A Hybrid Approach for Customer Segmentation and Loyalty Prediction in E-Commerce

Authors

  •   Elamurugan Balasundaram Associate Professor, Department of Management Studies, Sri Manakula Vinayagar Engineering College, Madagadipet, Mannadipet Commune - 605 107, Puducherry
  •   P. Aranganathan Associate Professor, Gnanam School of Business, Mary’s Nagar, Trichy-Thanjavur Express Highway, Sengipatty, Thanjavur - 613 402, Tamil Nadu
  •   Krishna Sudhir Annavajjala Professor & HOD, Department of MBA, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur District, Andhra Pradesh
  •   R. Sivakumar Assistant Professor, Department of Management Studies, Sri Manakula Vinayagar Engineering College, Madagadipet, Mannadipet Commune - 605 107, Puducherry
  •   Mathiazhagan Arumugam Assistant Professor, Department of Management Studies, Sri Manakula Vinayagar Engineering College, Madagadipet, Mannadipet Commune - 605 107, Puducherry
  •   A. Vinoth Assistant Professor, Department of Management Studies, Sri Manakula Vinayagar Engineering College, Madagadipet, Mannadipet Commune - 605 107, Puducherry

DOI:

https://doi.org/10.17010/pijom/2024/v17i10/173996

Keywords:

customer segmentation

, loyalty prediction, e-commerce, k-means clustering, XGBoost.

JEL Classifications Codes

, C38, C45, C55, L81, M31

Paper Submission Date

, September 20, 2023, Paper sent back for Revision, May 24, 2024, Paper Acceptance Date, July 15, Paper Published Online, October 15, 2024

Abstract

Purpose : This study aimed to enhance e-commerce customer segmentation and loyalty prediction by integrating machine learning (ML) with traditional statistical methods.

Design/Methodology/Approach : The research adopted a hybrid approach, utilizing k-means clustering for customer segmentation based on recency, frequency, and monetary values, followed by an XGBoost classifier application for loyalty prediction. The methodology involved analyzing actual e-commerce data and comparing results with established industry benchmarks.

Findings : The hybrid model demonstrated superior performance over conventional methods, evidenced by improved precision, recall, accuracy, and F1 scores in loyalty prediction, alongside higher silhouette scores and lower Davies–Bouldin indices for customer segmentation.

Practical Implications : The approach provided a more generalized, interpretable, and high-quality framework for e-commerce businesses to understand customer behavior and enhance retention strategies.

Originality/Value : The research contributed to the field by presenting a novel method that successfully combines ML and statistical analysis, offering a more effective solution for customer segmentation and loyalty prediction in e-commerce settings.

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Published

2024-10-15

How to Cite

Balasundaram, E., Aranganathan, P., Annavajjala, K. S., Sivakumar, R., Arumugam, M., & Vinoth, A. (2024). A Hybrid Approach for Customer Segmentation and Loyalty Prediction in E-Commerce. Prabandhan: Indian Journal of Management, 17(10), 56–69. https://doi.org/10.17010/pijom/2024/v17i10/173996

Issue

Volume 17, Issue 10, October 2024

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

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