Identifying and characterizing employee groups by turnover risk using predictive analytics

Bibliographic Details
Main Author: Vidotto, Bruno Cassanta
Publication Date: 2021
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/118203
Summary: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Identifying and characterizing employee groups by turnover risk using predictive analyticsTurnoverAttritionHuman ResourcesPeople AnalyticsMachine LearningPredictive AnalyticsProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis 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.Naranjo-Zolotov, Mijail JuanovichRUNVidotto, Bruno Cassanta2021-05-24T17:04:04Z2021-05-062021-05-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/118203TID:202729290enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:53:26Zoai:run.unl.pt:10362/118203Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:24:22.937190Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Identifying and characterizing employee groups by turnover risk using predictive analytics
title Identifying and characterizing employee groups by turnover risk using predictive analytics
spellingShingle Identifying and characterizing employee groups by turnover risk using predictive analytics
Vidotto, Bruno Cassanta
Turnover
Attrition
Human Resources
People Analytics
Machine Learning
Predictive Analytics
title_short Identifying and characterizing employee groups by turnover risk using predictive analytics
title_full Identifying and characterizing employee groups by turnover risk using predictive analytics
title_fullStr Identifying and characterizing employee groups by turnover risk using predictive analytics
title_full_unstemmed Identifying and characterizing employee groups by turnover risk using predictive analytics
title_sort Identifying and characterizing employee groups by turnover risk using predictive analytics
author Vidotto, Bruno Cassanta
author_facet Vidotto, Bruno Cassanta
author_role author
dc.contributor.none.fl_str_mv Naranjo-Zolotov, Mijail Juanovich
RUN
dc.contributor.author.fl_str_mv Vidotto, Bruno Cassanta
dc.subject.por.fl_str_mv Turnover
Attrition
Human Resources
People Analytics
Machine Learning
Predictive Analytics
topic Turnover
Attrition
Human Resources
People Analytics
Machine Learning
Predictive Analytics
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2021
dc.date.none.fl_str_mv 2021-05-24T17:04:04Z
2021-05-06
2021-05-06T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/118203
TID:202729290
url http://hdl.handle.net/10362/118203
identifier_str_mv TID:202729290
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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