Salesperson Churn Prediction with Machine Learning Approaches in the Retail Industry

Gizem Deniz Cömert; Tuncay Özcan; Tolga Kaya

doi:10.1007/978-3-031-24457-5_3

Salesperson Churn Prediction with Machine Learning Approaches in the Retail Industry

Gizem Deniz Cömert^*, Tuncay Özcan, Tolga Kaya

^*Corresponding author for this work

Department of Management Engineering

Kirklareli University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Due to its high cost, loss of productivity, and most importantly, loss of time in training a new employee, employee retention has become a strategy that has made even more attractive for many researchers and professionals in the field. The purpose of this study is to present a case study that addresses the problem of employee churn and develop a model which predicts employee retention best. In the present study, the most well-known machine learning techniques such as Logistic Regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Support Vector Machine (SVM), XGBoost, Artificial Neural Network (ANN) and Random Forest were used. Finally, the performance of the proposed approaches was evaluated. The numerical results showed that the proposed Naïve Bayes clearly outperformed all other classifiers according to all evaluation criteria except Accuracy. However, Random Forest gave the best results according to the accuracy criterion.

Original language	English
Title of host publication	Towards Industry 5.0 - Selected Papers from ISPR 2022
Editors	Numan M. Durakbasa, M. Güneş Gençyılmaz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	25-31
Number of pages	7
ISBN (Print)	9783031244568
DOIs	https://doi.org/10.1007/978-3-031-24457-5_3
Publication status	Published - 2023
Event	22nd International Symposium for Production Research, ISPR 2022 - Antalya, Turkey Duration: 6 Oct 2022 → 8 Oct 2022

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	22nd International Symposium for Production Research, ISPR 2022
Country/Territory	Turkey
City	Antalya
Period	6/10/22 → 8/10/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Binary classification
Employee churn prediction
Machine learning techniques
Random forest
XGBoost

Access to Document

10.1007/978-3-031-24457-5_3

Cite this

Cömert, G. D., Özcan, T., & Kaya, T. (2023). Salesperson Churn Prediction with Machine Learning Approaches in the Retail Industry. In N. M. Durakbasa, & M. G. Gençyılmaz (Eds.), Towards Industry 5.0 - Selected Papers from ISPR 2022 (pp. 25-31). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-24457-5_3

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title = "Salesperson Churn Prediction with Machine Learning Approaches in the Retail Industry",

abstract = "Due to its high cost, loss of productivity, and most importantly, loss of time in training a new employee, employee retention has become a strategy that has made even more attractive for many researchers and professionals in the field. The purpose of this study is to present a case study that addresses the problem of employee churn and develop a model which predicts employee retention best. In the present study, the most well-known machine learning techniques such as Logistic Regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Support Vector Machine (SVM), XGBoost, Artificial Neural Network (ANN) and Random Forest were used. Finally, the performance of the proposed approaches was evaluated. The numerical results showed that the proposed Na{\"i}ve Bayes clearly outperformed all other classifiers according to all evaluation criteria except Accuracy. However, Random Forest gave the best results according to the accuracy criterion.",

keywords = "Binary classification, Employee churn prediction, Machine learning techniques, Random forest, XGBoost",

author = "C{\"o}mert, {Gizem Deniz} and Tuncay {\"O}zcan and Tolga Kaya",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 22nd International Symposium for Production Research, ISPR 2022 ; Conference date: 06-10-2022 Through 08-10-2022",

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doi = "10.1007/978-3-031-24457-5_3",

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Cömert, GD, Özcan, T & Kaya, T 2023, Salesperson Churn Prediction with Machine Learning Approaches in the Retail Industry. in NM Durakbasa & MG Gençyılmaz (eds), Towards Industry 5.0 - Selected Papers from ISPR 2022. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 25-31, 22nd International Symposium for Production Research, ISPR 2022, Antalya, Turkey, 6/10/22. https://doi.org/10.1007/978-3-031-24457-5_3

Salesperson Churn Prediction with Machine Learning Approaches in the Retail Industry. / Cömert, Gizem Deniz; Özcan, Tuncay ; Kaya, Tolga.
Towards Industry 5.0 - Selected Papers from ISPR 2022. ed. / Numan M. Durakbasa; M. Güneş Gençyılmaz. Springer Science and Business Media Deutschland GmbH, 2023. p. 25-31 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Cömert, Gizem Deniz

AU - Özcan, Tuncay

AU - Kaya, Tolga

PY - 2023

Y1 - 2023

N2 - Due to its high cost, loss of productivity, and most importantly, loss of time in training a new employee, employee retention has become a strategy that has made even more attractive for many researchers and professionals in the field. The purpose of this study is to present a case study that addresses the problem of employee churn and develop a model which predicts employee retention best. In the present study, the most well-known machine learning techniques such as Logistic Regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Support Vector Machine (SVM), XGBoost, Artificial Neural Network (ANN) and Random Forest were used. Finally, the performance of the proposed approaches was evaluated. The numerical results showed that the proposed Naïve Bayes clearly outperformed all other classifiers according to all evaluation criteria except Accuracy. However, Random Forest gave the best results according to the accuracy criterion.

AB - Due to its high cost, loss of productivity, and most importantly, loss of time in training a new employee, employee retention has become a strategy that has made even more attractive for many researchers and professionals in the field. The purpose of this study is to present a case study that addresses the problem of employee churn and develop a model which predicts employee retention best. In the present study, the most well-known machine learning techniques such as Logistic Regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Support Vector Machine (SVM), XGBoost, Artificial Neural Network (ANN) and Random Forest were used. Finally, the performance of the proposed approaches was evaluated. The numerical results showed that the proposed Naïve Bayes clearly outperformed all other classifiers according to all evaluation criteria except Accuracy. However, Random Forest gave the best results according to the accuracy criterion.

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ER -

Salesperson Churn Prediction with Machine Learning Approaches in the Retail Industry

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Keywords

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