Employee turnover prediction and retention policies design: a case study

TL;DR

This paper explores employee turnover prediction using machine learning and introduces a novel approach to designing and testing retention policies based on model outputs, inspired by customer churn analysis.

Contribution

It presents a machine learning model for employee turnover prediction and introduces an innovative method for designing retention policies based on model insights.

Findings

Predictive model effectively identifies employees at risk of leaving.

Retention policies designed using model outputs show potential for reducing turnover.

The approach bridges customer churn analysis techniques to employee retention strategies.

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.