Penerapan Algoritma Clustering untuk Segmentasi Pelanggan Berdasarkan Perilaku Pembelian

Abstract

This study explores the application of the K-Means clustering algorithm for customer segmentation based on purchasing behavior. Using a dataset from Kaggle containing consumer shopping trends, the research focuses on seven key attributes: Age, Gender, Purchase Amount, Category, Purchase Frequency, Previous Purchases, and Preferred Payment Method. The K-Means algorithm successfully identified six distinct customer clusters, each with unique characteristics reflecting specific purchasing patterns. The analysis provides valuable insights into product preferences, buying habits, and demographic characteristics of customers. These findings have significant implications for marketing strategies and business decision-making, enabling companies to develop more targeted and personalized approaches to customer engagement and retention. The study demonstrates the effectiveness of the K-Means algorithm in customer segmentation and highlights its potential for optimizing marketing efforts and improving customer satisfaction in various business contexts.

Keywords: K-Means algorithm, customer segmentation, purchasing behavior, clustering, data mining, marketing strategy