Częstochowa: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
Prvé vydanie
146 strán
ISBN 978-83-63978-98-3
2024

Human Resource (HR) analytics has become a key strategic tool for organizations, enabling them to gain valuable insights into their workforce and make data-driven decisions. The use of HR analytics has been on the rise in recent years, with organizations increasingly recognizing its potential to improve HR processes, increase employee productivity and engagement, and reduce costs. One of the key areas where HR analytics can be applied is in the prediction of employee turnover. Employee turnover can have significant financial and organizational impacts, including the loss of valuable skills and knowledge, decreased productivity, and increased recruitment and training costs. Predictive analytics can help organizations to identify employees who are at risk of leaving and take proactive measures to retain them, ultimately reducing the overall cost of turnover.

The monograph provides a comprehensive understanding of employee turnover, including its definition, causes, consequences, and strategies for reducing it.

The monograph presents a predictive model for employee turnover on real company data, utilizing machine learning techniques. The created model is applied to predict the probability of leaving and the reason for leaving for each employee. The financial impact of the predicted turnover is integrated, resulting in the calculated cost of turnover for each employee. The suitability of predictive model implication in a Slovak company is also verified.