Employee turnover prediction and retention policies design: a case study

Edouard Ribes (), Karim Touahri () and Benoît Perthame ()
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Edouard Ribes: IRSEM - Institut de recherche stratégique de l'Ecole militaire - Ministère des armées
Karim Touahri: UPD5 - Université Paris Descartes - Paris 5
Benoît Perthame: LJLL - Laboratoire Jacques-Louis Lions - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique

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Abstract: This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design & test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.

Keywords: Churn prediction; Machine learning techniques; Employee Turnover; Classifi- cation; Retention Policy; Workforce Planning (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-big
Note: View the original document on HAL open archive server: https://hal.science/hal-01556746
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