Hybrid Clustering and Classification of At-Risk Customer Segments in Network Marketing

Abstract

Customer segmentation is a fundamental strategy for sustaining retention in network marketing businesses, where repeated transactions and multilayered relationships significantly impact long-term customer value. This study proposes a hybrid machine learning framework to classify at-risk customer segments—comprising regular customers, seasonal buyers, and churn-risk profiles—by integrating unsupervised clustering and supervised classification methods. A total of 36 engineered behavioral features were derived from longitudinal transaction data to capture spending behavior, recency, variability, and growth dynamics. Clustering algorithms including K-Means, Agglomerative Hierarchical Clustering, and Gaussian Mixture Models were applied and evaluated using standard clustering validity indices: Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index. K-Means with six clusters produced the most interpretable and balanced segmentation outcome. Cluster relabeling was conducted to align with business-relevant categories, followed by supervised validation using classifiers such as Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Random Forest and Support Vector Machine (SVM). Among these, SVM yielded the highest predictive accuracy (92.53%) and F1-Score (92.52). The results demonstrate the effectiveness of the proposed hybrid approach in enhancing segmentation precision and facilitating early detection of potential churn in a dynamic marketing environment.