User Satisfaction in Moo Opinion App: Machine Learning for Cooperative Segmentation

Abstract

This study addresses the critical need to understand digital application user satisfaction within the agricultural cooperative sector, specifically for the Moo Opinion application at the Village Unit Dairy Cooperative (KUD). The study's primary novelty lies in the implementation of an integrated, sequential Machine Learning framework—combining Random Forest (RF), Principal Component Analysis (PCA), and K-Means Clustering—to provide a granular analysis of user behavior in a specialized dairy ecosystem. The methodology first utilized RF for key feature selection, followed by PCA for dimensionality reduction, and K-Means for precise user segmentation. Primary data was collected from 40 respondents (20 farmers, 20 customers). Key findings reveal that Service Quality (0.42) and Milk Quality (0.36) are the most significant drivers of satisfaction, considerably outweighing economic factors like Milk Price (0.08). PCA identified two core satisfaction dimensions: Quality-Service Synergy (explaining 56.7% variance) and Structural-Economic Factors (explaining 25.7% variance), confirming the dominance of non-economic aspects. K-Means Clustering successfully identified three segments: Highly Satisfied (45%), Moderately Satisfied (38%), and Low Satisfaction (17%), with high cluster validity (Silhouette Coefficient 0.71). A recognized limitation of this study is the small sample size (N=40), which may affect the generalizability of the findings to larger cooperative populations. However, the results offer significant practical implications, highlighting the need for KUD to prioritize digital service quality and product value over pricing strategies to enhance loyalty and prevent churn.

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