Segmentasi Pelanggan WiFi RT/RW Net Menggunakan Metode K-Means Clustering untuk Identifikasi Potensi Churn sebagai Strategi Retensi Pelanggan
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1248Keywords:
Customer Segmentation; K-Means Clustering; Customer Churn; Knowledge Discovery in Databases (KDD); Silhouette CoefficientAbstract
Advances in information technology have made the internet an essential need for society in supporting various daily activities. Increasing competition among internet service providers has made customer retention one of the key challenges in ensuring business sustainability, particularly in addressing the phenomenon of churn that is, when customers stop using a service and switch to another provider. This study aims to segment customers to identify potential churn using the K-Means Clustering method on 984 active customers of CV. Bagus Agung Mandiri Sejahtera. The variables used include subscription duration, payment discipline frequency, satisfaction with technician and customer service, and intention to switch services. Data processing was conducted through the Knowledge Discovery in Databases (KDD) methodology. The results showed the formation of three clusters: Cluster 1 comprising 776 customers (78.86%), Cluster 2 comprising 161 customers (16.36%), and Cluster 3 comprising 47 customers (4.78%). Based on the profiling results, Cluster 2 is identified as the group with the highest potential for churn, characterized by a subscription duration value of 0.1801, a payment discipline frequency of 0.1843, technician satisfaction of 0.207, customer service satisfaction of 0.1946, and an intention to switch providers of 0. These characteristics indicate that customers in Cluster 2 tend to have shorter subscription periods, low payment discipline, low service satisfaction, and a high tendency to switch services. An evaluation using the Silhouette Coefficient yielded a value of 0.5773, indicating that the cluster structure provides good separation based on customer data characteristics. The results of this study can serve as a basis for developing more targeted retention strategies, particularly through improving the quality of technician and customer service, sending periodic payment reminders, and offering personalized approaches and loyalty programs to customers in Cluster 2.
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