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Applying Shifted-Beta-Geometric and Beta-Discrete-Weibull Models for Employee Retention Curve Projection

Author

Listed:
  • Evgeny A. Antipov

    (Canadian University Dubai
    Middlesex University Dubai)

  • Anastasiia A. Gagarskaia

    (HSE University)

  • Yulia S. Trofimova

    (HSE University)

  • Elena B. Pokryshevskaya

    (HSE University
    SmartDataProducts.com)

Abstract

Employees are vital assets to any organization, and their departure can result in reduced human capital and operational disruptions. To mitigate this, companies employ predictive analysis to forecast potential employee churn. Probability-based modeling for projecting employee churn is an underexplored area in HR analytics. This paper tests the applicability of the shifted-beta-geometric (sBG) and beta-discrete-Weibull (BdW) models within the context of employee survival projection. Using data from three cohorts of employees, we compare the results of these models with each other as well as with linear and logarithmic regressions. Our key finding is the superior performance of the BdW model, which can capture differences in churn rates between employees and within employees over time. The beta distribution captures the heterogeneous employee loyalty, while the Weibull distribution effectively captures retention rate changes over time. Our research demonstrates that parsimonious probabilistic models, which require minimal data and have so far been used only in customer analytics, can be applied in HR analytics for projecting employee retention curves.

Suggested Citation

  • Evgeny A. Antipov & Anastasiia A. Gagarskaia & Yulia S. Trofimova & Elena B. Pokryshevskaya, 2025. "Applying Shifted-Beta-Geometric and Beta-Discrete-Weibull Models for Employee Retention Curve Projection," SN Operations Research Forum, Springer, vol. 6(1), pages 1-16, March.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-025-00417-0
    DOI: 10.1007/s43069-025-00417-0
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    References listed on IDEAS

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    1. Jun, Duk Bin & Kim, Kyunghoon & Park, Myoung Hwan, 2016. "Forecasting annual lung and bronchus cancer deaths using individual survival times," International Journal of Forecasting, Elsevier, vol. 32(1), pages 168-179.
    2. Boshnakov, Georgi & Kharrat, Tarak & McHale, Ian G., 2017. "A bivariate Weibull count model for forecasting association football scores," International Journal of Forecasting, Elsevier, vol. 33(2), pages 458-466.
    3. Berry, Lindsay R. & Helman, Paul & West, Mike, 2020. "Probabilistic forecasting of heterogeneous consumer transaction–sales time series," International Journal of Forecasting, Elsevier, vol. 36(2), pages 552-569.
    4. Fader, Peter S. & Hardie, Bruce G.S. & Liu, Yuzhou & Davin, Joseph & Steenburgh, Thomas, 2018. "“How to Project Customer Retention” Revisited: The Role of Duration Dependence," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 1-16.
    5. Kang, Seungwoo & Oh, Hee-Seok, 2024. "Forecasting South Korea’s presidential election via multiparty dynamic Bayesian modeling," International Journal of Forecasting, Elsevier, vol. 40(1), pages 124-141.
    6. Alho, Juha M., 2014. "Forecasting demographic forecasts," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1128-1135.
    7. Levene, Mark & Fenner, Trevor, 2021. "A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1227-1234.
    8. Gandy, Axel, 2012. "Performance monitoring of credit portfolios using survival analysis," International Journal of Forecasting, Elsevier, vol. 28(1), pages 139-144.
    9. Lassila, Jukka & Valkonen, Tarmo & Alho, Juha M., 2014. "Demographic forecasts and fiscal policy rules," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1098-1109.
    10. Fader, Peter S. & Hardie, Bruce G.S., 2009. "Probability Models for Customer-Base Analysis," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 61-69.
    11. Iversen, Emil B. & Morales, Juan M. & Møller, Jan K. & Madsen, Henrik, 2016. "Short-term probabilistic forecasting of wind speed using stochastic differential equations," International Journal of Forecasting, Elsevier, vol. 32(3), pages 981-990.
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