The main goal of employee retention is to prevent competent employees from leaving the company. When analysing the main reasons why employees leave and determining their turnover probability, the question arises: Which retention strategies have an actual effect on turnover and for which profile of employees do these strategies work?
To determine the effectiveness of different retention strategies, an overview is given of retention strategies that can be found in the literature. Next, the paper presents a procedure to build an uplift model for testing the effectiveness of the different strategies on HR data. The uplift model is based on random forest estimation and applies personal treatment learning estimation.
Through a data-driven approach, the actual effect of retention strategies on employee turnover is investigated. The retention strategies compensation and recognition are found to have a positive average treatment effect on the entire population, while training and flexibility do not. However, with personalised treatment learning, the treatment effect on the individual level can be estimated. This results in an ability to profile employees with the highest estimated treatment effect.
The results yield useful information for human resources practitioners. The personalised treatment analysis results in detailed retention information for these practitioners, which allows them to target the right employees with the right strategies.
Even though the uplift modelling approach is becoming increasingly popular within marketing, this approach has not been taken within human resources analytics. This research opens the door for further research and for practical implementation.
