Penerapan Agglomerative Hierarchical Clustering Untuk Segmentasi Pelanggan - Widyawati Widyawati; Wawan Laksito Yuly Saptomo; Yustina Retno Wahyu Utami (STMIK Sinar Nusantara, 2020)
As more businesses emerge, companies need to have the right marketing strategy to provide the best service to customers. The first step is to know the type of customer and make appropriate marketing strategies according to the type of customer. In this research, it is proposed for clustering customers so that an appropriate strategy for that customer group can be determined. The method used for cluster formation uses Agglomerative Hierarchical Clustering with Average Linkage approach and distance determination using Manhattan Distance. The variables in this research are Recency, Frequency, and Monetary (RFM). The results of testing using the Silhouette coefficient show that the results of 7 clusters are the best results when compared with 2 clusters up to 20 clusters because they have the smallest minus value. Based on the results of the Silhoutte coefficient, customer segmentation uses 7 clusters with each cluster representing the existing customer type.