HR Analytics in Retail: Predicting Employee Churn with Machine Learning

Serhan Berke Erden*, Mert Erişen, Yavuz Nuri Sarıgül, Buse Eken, Tuncay Özcan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The retail sector is facing operational inefficiencies and high recruitment costs due to the increasing turnover rate. This project focuses on identifying and analyzing the key factors leading to the departure of qualified personnel. The data discussed is a real-life HR analytical case of a company working in the retail industry. Using advanced machine learning algorithms such as LGBM, XGBoost, AdaBoost, and CatBoost, the study seeks to reveal the relationship between variables like education level, city, and age. The project provides actionable insights that can inform strategic decisions by using both qualitative and quantitative data sources. According to the results, the most successful model is discovered as CatBoost. The findings indicate that the employee’s average sales and its coefficient of variation, trends of sales, and age of employee play crucial roles in employee churn. To interpret these, an increase in an employee's sales rates correlates with a higher likelihood of retaining their position. Actions taken in light of the project's findings can contribute to companies predicting employee churn in advance, thereby reducing turnover rates and improving operational costs.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
EditorsCengiz Kahraman, Sezi Cevik Onar, Selcuk Cebi, Basar Oztaysi, Irem Ucal Sari, A. Cagrı Tolga
PublisherSpringer Science and Business Media Deutschland GmbH
Pages109-116
Number of pages8
ISBN (Print)9783031671913
DOIs
Publication statusPublished - 2024
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2024 - Canakkale, Turkey
Duration: 16 Jul 202418 Jul 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1090 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2024
Country/TerritoryTurkey
CityCanakkale
Period16/07/2418/07/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • AdaBoost
  • ANOVA
  • Catboost
  • Employee Churn
  • HR Analytics
  • LGBM
  • Machine Learning
  • Statistical Tests
  • Turnover
  • XGBoost

Fingerprint

Dive into the research topics of 'HR Analytics in Retail: Predicting Employee Churn with Machine Learning'. Together they form a unique fingerprint.

Cite this