Big Data Analytics and Its Impact on Customer Behavior Prediction in Retail Businesses

Abstract

The integration of Big Data Analytics (BDA) in retail has revolutionized customer behavior prediction, enabling businesses to personalize offerings and enhance customer satisfaction. This study aims to assess the impact of Big Data Analytics on customer behavior prediction in retail businesses, exploring the relationship between data-driven insights and retail strategies. A mixed-methods approach was employed, combining quantitative data analysis from retail transaction data and qualitative surveys. Data was collected from 124 consumers across multiple retail sectors. Various machine learning algorithms, such as regression analysis and decision trees, were applied to analyze customer preferences, purchasing patterns, and demand forecasting. Standard deviation and p-value calculations were used to assess the statistical significance of findings. The study revealed that Big Data Analytics significantly improved customer behavior prediction, with 85% accuracy in demand forecasting and 90% in customer segmentation. The predictive models showed a standard deviation of 2.5%, suggesting a high level of reliability. The p-value was calculated at 0.003, indicating that the results were statistically significant at the 95% confidence level. Additionally, the use of personalized recommendations led to a 30% increase in customer engagement and a 22% increase in sales conversion rates. Predictive analytics enabled retailers to anticipate customer preferences, optimizing marketing strategies, inventory management, and pricing. Big Data Analytics is a game-changer for customer behavior prediction in retail. The results emphasize its effectiveness in improving customer engagement and business profitability, with significant statistical support.