Enhancing customer segmentation through factor analysis of mixed data (FAMD)-based approach using K-means and hierarchical clustering algorithms

In today’s data-driven business landscape, effective customer segmentation is crucial for enhancing engagement, loyalty, and profitability. Traditional clustering methods often struggle with datasets containing both numerical and categorical variables, leading to suboptimal segmentation. This study addresses this limitation by introducing a
novel application of Factor Analysis of Mixed Data (FAMD) for dimensionality reduction, integrated with K-means and Agglomerative Clustering for robust customer segmentation. While FAMD is not new in data analytics, its potential in customer segmentation has been underexplored. This research bridges that gap by demonstrating how FAMD can
harmonize mixed data types, preserving structural relationships that conventional methods overlook. The proposed methodology was tested on a Kaggle-sourced retail dataset comprising 3900 customers, with pre-processing steps including correlation ratio filtering (η ≥ 0.03), standardization, and encoding. FAMD reduced the feature space to three principal components, capturing 81.46% of the variance, which facilitated clearer segmentation.

Comparative clustering analysis showed that Agglomerative Clustering (Silhouette Score: 0.52) outperformed K-means (0.51) at k = 4, revealing distinct customer segments
such as seasonal shoppers and high spenders. Practical implications include the development of targeted marketing strategies, validated through heatmap visualizations and
cluster profiling. This study not only underscores the suitability of FAMD for customer segmentation but also sets the stage for more nuanced marketing analytics driven by
mixed-data methodologies.