HR Analytics and Employee Turnover: A Data-Driven Investigation

This report delves into the application of data science and HR analytics to predict employee turnover. It begins by highlighting the significance of employee attrition as a critical issue for organizations. The report discusses the limitations of traditional methods like survival curves and logistic regression, and emphasizes the use of machine learning for predicting employee performance and identifying key factors associated with attrition. The purpose of this study is to analyze the factors influencing employee turnover using data mining techniques, with the goal of offering recommendations for effective human resource planning. The research objectives include leveraging predictive analytics in HR and formulating research questions to understand the essential elements of implementing predictive analytics and its value in HR. The report aims to provide a data-driven approach to understanding and addressing employee turnover, ultimately contributing to improved retention strategies and more effective human resource management.

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Data Analytics and Business Intelligence
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Predict Employee Turnover using Human Resource Analytics
Introduction
Employee turnover specifically through attrition has become a major expense to many
organisations. As a result, it has made predicting turnover to be at the frontline need of
human resource in most companies. Accordingly, till recently, the mainstream method has
been the use of survival curves or logistic regression for modelling employee attrition
(Albrecht et al., 2015, p. 9). Nevertheless, with the dawn of machine learning (ML) it is now
possible to predict employee performance and providing explanations about the critical
aspects associated with employee attrition.
Problem Statement
The war about talent has become a crucial challenge in most of the organisation today.
Certainly, corporations are competing on attracting and retaining the talent, developing
workforce as well as takin care of their well-being. Accordingly, understanding predictive
analytics as well as being in a better position to leverage it on day to day operations and
support human resource business operations in this current challenge.
Purpose of the study
The objective of this paper is to analyse the aspects that influence turnover among employees
in companies through data mining techniques. Certainly, a range of predictive models are
employed to recognise the turnover occurrence. The outcome of this analysis would offer
recommendations to provide effectiveness and efficiency regarding human resource planning
procedure that should be utilised to focus on the problem of employee turnover (Guest, 2017,
p. 22).
Research objectives

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The objective of this research is to leverage predictive analytics in HR and the assumed
assumption of doing so. In this essence the research will use the following research questions.
Research questions
What are the key building blocks required for implementing predictive analytics in HR?
What is the value of leveraging predictive analytics in HR?

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Bibliography
Albrecht, S.L., Bakker, A.B., Gruman, J.A., Macey, W.H. and Saks, A.M., 2015. Employee
engagement, human resource management practices and competitive advantage: An
integrated approach. Journal of Organizational Effectiveness: People and Performance, 2(1),
pp.7-35.
Guest, D.E., 2017. Human resource management and employee well‐being: Towards a new
analytic framework. Human Resource Management Journal, 27(1), pp.22-38.