E-Commerce Customer Segmentation Application Based on the K-Means Algorithm

Nehemia; Jekoniah Nahum Pakage; Veronica Lois; Regina Arieskha

doi:10.59934/jaiea.v5i2.1922

Authors

Nehemia Universitas Bina Sarana Informatika Pontianak
Jekoniah Nahum Pakage Universitas Bina Sarana Informatika
Veronica Lois Universitas Bina Sarana Informatika
Regina Arieskha Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i2.1922

Keywords:

Customer Segmentation, E-commerce, K-Means Algorithm, RFM Analysis, Streamlit

Abstract

Ineffective e-commerce marketing serves as the background for this research, which aims to develop a customer segmentation application for targeted marketing. The K-Means Clustering method with RFM (Recency, Frequency, Monetary) analysis is applied to data from 178 customers. The research methodology includes data preprocessing, feature transformation, and the determination of the optimal K using the Elbow Method. The results indicate that K=3 is the optimal number of clusters. Three segments were successfully identified: 'Champions' (18.5%, 33 customers) with the highest Frequency/Monetary values, 'Active & Potential' (41%, 73 customers) with the lowest Recency (most recent), and 'At Risk' (40.5%, 72 customers) with the highest Recency (longest duration since last transaction). The study concludes that the developed Streamlit-based application successfully visualizes these segments interactively to support strategic decision-making in marketing.

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