IoT-Based Customer Behavior Patterns in Retail Stores Using Recurrent Neural Networks

The use of Internet of Things (IoT) in retail has transformed the investigation and understanding of customer behavior. This research examines the use of Recurrent Neural Networks (RNNs) to predict and model customer behavior patterns in stores using IoT-generated data. Retailers may get extensive insights into customers’ movements, preferences, and purchase choices by using real-time data from smart shelves, beacons, and other connected devices. RNNs, due to their capacity to analyze sequential data, are used to capture the temporal dynamics of customer interactions, allowing more precise predictions of shopping behaviors. To discern notable behavioral patterns, guide personalized marketing methods, and improve inventory management systems. The results underscore the capability of integrating IoT data with RNNs to enhance customer experience and improve operational efficiency in the retail industry. This research enhances the comprehension of how advanced machine learning methodologies may use IoT data to revolutionize consumer behavior analysis in a quickly changing retail environment.