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

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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.

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Notes

  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.

  3. Source: https://www.latentclassanalysis.com/software/lca-stata-plugin/.

  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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Acknowledgements

Xuebin Cui acknowledges the support by the National Natural Science Foundation of China (Grant 72002096) and the Fundamental Research Funds for the Central Universities (Grant 010414370113). Fei Jin acknowledges the support by the National Natural Science Foundation of China (Grant 72002143), China Postdoctoral Science Foundation (2019M663545), the Youth Foundation for Humanities and Social Sciences of the Ministry of Education of China (20YJC630053) and the Fundamental Research Funds for the Central Universities (skbsh2020-21).

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Authors and Affiliations

  1. Department of Marketing and Electronic Business at the School of Business, Nanjing University, 22 Hankou Road, Gulou District, Nanjing, 210093, Jiangsu, China

    Xuebin Cui

  2. Department of Marketing and Electronic Business at the Business School, Sichuan University, No. 24 Southern Yihuan, Chengdu, 610065, Sichuan, China

    Fei Jin

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  1. Xuebin Cui
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Cui, X., Jin, F. Unraveling mobile internet behavior through customer segmentation: a latent class analysis. Electron Commer Res 23, 2379–2398 (2023). https://doi.org/10.1007/s10660-022-09542-y

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