Examining Selected Theoretical Distributions of Life Expectancy to Analyse Customer Loyalty Durability. The Case of a European Retail Bank


  • Dominik Kubacki University of Łódź, Faculty of Economics and Sociology, Department of Banking http://orcid.org/0000-0003-2389-0168
  • Robert Kubacki




survival analysis, customer lifetime value, banking, parametric models, Kaplan–Meier estimator


One of the key elements related to calculating Customer Lifetime Value is to estimate the duration of a client’s relationship with a bank in the future. This can be done using survival analysis. The aim of the article is to examine which of the known distributions used in survival analysis (Weibull, Exponential, Gamma, Log‑normal) best describes the churn phenomenon of a bank’s clients. If the aim is to estimate the distribution according to which certain units (bank customers) survive and the factors that cause this are not so important, then parametric models can be used. Estimation of survival function parameters is faster than estimating a full Cox model with a properly selected set of explanatory variables. The authors used censored data from a retail bank for the study. The article also draws attention to the most common problems related to preparing data for survival analysis.


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Akaike H. (1974), A New Look at the Statistical Model Identification, “IEEE. Transactions on Automatic Control”, vol. Ac–19, no. 6, pp. 716–723.
Google Scholar

Balicki A. (2006), Analiza przeżycia i tablice wymieralności, Polskie Wydawnictwo Ekonomiczne, Warszawa.
Google Scholar

Erişoğlu Ü., Erişoğlu M., Erol H. (2011), A Mixture Model of Two Different Distributions Approach to the Analysis of Heterogeneous Survival Data, “World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering”, vol. 5, no. 6, pp. 544–548.
Google Scholar

Jackson C. (2016), flexsurv: A Platform for Parametric Survival Modeling in R, “Journal of Statistical Software”, vol. 70, no. 8, pp. 1–33.
Google Scholar

Jajuga K., Walesiak M. (1999), Standaridisation of data set under different measurement scales, [in]: Classification and Information Processing at the Turn of the Millennium: Proceedings of the 23rd Annual Conference of the Gesellschaftfür Klassifikatione.V., University of Bielefeld, Bielefeld, pp. 105–112.
Google Scholar

Jeffery M. (2010), Data‑Driven Marketing. The 15 Metrics Everyone in Marketing Should Know, John Wiley & Sons, Hoboken.
Google Scholar

Kaplan E. L., Meier P. (1958), Nonparametric Estimation from Incomplete Observations, “Journal of the American Statistical Association”, vol. 53, no. 282, pp. 457–481.
Google Scholar

The Comprehensive R Archive Network, https://cran.r-project.org/ (accessed: 23.03.2019).
Google Scholar




How to Cite

Kubacki, D., & Kubacki, R. (2020). Examining Selected Theoretical Distributions of Life Expectancy to Analyse Customer Loyalty Durability. The Case of a European Retail Bank. Acta Universitatis Lodziensis. Folia Oeconomica, 4(349), 81-92. https://doi.org/10.18778/0208-6018.349.05