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10-03-2022

Unraveling mobile internet behavior through customer segmentation: a latent class analysis

Authors: Xuebin Cui, Fei Jin

Published in: Electronic Commerce Research

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Abstract

This paper investigates heterogeneous mobile app usage patterns through customer segmentation and further examines how customer characteristics are correlated with these heterogeneous usage patterns. The research utilizes a unique individual-level mobile app usage dataset and employs a latent class model incorporating concomitant variables. The results uncover four mobile customer segments and show that: (1) customer mobility is most positively associated with the usage pattern of Social-Type Users, with the highest social app usage but the lowest entertainment app usage; (2) customer phone price is most positively correlated with the usage pattern of Entertainment-Type Users, with the highest entertainment and e-commerce app usage; and (3) customer’s number of app downloads is most positive for the usage pattern of Information-Type Users, with the highest information and travel app usage. This study contributes to the literatures on customer segmentation and mobile app usage. The findings offer important implications for app managers.
Footnotes
1
The name of the company is kept anonymous in accordance with a confidentiality agreement.
 
2
The seventeen app subcategories in Anzhi app market include: instant messaging, social networking, video, music, game, work and learning, news and reading, weather and navigation, shopping, finance, photography and beauty, safety, input methods, system tools, desktop themes, integrated services, and browsers.
 
4
The exposition of “highest or lowest” in this study refers to the focal segment presenting the highest or lowest likelihood of one specific app usage compared to the other three customer segments.
 
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Metadata
Title
Unraveling mobile internet behavior through customer segmentation: a latent class analysis
Authors
Xuebin Cui
Fei Jin
Publication date
10-03-2022
Publisher
Springer US
Published in
Electronic Commerce Research
Print ISSN: 1389-5753
Electronic ISSN: 1572-9362
DOI
https://doi.org/10.1007/s10660-022-09542-y