TY - JOUR
KW - Clustering Algorithms
KW - Customer Segmentation
KW - Machine Learning
KW - Retail Analysis
KW - Unsupervised Learning
AU - Nur Diyabi
AU - Duygu Çakır
AU - Ömer Melih Gül
AU - Tevfik Aytekin
AU - Seifedine Kadry
AB - 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.
IS - Regular issue
M1 - 3
N2 - 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.
PY - 2025
SE - 175
SP - 175
EP - 190
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Evaluating Customer Segmentation Techniques in the Retail Sector
UR - https://www.ijimai.org/journal/bibcite/reference/3582
VL - 9
SN - 1989-1660
ER -