Intelligent Forecasting In Retail: Optimizing Customer Flow Management With Lstm Neural Networks

Customer flow plays a crucial role in the operational efficiency and customer satisfaction of retail environments, particularly in the management of queuing systems. Efficient queue management enhances customer experience by reducing waiting times and optimizing service delivery, whereas poor management can result in dissatisfaction, revenue loss, and operational inefficiencies. In this study, we propose an advanced customer flow prediction model using Long Short-Term Memory (LSTM) neural networks. Our approach leverages historical customer flow data and integrates external influencing factors, such as weather conditions and promotional events, to enhance predictive accuracy. The developed LSTM-based model provides real-time forecasts of customer waiting times, allowing businesses to proactively allocate resources and improve overall queue management. Computational experiments demonstrate that our model achieves high predictive accuracy, outperforming traditional time-series forecasting methods such as ARIMA. The results show a reduction in customer waiting times by approximately 20% and an 18% improvement in resource utilization, highlighting the practical benefits of implementing AI-driven predictive analytics in retail operations. Furthermore, residual analysis confirms the robustness of the model, ensuring reliability in dynamic retail scenarios. This study underscores the potential of deep learning techniques in transforming customer service strategies and optimizing operational planning. Future research will explore the integration of optimization techniques to further refine decision-making processes in queue management and resource allocation.