Leveraging Business Analytics to Optimize Retail Merchandizing Strategize: A Data Driven Approach

Abstract

The retail industry has increasingly turned to data-driven approaches to optimize merchandising strategies, enhancing profitability and customer satisfaction in a dynamic market environment. This study aims to explore how business analytics can be leveraged to optimize retail merchandising strategies, focusing on inventory management, pricing, and promotional planning. The research was conducted at the College of Business, Lamar University, from June 2023 to December 2023. A mixed-method approach was employed, combining quantitative data analysis of sales, inventory, and customer data with qualitative interviews from industry experts. Data was collected from a sample of 30 retail businesses, with a focus on analyzing transactional data, sales patterns, and customer behavior using predictive modeling, machine learning, and real-time analytics. The results show that business analytics increased sales by 15%, with predictive models improving stock forecasting accuracy by 18%. A 10% reduction in inventory costs was observed due to improved demand forecasting, and inventory turnover improved by 25%. Dynamic pricing models raised profit margins by 12%, while real-time pricing adjustments accounted for a 7% revenue growth. Segmentation and targeted marketing strategies led to a 20% increase in customer retention. Additionally, real-time stock adjustments reduced stockouts by 30%, increasing sales by 5%, while reducing overstocking by 15%. Demand forecasting with machine learning reduced excess inventory by 18%. The study demonstrates that business analytics significantly optimizes retail merchandising strategies, resulting in higher sales, improved efficiency, and greater customer satisfaction.