<![CDATA[Purpose – This study aims to improve customer segmentation quality by developing a hybrid clustering approach that integrates centroid-based and density-based techniques to better capture complex data structures and noise in customer datasets. Design/methods/approach – The proposed method combines K-Means and DBSCAN in a sequential hybrid framework. K-Means is first applied to identify the global structure of customer groups using centroid similarity. Subsequently, DBSCAN is performed within each cluster to refine cluster boundaries and detect noise points. The dataset is preprocessed using Min–Max normalization, and clustering performance is evaluated using the Silhouette Score and Davies–Bouldin Index. Findings - Experimental results show that the hybrid approach outperforms standalone methods. The proposed model achieves a Silhouette Score of 0.71 and a Davies–Bouldin Index of 0.42, indicating improved cluster compactness and separation. Additionally, the method successfully identifies 6% of data points as noise, enhancing segmentation reliability and interpretability. Research implications/limitations – This study demonstrates the effectiveness of combining clustering paradigms for improved segmentation. However, the evaluation is limited to a relatively small dataset with three features, and DBSCAN parameter selection remains data-dependent. Future research may explore larger datasets, higher-dimensional features, and automated parameter optimization techniques. Originality/value – This research contributes a practical hybrid clustering framework that integrates K-Means and DBSCAN in a structured manner, enabling more robust, interpretable, and noise-aware customer segmentation suitable for data-driven marketing analytics and decision support systems.]]>
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