Model based on data mining techniques for customer segmentation in distribution companies
DOI:
https://doi.org/10.55873/rad.v4i1.366Keywords:
artificial intelligence, clustering algorithms, customer segmentation, RFM análisis, distribution companiesAbstract
competitive markets due to the management of multiple product categories and diverse customer bases. This study aimed to design and implement a comprehensive model based on data mining techniques for effective customer segmentation in a distribution company. The KDD methodology was employed, integrating RFM analysis with the K-means algorithm and a quartile-based approach. A dataset of 44,000 transactional records covering a 10-month period from five branches was analyzed using a structured seven-step model. The results identified five distinct customer segments with differentiated characteristics and specific patterns across 25 product categories. Validation using the silhouette method confirmed K-means with five clusters as the optimal configuration (coefficient = 0.4). The developed model enabled the identification of complex purchasing patterns and facilitated the implementation of personalized business strategies, thereby improving the decision-making process.
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Copyright (c) 2025 Injante-Oré, Richard Enrique, Jaime Chacaliaza-Almeyda
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