Article
Hybrid Structured–Unstructured Delivery Feedback Analysis for ECommerce LMD Enhancement
The rapid growth of e-commerce and online delivery platforms has increased the importance of efficient last-mile courier services. Companies such as Amazon and Flipkart depend on courier performance to ensure timely delivery and maintain customer satisfaction. The surge in online orders generates large volumes of structured delivery data and unstructured customer feedback, containing valuable insights into courier efficiency, delivery accuracy, service reliability, and overall experience. Traditional evaluation methods, including manual review and basic statistical summaries like average ratings, fail to effectively analyze large datasets or interpret complex textual feedback. To address this challenge, this study proposes a machine learning (ML)-based analytical framework for sentimentdriven performance benchmarking of last-mile couriers. The framework integrates data preprocessing, text feature extraction, and multiple classification models to analyze courier-related data. A graphical user interface (GUI) allows users and administrators to interact with the system, while Redis serves as a lightweight in-memory database for secure authentication and credential management. The study implements several ML algorithms, including Gaussian Naive Bayes (GNB), Random Forest (RF), and K-Nearest Neighbors (KNN), as baseline classifiers. Additionally, a hybrid model, Leaf2VecKNN, is proposed to enhance classification performance. In this approach, RF converts courier features into leaf-node representations, which are encoded and classified using KNN similarity learning. Experimental results show that the proposed Leaf2VecKNN model achieves the highest accuracy of 91.21%, outperforming baseline models. The framework provides an efficient solution for automated courier feedback classification and supports data-driven decision-making to improve delivery service quality
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