Analisis Segmentasi Pelanggan E-Commerce menggunakan Metode RFM dan K-MEANS Clustering untuk Strategi Bisnis
-
Efrain Yaphet Obert
*
Universitas Bina Sarana Informatika, Jakarta,
Indonesia
- Mulia Rahmayu Universitas Bina Sarana Informatika, Jakarta, Indonesia (*) Corresponding Author
Abstract
The rapid development of the e-commerce industry has generated vast amounts of customer transaction data. However, most companies face challenges in processing this raw data into strategic business insights. Consequently, management struggles to understand variations in transactional behavior, predict buyer preferences, and maintain personalized customer loyalty. This condition risks increasing customer churn and driving promotional cost inefficiencies due to misdirected marketing efforts. This study aims to perform customer segmentation based on transaction data to provide targeted and measurable marketing strategy recommendations. The methodology integrates the RFM (Recency, Frequency, Monetary) model with the K-Means Clustering algorithm. A secondary dataset sourced from Kaggle was processed through Exploratory Data Analysis (EDA), data cleaning, and data type transformation. The optimal number of clusters was determined using the Elbow Method and validated with a quantitative evaluation metric, the Silhouette Score. The test results indicated that a four-cluster solution provided the best structure with a Silhouette Score of 0.43. The four main identified clusters include Lost Customers (39.2%), Low-Value Customers (37.2%), Big Spender Customers (20.4%), and Loyal Customers (3.2%). These segmentation results were subsequently visualized through an interactive Looker Studio dashboard. The implementation of this dashboard provides practical contributions by delivering operational insights for executing segmented promotional strategies, optimizing marketing budget allocation, and systematically mitigating customer churn risks.
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