Integrating Hybrid Clustering Techniques for Advanced Retail Segmentation

Customer segmentation theatres a key role in retail, permitting businesses to adapt marketing policies, optimize reserve allocation, and expand general customer completion. Traditional clustering methods, such as K-means and DBSCAN, have been broadly employed for this drive. However, each of these methods has its limitations, such as compassion to noise in DBSCAN or the statement of round clusters in K-means. To address these boundaries, this paper proposes a hybrid clustering approach that mixes multiple clustering methods, including K-means, DBSCAN, Spectral Clustering, Fuzzy C-means, and Hierarchical Clustering. By combination the resources of these methods, the hybrid approach distributes more careful and sensitive segmentation results, exclusively in complex retail atmospheres where customer behaviour is assorted and data structures are involved. The results establish that the hybrid approach knowingly improves division truth compared to individual methods. The combined clustering techniques allow the model to detect both distinct, well-separated clusters and more nuanced, overlapping segments, providing a more comprehensive view of customer behaviors. This enhanced segmentation authorizes retailers to make data-driven decisions, such as better-targeted marketing operations, more efficient record management, and personalized customer experiences. The paper concludes by discussing the potential for further research in applying hybrid clustering models in other sectors and integrating predictive analytics for even more refined customer insights.