The role of AI-enhanced fast delivery services in strengthening customer retention and loyalty in competitive markets.

This research presents an AI-enhanced framework to optimize last-mile delivery systems by integrating predictive analytics, Reinforcement Learning (RL), and customer personalization. The predictive analytics component utilized XGBoost and Random Forest models to forecast delivery times. Random Forest achieved better performance, with a Root Mean Square Error of 1.52 and an R-squared value of 0.56. RL-based route optimization improved operational efficiency by reducing the average delivery time from 31.2 to 25.4 min, increasing timely deliveries from 78\% to 92\%, and reducing idle time by 15\%. Customer personalization, driven by sentiment analysis and clustering, increased positive sentiment from 68\% to 80\%. It also improved Net Promoter Scores from 68 to 85 and increased customer retention from 74\% to 89\%. The proposed framework addresses the challenges of last-mile delivery by combining data-driven predictions, adaptive routing, and personalized customer strategies. Future work will explore real-world implementation using real-time traffic data and advanced personalization techniques to improve adaptability and scalability.