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

Author

Listed:
  • Xuebin Cui

    (Nanjing University)

  • Fei Jin

    (Sichuan University)

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.

Suggested Citation

  • Xuebin Cui & Fei Jin, 2023. "Unraveling mobile internet behavior through customer segmentation: a latent class analysis," Electronic Commerce Research, Springer, vol. 23(4), pages 2379-2398, December.
    • Handle: RePEc:spr:elcore:v:23:y:2023:i:4:d:10.1007_s10660-022-09542-y
    DOI: 10.1007/s10660-022-09542-y
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    References listed on IDEAS

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