Evaluating Customer Segmentation Techniques in the Retail Sector.

Nur Diyabi; Duygu Çakır; Ömer Melih Gül; Tevfik Aytekin; Seifedine Kadry

doi:10.9781/ijimai.2025.05.001

Authors

Nur Diyabi Department of Computer Engineering, Bahcesehir University.
Duygu Çakır Department of Software Engineering, Bahcesehir University.
Ömer Melih Gül Informatics Institute, Istanbul Technical University.
Tevfik Aytekin Department of Computer Engineering, Bahcesehir University.
Seifedine Kadry Department of Computer Science and Mathematics, Lebanese American University.

DOI:

https://doi.org/10.9781/ijimai.2025.05.001

Keywords:

Clustering Algorithms, Customer Segmentation, Machine Learning, Retail Analysis, Unsupervised Learning

Abstract

In the current competitive corporate landscape, understanding client preferences and adapting marketing strategies accordingly has become crucial. This study evaluates the effectiveness of four machine learning algorithms (K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), and Self-Organizing Maps (SOM)) for customer segmentation in the Turkish retail market. Two datasets were analyzed: a large-scale Turkish market sales dataset and a focused marketing campaign dataset. The research employed a comprehensive methodology encompassing data preparation, algorithm application, and performance evaluation using metrics such as the Calinski-Harabasz Index and Davies- Bouldin score. Results indicate that K-Means demonstrated superior performance in terms of interpretability and statistical validity. DBSCAN showed strengths in identifying non-spherical clusters, while GMM and SOM provided more granular segmentation. The findings offer actionable insights for Turkish retailers to optimize marketing strategies and enhance customer relationship management. This study contributes to the field of retail analytics by providing a methodological framework for evaluating customer segmentation techniques in specific market contexts.

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