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

Xuebin Cui () and Fei Jin ()
Additional contact information
Xuebin Cui: Nanjing University
Fei Jin: Sichuan University

Electronic Commerce Research, 2023, vol. 23, issue 4, No 15, 2379-2398

Abstract: 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.

Keywords: Mobile marketing; Mobile app usage; Customer segmentation; Latent class model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10660-022-09542-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:elcore:v:23:y:2023:i:4:d:10.1007_s10660-022-09542-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10660

DOI: 10.1007/s10660-022-09542-y

Access Statistics for this article

Electronic Commerce Research is currently edited by James Westland

More articles in Electronic Commerce Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().