B2B Customer Segmentation Based on Customer Lifetime Value Concept and RFM Modeling


  • Fakhruddin Agung Laksono Universitas Hayam Wuruk Perbanas
  • Basuki Rachmat Universitas Hayam Wuruk Perbanas
  • Yudi Sutasrso Universitas Hayam Wuruk Perbanas




RFM, Customer Lifetime Value, K-means Clustering


The company has limited resources to use in implementing marketing strategies for its customers. The first step to be able to develop an effective and efficient marketing strategy is to divide customers into several large groups based on their similarities. The company needs to allocate its limited resources proportionally to groups of customers based on the value and benefits that those customers can contribute to the company. One of the bases for customer grouping is based on the concept of Customer Lifetime Value (CLV) with Recency, Frequency, and Monetary (RFM) modeling. CLV ratings show how much value and benefits customers can bring to a company. This study conducted a cluster analysis with the K-means algorithm on 351 customers based on their RFM value. The number of clusters is most effectively obtained through the elbow method. Cluster analysis produces 4 customer clusters that have different characteristics and are ranked based on their CLV values. Cluster names and marketing strategy recommendations for each cluster are arranged based on their characteristics and CLV rating. The four clusters formed are the Non-Valuable Customers cluster, VIP Customers cluster, Valuable Customers cluster, and Potentially Valuable Customers cluster.


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How to Cite

Laksono, F. A., Rachmat, B., & Sutasrso, Y. (2024). B2B Customer Segmentation Based on Customer Lifetime Value Concept and RFM Modeling. International Journal of Economics Development Research (IJEDR), 5(1), 539–337. https://doi.org/10.37385/ijedr.v5i1.4295