BIG DATA ANALYTICS FOR RETAIL: PERSONALIZING THE CUSTOMER EXPERIENCE THROUGH DATADRIVEN INSIGHTS

Abstract

The retail industry is undergoing a transformation driven by big data analytics, enabling businesses to personalize the customer experience in ways that were previously unimaginable. This research explores how big data analytics can be leveraged to provide personalized recommendations and improve customer satisfaction in the retail sector. The problem this study addresses is the challenge of effectively analyzing vast amounts of consumer data to derive meaningful insights. Previous research has highlighted the potential of big data analytics, but the specific role of advanced analytics in personalizing the retail experience remains underexplored. This study employs machine learning algorithms, including collaborative filtering and deep learning models, to analyze consumer purchase behavior and demographic data. The results indicate that personalized recommendations based on data-driven insights significantly improve customer engagement and sales conversion. This study contributes to existing literature by demonstrating the practical application of big data in retail, emphasizing the value of customer-centric strategies. The findings imply that retail businesses should invest in big data technologies to enhance their personalization efforts and remain competitive in an increasingly data-driven marketplace.

Keywords: Big Data, Retail, Personalization, Customer Experience, Machine Learning, Data-Driven Insights, Consumer Behavior