Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sucharitha, M. Martha; Kumar, J. P. Senthil; Sateesha, G.; Prasad, M. V. Ram; Basha, Md. Shaik Amzad Title A name given to the resource A shap-enhanced PCA-DBSCAN framework for interpretable retail customer segmentation and strategic insight Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of System Assurance Engineering and Management;Volume;17;Issue;2;pp.543-607 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s13198-025-02983-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13198-025-02983-2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018752863?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018752863?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sucharitha M.M., Christ (Deemed to Be University), Bangalore, India; Kumar J.P.S., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bangalore, India; Sateesha G., International Institute of Business Study, Bangalore, India; Prasad M.V.R., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bangalore, India; Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bangalore, India Description An account of the resource The rapid expansion of online retail underscores the critical need for precise customer segmentation to drive personalized marketing, reduce churn, and boost lifetime value. This study develops an end-to-end, highly interpretable segmentation pipeline encompassing advanced feature engineering, dimensionality reduction, exhaustive hyperparameter tuning, and robust validation to reveal stable, actionable customer groups in a large, real-world UK online-retail dataset (541,909 records). We augment the classic RFM (Recency, Frequency, Monetary) framework with: TPAC TF-IDF embeddings of item descriptions, holiday-purchase flags, and exponential recency decay; CACV net monetary value and cancellation ratios. After outlier filtering on RFM scores, we apply PCA (230 dimensions) and compare ten clustering methods (selected to represent major algorithmic paradigms: centroid-based [K-Means], probabilistic [GMM], hierarchical [BIRCH, Agglomerative], density-based [DBSCAN, OPTICS, HDBSCAN], graph-based [Spectral], message-passing [Affinity Propagation], and mode-seeking [Mean Shift]). We perform a full grid search per algorithm using a 'safe' silhouette scorer (ignoring noise) and also report Davies-Bouldin and Calinski-Harabasz indices. Temporal stability is assessed via adjusted Rand indices across time splits, and cluster interpretability is enhanced through SHAP-based feature importance analyses. By integrating textual, temporal, and cancellation behaviors into segmentation followed by systematic tuning and multi-metric validation our pipeline delivers superior cluster quality and actionable business insights compared to prior work. Segments directly enable strategic interventions: 'High-Decay Loyalists' (precision = 0.92) receive VIP retention offers yielding 2231% ROI lift, while 'At-Risk Cancellers' (recall = 0.89) trigger targeted win-back campaigns. We also demonstrate a reproducible framework for selecting both model and feature set. DBSCAN (? = 0.3, min_samples = 3 on 10 PCA components) achieved the best silhouette score (0.986), markedly exceeding the 0.72 benchmark in the literature. Agglomerative clustering (average linkage, 2 clusters) scored 0.776, while OPTICS and Spectral Clustering also outperformed classical Gaussian- or centroid-based models. A temporal ARI above 0.8 confirms cluster stability. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025. Subject The topic of the resource Customer segmentation; DBSCAN; Hyperparameter tuning; PCA; RFM + TPAC + CACV; SHAP interpretability; Silhouette score; Temporal stability Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 9756809; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online