Advanced Customer Segmentation Using Clustering Techniques in Big Data

Abstract

This study explores advanced customer segmentation using clustering techniques in big data to enhance marketing strategies. Traditional segmentation methods, which primarily rely on demographic and basic behavioral data, often fail to capture complex consumer behaviors. By leveraging K-Means clustering, this research identified four distinct customer segments from a large, multi-dimensional dataset. The process involved data preprocessing, clustering, and evaluation using metrics such as the silhouette score, which achieved a high value of 0.748, indicating well-defined clusters. The results were visualized through scatter plots and summarized in a tabular format, highlighting key characteristics of each segment. The findings demonstrate that clustering-based segmentation offers actionable insights for personalized marketing, customer engagement, and retention strategies. This study concludes that adopting advanced segmentation frameworks in big data environments enables businesses to better understand their customers, resulting in improved targeting and competitive advantage. Future research could incorporate additional features and alternative algorithms.