- Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Dergisi
- Vol: 25 Issue: 74
- Customer Segmentation Using K-Means Clustering Algorithm and RFM Model
Customer Segmentation Using K-Means Clustering Algorithm and RFM Model
Authors : Gözde ASLANTAŞ, Mustafacan GENÇGÜL, Merve RUMELLİ, Mustafa ÖZSARAÇ, Gözde BAKIRLI
Pages : 491-503
View : 6 | Download : 4
Publication Date : 2023-05-15
Article Type : Research
Abstract :The key points in customer segmentation are determining target customer groups and satisfying their needs. Recency-Frequency-Monetary (RFM) analysis and K-Means clustering algorithm are the popular methods for customer segmentation when analyzing customer behavior. In our study, we adapt the K-means clustering algorithm to RFM model by extracting features that represent RFM aspects of home appliances. Customers with similar RFM-oriented features are assigned to the same clusters, while customers with non-similar RFM-oriented features are assigned to different clusters. In the experiments, clustering achieved the determined threshold for Silhouette Score. The resulting clusters were ranked and named by Customer Lifetime Value (CLV) metric, which measures how valuable a customer is to the business.Keywords : Müşteri Segmentasyonu, RFM Model, K-Means Algoritması, Müşteri Yaşam Boyu Değeri