BIG DATA DESCRIPTIVE STATISTICS AND UNSUPERVISED CLUSTERING INTO CUSTOMER SEGMENTATION

Abstract

The rapid growth of e-commerce has transformed consumer purchasing behavior, generating vast amounts of transactional and behavioral data. Leveraging big data analytics offers an opportunity to uncover patterns of behavior, enabling firms to identify distinct customer segments and tailor marketing strategies effectively. This study explores the use of descriptive statistics and unsupervised clustering to identify distinct consumer behavior patterns in e-commerce by collecting data of 2500 transactions in order to find hidden behavioral patterns and significant consumer segments using a data-driven approach. Using behavioral and transactional data, descriptive summaries, principal component analysis (PCA), and K-means clustering were applied to extract meaningful customer segments. Results reveal three main consumer groups differing significantly in purchase frequency, spending, and loyalty behavior. The findings contribute to marketing analytics by demonstrating the role of descriptive big data analysis in practical segmentation and provide actionable insights for personalized marketing strategies.