Clustering-Based Analysis of E-commerce Customers’ Consumption Behavior in the Post-epidemic Period

DOI

10.2991/978-94-6463-210-1_5How to use a DOI?

Keywords

Consumer behavior; E-commerce customers; Cluster analysis

Abstract

The COVID-19 epidemic in 2020 brought huge changes to the world, causing many people to engage in online shopping and some consumers’ purchasing behavior to change before and after the epidemic. In order to better study the consumption behavior of e-commerce customers in the post-epidemic era, this study takes Chinese online shoppers as the research target and uses questionnaires to collect their consumption behavior preferences and to quantify them. This article conducts K-means cluster analysis on consumer behavior data in the post-epidemic era to study the characteristics of different types of consumer behavior, classifying consumers into general consumers, purchase consumers, motivated consumers, product consumers and evaluation consumers. I also make targeted recommendations to e-commerce platforms to help them and hope these will help e-commerce platforms to better cope with the impact of the COVID-19 and enable them to retain customers and achieve accurate marketing in the post-epidemic era, which will ultimately benefit both buyers and sellers.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Zhengyan Cui
PY  - 2023
DA  - 2023/07/25
TI  - Clustering-Based Analysis of E-commerce Customers’ Consumption Behavior in the Post-epidemic Period
BT  - 2023 4th International Conference on E-Commerce and Internet Technology (ECIT 2023)
PB  - Atlantis Press
SP  - 30
EP  - 35
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-210-1_5
DO  - 10.2991/978-94-6463-210-1_5
ID  - Cui2023
ER  -