AI-Driven Customers Segmentation Using K-Means Clustering

Effective customer segmentation is essential for businesses to enhance marketing strategies and improve customer engagement. This study applies K-Means clustering to segment customers based on demographic and behavioral data collected through structured surveys and questionnaires. The dataset includes attributes such as age, income, purchase frequency, product preferences, and brand loyalty. Data preprocessing involved normalization, imputation, and encoding to ensure quality and suitability for clustering. The optimal number of clusters was determined using the Elbow Method and Davies-Bouldin Index, resulting in three distinct segments: High-Spending, Frequent Shoppers, Price-Sensitive, Infrequent Shoppers, and Brand-Loyal, Moderate Shoppers. These clusters provide actionable insights for businesses, enabling tailored strategies such as loyalty programs, discounts, and targeted promotions. The Davies-Bouldin Index score of 1.27 and visualization using Principal Component Analysis (PCA) validated the effectiveness and distinctiveness of the segmentation. This research highlights the potential of AI-driven methods like K-Means clustering in uncovering hidden patterns in customer behavior, offering a robust alternative to traditional segmentation approaches. Future studies could explore larger datasets, integrate additional behavioral attributes, and compare the performance of other clustering algorithms to further enhance segmentation outcomes.