Previsão de churn na borda: uma solução com atributos temporais e preservação de privacidade
Abstract
This paper proposes a solution for predicting churn with privacy preservation by using edge computing. With the increasing popularity of smartphones, users are becoming more demanding regarding mobile app usage. Installing and removing an app are frequent routines and the ease of uninstallation can facilitate churn, which is customer abandonment. Companies seek to minimize churn since the cost of acquiring new customers is much higher than retaining current ones. To predict possible abandonment, organizations are increasingly adopting artificial intelligence (AI) techniques. Nevertheless, customers are becoming more concerned about their data privacy. In this context, we propose a technique called CANCEL, which creates attributes based on users’ temporal behavior, with edge computing to predict churn locally, without transmitting users’ data. The paper presents the evaluation of CANCEL in comparison to baseline solutions, the development of a mobile app integrated with the proposed method and deployed as an edge computing solution.
References
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Rahman, M. and Kumar, V. (2020). Machine learning based customer churn prediction in banking. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pages 1196–1201.
Rajamohamed, R. and Manokaran, J. (2018). Improved credit card churn prediction based on rough clustering and supervised learning techniques. Cluster Computing, 21(1):65–77.
Schlackl, F., Link, N., and Hoehle, H. (2022). Antecedents and consequences of data breaches: A systematic review. Information & Management, 59(4):103638.
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Zhou, J., Yan, J.-f., Yang, L., Wang, M., and Xia, P. (2019). Customer churn prediction model based on lstm and cnn in music streaming. DEStech Transactions on Engineering and Technology Research, 5.
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS ’17, page 1175–1191, New York, NY, USA. Association for Computing Machinery.
Capanema, C. and Silva, F. (2021). Detecção de pontos de interesse e predição de próximo local de visita de usuários móveis com base em dados esparsos. In Anais Estendidos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 129–136, Porto Alegre, RS, Brasil. SBC.
Fox, G., van der Werff, L., Rosati, P., Takako Endo, P., and Lynn, T. (2022). Examining the determinants of acceptance and use of mobile contact tracing applications in brazil: An extended privacy calculus perspective. Journal of the Association for Information Science and Technology, 73(7):944–967.
Gupta, S., Lehmann, D. R., and Stuart, J. A. (2004). Valuing customers. Journal of marketing research, 41(1):7–18.
Haddadi, S. J., Mohammadi, M. O., Bahrami, M., Khoeini, E., Beygi, M., and Khoshkar, M. H. (2022). Customer churn prediction in the iranian banking sector. In 2022 International Conference on Applied Artificial Intelligence (ICAPAI), pages 1–6.
Insights, F. (2011). Bringing 20/20 foresight to marketing. May ([link]).
Jain, H., Khunteta, A., and Srivastava, S. (2020). Churn prediction in telecommunication using logistic regression and logit boost. Procedia Computer Science, 167:101–112.
Maia, W., Silva, F., and Silva, T. (2020). Um estudo sobre a relação entre smartphones e dados demográficos. In Anais do IV Workshop de Computação Urbana, pages 302–315, Porto Alegre, RS, Brasil. SBC.
Mammen, P. M. (2021). Federated learning: Opportunities and challenges. arXiv preprint arXiv:2101.05428.
Milošević, M., Živić, N., and Andjelković, I. (2017). Early churn prediction with personalized targeting in mobile social games. Expert Systems with Applications, 83:326–332.
Rahman, M. and Kumar, V. (2020). Machine learning based customer churn prediction in banking. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pages 1196–1201.
Rajamohamed, R. and Manokaran, J. (2018). Improved credit card churn prediction based on rough clustering and supervised learning techniques. Cluster Computing, 21(1):65–77.
Schlackl, F., Link, N., and Hoehle, H. (2022). Antecedents and consequences of data breaches: A systematic review. Information & Management, 59(4):103638.
Tan, F., Wei, Z., He, J., Wu, X., Peng, B., Liu, H., and Yan, Z. (2018). A blended deep learning approach for predicting user intended actions. In 2018 IEEE International Conference on Data Mining (ICDM), pages 487–496.
Verhelst, T. (2018). Churn prediction and causal analysis on telecom customer data.
Yang, C., Shi, X., Jie, L., and Han, J. (2018). I know you’ll be back: Interpretable new user clustering and churn prediction on a mobile social application. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’18, page 914–922, New York, NY, USA. Association for Computing Machinery.
Zhou, J., Yan, J.-f., Yang, L., Wang, M., and Xia, P. (2019). Customer churn prediction model based on lstm and cnn in music streaming. DEStech Transactions on Engineering and Technology Research, 5.
Published
2023-08-06
How to Cite
COIMBRA, Gabriel T.; SANTOS, Victor Hugo R.; MAIA, Pedro A.; SILVA, Letícia O.; SOUZA, Rayanne P.; SILVA, Fabrício A.; SILVA, Thais R. M. Braga.
Previsão de churn na borda: uma solução com atributos temporais e preservação de privacidade. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 15. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 121-130.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2023.230498.
