Machine Learning-Based Customer Segmentation: A Comprehensive Investigation of Techniques, Challenges and Applications

Yazhi Zhang

2024

Abstract

Customer segmentation is vital for optimizing targeted marketing strategies, improving customer experiences, and driving profitability across various industries. This study proposes to provide a comprehensive analysis of how machine learning can improve segmentation accuracy and offer deeper insights into customer behaviours. To achieve this, this paper conducted a detailed examination of machine learning methods used in customer segmentation across banking, telecommunications, and healthcare industries. The methods reviewed include decision trees, random forests, k-means clustering, hierarchical clustering, auto machine learning (AutoML) tools like H2O, and deep learning models. The study also involved analyzing specific machine learning workflows, including problem definition, data collection, preprocessing with techniques like Local Outlier Factor (LOF) and Principal Component Analysis (PCA), model selection, training, evaluation, and deployment. Each industry-specific case study was scrutinized to emphasize the effectiveness and risks of these methods in real-world applications. The results demonstrate that while machine learning significantly enhances customer segmentation, it also introduces challenges related to model interpretability, domain applicability, and privacy concerns. The study supports the hypothesis that incorporating interpretability tools like SHAP and LIME, leveraging transfer learning, and adopting federated learning are crucial for overcoming these challenges.

Paper Citation

in Harvard Style

Zhang Y. (2024). Machine Learning-Based Customer Segmentation: A Comprehensive Investigation of Techniques, Challenges and Applications. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 97-101. DOI: 10.5220/0013207200004568

in Bibtex Style

@conference{ecai24,
author={Yazhi Zhang},
title={Machine Learning-Based Customer Segmentation: A Comprehensive Investigation of Techniques, Challenges and Applications},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={97-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013207200004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Machine Learning-Based Customer Segmentation: A Comprehensive Investigation of Techniques, Challenges and Applications
SN - 978-989-758-726-9
AU - Zhang Y.
PY - 2024
SP - 97
EP - 101
DO - 10.5220/0013207200004568
PB - SciTePress