Innovative Order Delivery Success Prediction in Online Retail: Integrating ML and LLM to Gain Actionable and Understandable Insights

This study introduces an innovative approach to retail analytics by developing a Machine Learning (ML) model that predicts next order delivery success status and combines these predictions with Large Language Models (LLM) for enhanced explainability. Addressing a critical challenge in online retail, this research pioneers a hybrid analytics model. This model not only analyzes retail order data and environmental variables to forecast next-time order delivery success but also enhances interpretability through LLM-generated annotations, which are informed by the parameters from the ML prediction model. The resulting detailed, insightful explanations rendered by LLM transform complex data predictions into comprehensible insights, offering valuable and intelligible information to business owners and stakeholders. This dual approach ensures accuracy in forecasting and clarity in communication, facilitating informed and expedited decisionmaking. The article delineates how this novel hybrid method can lead to more actionable and understandable analytics in retail, thus democratizing data-driven decision-making across various business operational levels.