Comparative study of customer segmentation strategies based on business analytics
This thesis explores a comparative analysis of customer segmentation strategies supported by advanced analytical methodologies. It focuses on two foundational frameworks: Recency, Frequency, Monetary (RFM) and Customer Lifetime Value (CLV), which respectively capture short-term transactional behavio...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/429129 |
| Acceso en línea: | https://hdl.handle.net/2117/429129 |
| Access Level: | acceso abierto |
| Palabra clave: | Decision support systems Business intelligence Customer segmentation strategies Business analytics Sistemes d'ajuda a la decisió Àrees temàtiques de la UPC::Economia i organització d'empreses::Competitivitat i innovació |
| Sumario: | This thesis explores a comparative analysis of customer segmentation strategies supported by advanced analytical methodologies. It focuses on two foundational frameworks: Recency, Frequency, Monetary (RFM) and Customer Lifetime Value (CLV), which respectively capture short-term transactional behaviors and long-term economic contributions. These metrics are subsequently analyzed through five clustering algorithms: K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM), and Fuzzy C-Means. The study utilizes the UK E-Commerce data set from the UCI repository, which undergoes meticulous preprocessing and normalization to ensure robust and consistent input for the clustering models. The evaluation framework leverages two internal validation metrics—the Silhouette Score and the Calinski–Harabasz Index—to provide complementary perspectives on local density separation and global variance partitioning. Experimental results reveal that DBSCAN consistently outperforms other methods in identifying dense microclusters, often representing high-value or niche customers. In contrast, K-Means and Hierarchical Clustering exhibit stronger performance in generating broader global partitions. While Fuzzy C-Means achieves moderate results by accommodating overlapping segment boundaries through soft membership, GMM struggles with the non-Gaussian characteristics of the RFM and CLV datasets. The findings underscore that no single approach universally outperforms the others. Instead, the selection of metrics and clustering algorithms should be strategically aligned with business goals, such as identifying anomalies or performing large-scale segmentation. This study provides actionable insights for businesses aiming to enhance marketing strategies, optimize resource allocation, and strengthen customer relationship management (CRM) through data-driven segmentation approaches. |
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