<![CDATA[Customer segmentation in Cash on Delivery (COD) logistics is inherently challenging due to highly heterogeneous, skewed transaction data and elevated rejection rates, which frequently limit the efficacy of traditional deterministic clustering models. To address this, this study proposes a robust probabilistic segmentation framework utilizing the Gaussian Mixture Model (GMM) to extract actionable insights from complex COD operations. We analyzed 5,147 raw transaction records, meticulously aggregating them into 2,225 unique customer profiles engineered around three critical logistics-centric features: delivery frequency, total COD value, and average COD value. The model’s optimal configuration was rigorously determined using the Bayesian Information Criterion (BIC) to prevent overfitting, and the entire analytical pipeline was seamlessly integrated into an interactive, web-based Decision Support System (DSS). Empirical results demonstrate that a two-cluster solution optimally balances statistical fit and operational interpretability, clearly delineating "High-Value Active Customers" (68%) from "Low-Value Occasional Customers" (32%). Comparative analysis validates GMM’s superiority over baseline algorithms like K-Means, achieving a significantly higher Silhouette Score (0.64 vs. 0.51) and effectively capturing the non-spherical, overlapping data distributions characteristic of COD behaviors. Crucially, the probabilistic soft-clustering capability of GMM uniquely identified a transitional segment of 15.7% "boundary customers," offering logistics managers a nuanced, early-warning lens for preemptive risk mitigation and targeted interventions. Ultimately, this research bridges the critical gap between advanced probabilistic analytics and operational logistics, providing express delivery enterprises with a scalable, data-driven tool for dynamic route optimization, failed-delivery mitigation, and differentiated service personalization.]]>
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