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Identifying and characterizing employee groups by turnover risk using predictive analytics
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Resumo(s)
This project presents a predictive analytics project developed in a European multinational to
understand and predict the turnover of its employees. It analyses the Human Resources current
challenges, such as the increasing global competition for talent, where players compete for scarce
skillsets such as technology and data science, and the new strategies necessary to deal with this
scenario. The study explores the literature review of these contextual matters and of the studies of
variables that influence turnover, generating insights and input for applying techniques aligned with
the new mindset of identifying ‘flight-risk’ groups and developing targeted actions instead of only
one-size-fits-all solutions. The project gathered data from different sources of the organization,
designed variables, based on a literature review and internal brainstorms, treated data quality issues,
transformed the data and applied three different machine learning algorithms to develop a
classification predictive model. The study evaluated 46 input variables and selected a set of 26 that
had higher impact on the turnover which were used in the models. Finally, it applied clustering
techniques to divide employees in clusters, and identified two containing more extreme turnover
behaviors (“Loyal” and “Flight risk”) and described them accordingly to their main characteristics
contributing with practical insights to support potential decisions.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Palavras-chave
Turnover Attrition Human Resources People Analytics Machine Learning Predictive Analytics