Real-Time Demand Forecasting in Retail Using Multimodal Data (social media + POS + Weather + Macro Indicators

Abstract

Accurate real-time demand forecasting in retail is crucial for optimizing inventory management, reducing wastage, and improving customer satisfaction. Traditional forecasting approaches often rely solely on historical sales data, overlooking dynamic external factors that influence consumer behavior. This study proposes a multimodal demand forecasting framework that integrates Point-of-Sale (POS) transactions, social media signals, weather conditions, and macroeconomic indicators to capture both intrinsic and extrinsic drivers of retail demand. POS data provides fine-grained insights into sales trends, while social media analytics capture emerging consumer sentiments and product popularity. Weather data introduces environmental context, highlighting seasonality and weather-sensitive consumption patterns. Macroeconomic indicators account for broader economic conditions affecting purchasing power. Each data modality undergoes rigorous preprocessing, feature extraction, and temporal alignment before being fused using attention-based multimodal learning, allowing the model to dynamically weigh the importance of each input. A deep learning architecture is employed to leverage both temporal patterns and cross-modal correlations, facilitating accurate real-time predictions. Experimental results demonstrate that the proposed framework outperforms conventional single-source forecasting models in accuracy and responsiveness, particularly during unexpected demand fluctuations. This approach provides retailers with actionable insights for proactive inventory and marketing strategies, paving the way for intelligent, data-driven retail operations in rapidly evolving markets.