Presentation of the IBDA algorithm for improving decision-making in electronic businesses

Abstract This study investigates the impact of big data analytics on decision-making for an Iranian online store, utilizing a dataset of 500,000 sales transactions, page views, and customer reviews. By employing advanced machine learning algorithms like linear regression and text classification, the research reveals that big data analytics enhances demand prediction accuracy and uncovers complex customer behavioral patterns. This enables businesses to optimize processes and strategies, leading to reduced operational costs and higher customer satisfaction. The study introduces the Integrated Big Data Analysis (IBDA) algorithm, which combines various analytical methods. The IBDA shows improved precision in sales forecasting (with a Mean Squared Error of 1.90) and sentiment analysis (achieving 87% accuracy), outperforming traditional techniques. The findings emphasize that e-businesses can achieve a sustainable competitive advantage through effective utilization of big data analytics. By enhancing operational efficiency and fostering deeper customer engagement, organizations can drive growth and success in the competitive e-commerce landscape. Ultimately, the IBDA algorithm equips e-businesses with the ability to navigate their data efficiently, leading to informed decision-making and improved performance in the market.