Real-Time Price Elasticity Analysis in Retail Using IoT and Machine Learning

This research introduces a new approach to retail pricing elasticity analysis that uses the complementary strengths of the Internet of Things (IoT) and machine learning. IoT sensors, smart shelves, and cameras allow for the continuous collection of real-time data on key retail factors, including customer behavior, inventory levels, and foot traffic. The next step is seamlessly combining this data with price and sales history. Applying the robust ensemble learning approach known as Gradient Boosting Machines (GBM) to examine the integrated dataset forms the core of the proposed system. With GBM, complex relationships between price changes and customer reactions may be more easily identified, leading to more accurate price elasticity estimates. By automatically altering pricing in response to real-time market dynamics, competition activities, and consumer preferences, the system enables merchants to make data-driven choices. This flexible price optimization technique enhances revenue generation, consumer happiness, and loyalty. The system also uses a feedback loop to improve real-time pricing techniques by considering new information from current data streams. The proposed methodology has been shown effective via case studies and simulations by optimizing pricing strategies and maximizing profitability in varied retail situations.