Employee profile and labor turnover in outsourcing companies: A data mining approach

Abstract

Data mining can be applied to seek hidden information on large volumes of data. On Human Resources Management, it helps to identify reasons behind turnover and employee behavior. That knowledge leads to identify unwanted employee profiles and help to improve personnel selection processes, which are a media to reduce the turnover rate in companies. In this paper we analyzed the situation of a Human Resources Outsourcing company and tested various data mining techniques to compare which presented a better performance and had a better suitability to classify labor turnover on low skill employees of an outsourcing company. A limitation for this research was the partial absence of sociodemographic data in the employees data bases as well variables related to organizational climate and culture. Through the CRISP-DM methodology we created and evaluated different classification models and obtained a list of relevant characteristics of employee’s profiles prone to turnover. The results showed that Age, Salary, Location and Work Experience in Time and Area are key factors which help to classify turnover and can be used to suggest personnel selection policies to the company. This research implied the analysis of a Human Resources Outsourcing company and low skill employee’s data, of which little research has been done in both approaches. The results obtained can help other companies with low skill employees or even other Human Resources Outsourcing to have a framework of where to start to get data of employees and analysis the profiles prone to turnover