Data-Driven Insights: Mall Customer Segmentation through Fuzzy C-Means Clustering

Abstract

This study, employed the Fuzzy C-Means clustering algorithm to segment mall customers based on various demographic and spending attributes. Using a dataset of 200 mall customers, the study analyzed their annual income, spending score, and other relevant factors to identify distinct customer groups. The Fuzzy C-Means algorithm, which allows data points to belong to multiple clusters with varying degrees of membership, provided a nuanced segmentation that traditional clustering methods might miss. To enhance the interpretability of the results, the authors utilized Plotly, an interactive visualization tool, to create dynamic and insightful visual representations of the segmented data. The analysis revealed five distinct customer segments, each with unique characteristics and behaviors. The interactive plots facilitated a deeper understanding of the clustering results, enabling the identification of key patterns and trends within each segment. This segmentation can inform targeted marketing strategies and personalized customer experiences, potentially driving increased customer satisfaction and loyalty. The combination of Fuzzy C-Means clustering and Plotly visualization proved effective in uncovering complex relationships within the data, demonstrating the potential of these techniques in retail analytics and customer segmentation.

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