Big Data Analytics in E-commerce Driving Business Decisions Through Customer Behavior Insights

Abstract

This proposition seeks to find out the significant opportunities BIG Data Analysis can offer to e-commerce and generate analytic decisions about customer behavior. With evolving e-commerce, consumer actions have been the most important factor for businesses keeping competitive. This comprehensive framework that combines machine learning, predictive analytics, and customer segmentation provides actionable insights by addressing the challenges posed in areas such as seasonality, economic trends, privacy concerns, and scalability. Moreover, it combines privacy-preserving techniques and ethical data governance that enables businesses to do so while building trust with their customers. With the merging of data science and user experience design, this study focuses on real-time decision-making, personalized recommendations, and enriched customer engagement. A scalable solution for e-commerce businesses, big or small, looking to increase performance, forecast demand, boost customer loyalty epidemic and partner experience in one app; the aim of the findings. Our research bridges the gap between academia and practice, and it acts as a guide for e-commerce companies to utilize Big Data to gain a strategic edge in the competitive market.

Keywords

• big data analytics
• e-commerce
• customer behaviour
• machine learning
• predictive analytics
• customer segmentation
• privacy preservation