Proceedings of the 2017 2nd International Seminar on Education Innovation and Economic Management (SEIEM 2017)

Customer Segmentation of Third Party Review Website Based on Cluster Analysis

Authors
Shan Gao, Changbin Jiang
Corresponding Author
Shan Gao
Available Online December 2017.
DOI
https://doi.org/10.2991/seiem-17.2018.5How to use a DOI?
Keywords
cluster analysis, discriminant analysis, customer segmentation, Dianping Holdings website
Abstract
This paper made segmentation of customers on the website of Dianping Holdings and identified the characteristics of different types of customers, so as to give recommendations to different clients. The paper used locomotive collector to grasp the customer information on the website of Dianping Holdings. After processing the original data, 38791 pieces of final data were got. Then, SPSS software was used in cluster analysis and discriminant analysis. In the end, the paper subdivides the customers on the Dianping Holdings website into six categories: hyperactive customers, active customers, moderately active customers, low-value customers, potentially high-value customers and high-value customers. It also finds that the number of female customers is more than male customers in six categories.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 2nd International Seminar on Education Innovation and Economic Management (SEIEM 2017)
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
December 2017
ISBN
978-94-6252-442-2
ISSN
2352-5398
DOI
https://doi.org/10.2991/seiem-17.2018.5How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Shan Gao
AU  - Changbin Jiang
PY  - 2017/12
DA  - 2017/12
TI  - Customer Segmentation of Third Party Review Website Based on Cluster Analysis
BT  - 2017 2nd International Seminar on Education Innovation and Economic Management (SEIEM 2017)
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/seiem-17.2018.5
DO  - https://doi.org/10.2991/seiem-17.2018.5
ID  - Gao2017/12
ER  -