Pengaruh Analisis Customer Lifetime Value Menggunakan RFM Dan K-Means Clustering Terhadap Strategi Pemasaran Industri E-Commerce

Juhairiah Juhairiah (Universitas Sangga Buana YPKP)
Taufik Zulfikar (Universitas Sangga Buana YPKP)

JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan)
Vol 10 No 2 (2026): JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan)

The rapid growth of the e-commerce industry has intensified competition in customer retention and business profitability. Therefore, companies need to understand customer characteristics to develop targeted marketing strategies. This study aims to analyze Customer Lifetime Value (CLV) using the Recency, Frequency, Monetary (RFM) approach and K-Means Clustering to support marketing decision-making. The research methodology follows the CRISP-DM framework utilizing the Online Retail Dataset consisting of 4,338 customers. The K-Means algorithm successfully classified customers into five main segments: Potential Customers, Hibernating Customers, Champions, Loyal Customers, and VIP Customers. The VIP Customers segment achieved the highest average CLV of 572,425.61, while Hibernating Customers had the lowest average CLV of 1,435.94. Furthermore, Potential Customers contributed the largest share of revenue due to their dominant population size. The integration of RFM, K-Means, and CLV provides deeper insights into customer behavior to support more effective marketing strategies.