, China), Zhenji Zhang (School of Economics and Management, Beijing Jiaotong University, Beijing, China), Xiaolan Guan (Beijing Institute of Graphic Communication, Beijing, China) and Lida Wang (China Software Testing Center, Beijing, China)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/jeco.2013040101
OnDemand PDF Download:
No Current Special Offers


E-government in China has entered the development stage of personalized services, and user segmentation has become an urgent demand. On the basis of systematic interpretation of e-government development stages, in this article, the authors introduce CRM and customer segmentation concept into e-government areas, construct e-government user segmentation model, and obtain user segmentation results by empirical analysis. Comparing with existing segmentation methods based on experience, because of the introduction of customer segmentation concept and K-means algorithm, e-government user segmentation model presented makes segmentation more scientific and reasonable and can adjust dynamically as the user needs change, continuously improving.
Article Preview

1. Introduction

After many years of construction, e-government in China has made tremendous achievements. E-government has played an active and visible role in government center work, in events about the people’s livelihood, in the actual needs of enterprises and social public. From the perspective of public service, e-government in China has gone through development stages from services provided by the departments to services provided by means of user object or application theme (Zhang, 2009). Currently, we meet the development stage of personalized service (Alshehri, 2011). E-government users’ needs always change with the change of environment and the development of e-government, different service objects always have different needs (Faizullah, 2012). Consequently government should provide personalized and customized services on basis of users’ various needs adequately.

However, most e-government websites provide generic services facing all the service objects, lack of characteristic and pertinence, unable to satisfy general users’ various needs (Chan, 2008). In order to solve this contradiction, first of all, we should fully understand user’s needs, then make the segmentation and provide personalized services for segmentation groups.

At present, customer segmentation mainly surrounds variables like population statistics, behavior and customer value. William proposed AIO (Activity, Interests, Opinion) segmentation and value concept segmentation. Many marketers believe that behavior variables are the best starting point to make market segmentation. Benefit segmentation was first proposed by Haley, he considered that we should get the real benefit behind by customer’s behavior, attitude and motivation. Customer value segmentation was proposed based on CLP model, with comprehensive application of variables like customer lifetime value, psychological factors and using frequency, segmenting customers into platinum level, gold level, steel level and lead level. It can be seen that research on customer segmentation in e-government is rare.

Therefore, in this article, we introduce CRM and customer segmentation concept into e-government areas, referencing the main cluster algorithm in CRM-----K-means algorithm, construct e-government users segmentation model to segment users and provide premise security for personalized services.

Complete Article List

Search this Journal:
Open Access Articles
Volume 19: 4 Issues (2021): 2 Released, 2 Forthcoming
Volume 18: 4 Issues (2020)
Volume 17: 4 Issues (2019)
Volume 16: 4 Issues (2018)
Volume 15: 4 Issues (2017)
Volume 14: 4 Issues (2016)
Volume 13: 4 Issues (2015)
Volume 12: 4 Issues (2014)
Volume 11: 4 Issues (2013)
Volume 10: 4 Issues (2012)
Volume 9: 4 Issues (2011)
Volume 8: 4 Issues (2010)
Volume 7: 4 Issues (2009)
Volume 6: 4 Issues (2008)
Volume 5: 4 Issues (2007)
Volume 4: 4 Issues (2006)
Volume 3: 4 Issues (2005)
Volume 2: 4 Issues (2004)
Volume 1: 4 Issues (2003)
View Complete Journal Contents Listing