Empirical evidence suggests that couriers often deviate from pre-planned delivery routes, responding to practical realities that routing algorithms may overlook, such as dynamic traffic conditions or recipient availability. This study explores the reasons behind delivery drivers’ deviations from assigned routes, particularly how their accumulated experiences inform their navigation choices. Using data from the 2021 Amazon Last-mile Research Challenge, which labels courier-performed routes based on practical considerations like productivity and customer satisfaction, we demonstrate that the quality of a delivery route is influenced by more than just travel time. Factors like turn sharpness, backtracking distance, and neighbourhood visit timing also have a statistically significant impact on route quality. This study trains an energy-based model to predict the likelihood of a route being of high quality when executed in the field. We integrate the model’s insights into an insertion heuristic to generate high-quality last-mile delivery routes.