Enhancing Online Retail Insights: K-Means Clustering and PCA for Customer Segmentation

Abstract

Businesses seeking to enhance online retail operations and gain a deeper understanding of consumer behavior must effectively segment their clientele. This study introduces a comprehensive approach that integrates K-Means clustering for customer segmentation with Principal Component Analysis (PCA) for dimensionality reduction. Using this approach on a large online retail dataset using Recency, Frequency, and Monetary (RFM) metrics greatly reduces data complexity while preserving important variances. This approach helps to identify individual client categories, so improving focused marketing plans and streamlining retail industry decision-making. This method facilitates the identification of different client segments, hence enhancing focused marketing strategies and improving decision-making in the retail sector. The framework serves as an excellent instrument for data-driven customer relationship management, exhibiting both scalability and interpretability. This study addresses critical limitations, including dependence on transactional data and a unique dataset, as well as future directions that involve the integration of supplementary data sources, investigation of advanced clustering methodologies, and enablement of real-time implementation. The results highlight the effectiveness of combining PCA and K-Means to improve customer segmentation in dynamic retail settings.

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