Employee turnover prediction and retention policies design: a case study

Edouard Ribes (1) , Karim Touahri (2) , Benoît Perthame (3)

1 IRSEM - Institut de recherche stratégique de l'Ecole militaire
2 UPD5 - Université Paris Descartes - Paris 5
3 LJLL - Laboratoire Jacques-Louis Lions

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.

Mots clés: Churn prediction; Machine learning techniques; Employee Turnover; Classifi- cation; Retention Policy; Workforce Planning

Domaines: Economies et finances; Gestion et management; Méthodes et statistiques; Equations aux dérivées partielles [math.AP]; Machine Learning [stat.ML]; Econométrie de la finance [q-fin.ST]

Edouard Ribes, Karim Touahri, Benoît Perthame. Employee turnover prediction and retention policies design: a case study. 2017. ⟨hal-01556746⟩