Real-Time K-Means Clustering with Firebase ML Kit: Segmenting Barber Shop Customers by Booking Behavior and Churn Risk - Rafie Rafie; Helda Yunita; Amrul Hadiyanoor (Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM), 2025)
This study addresses the challenges of high churn rates and the need for real-time customer behavior analysis in barber shops by developing a K-Means model integrated with Firebase ML Kit. The research analyzes 70,000 booking records from an Android application, focusing on features such as booking frequency, average cancellations, and recency day. The model achieves optimal performance with 5 clusters, validated by a Silhouette Score of 0.58 and a Davies-Bouldin Index of 0.541. Key segments like "Inactive Members" and "High Volume Churners" are successfully identified, enabling targeted business strategies such as reactivation campaigns and priority booking offers. The system is implemented in a mobile application, providing real-time customer segmentation and actionable insights. This approach offers a scalable solution to enhance customer retention and operational efficiency in the barber shop industry