Segmentasi Pelanggan Menggunakan K-Means Clustering Berdasarkan Data Kepribadian dan Pola Konsumsi - Iqbal, Iqbal; Hidayat, Nurul; Gevano, Daiva Paundra; Ilahi, Andhika Putra Restu (Jurnal Teknik Informatika (Jutif), 2025)
In today's competitive business landscape, a deep understanding of customer behavior and preferences is crucial for strategic success. Customer segmentation emerges as a vital approach to identify distinct customer subgroups, enabling personalized and efficient marketing strategies. However, many companies still struggle to achieve this understanding due to suboptimal data utilization and inaccurate manual grouping methods. To address these challenges, this research proposes and implements a data mining approach using the K-Means Clustering algorithm for automated and measurable customer segmentation. Leveraging the "Customer Personality Analysis" dataset from Kaggle, this study aims to uncover hidden patterns in customer demographics (age, income, marital status, number of children) and purchasing behavior (number and frequency of transactions). A comprehensive data pre-processing pipeline, including handling missing values, feature engineering, irrelevant column removal, categorical transformation, and numerical scaling, ensures data quality and readiness. Using the Elbow Method, four optimal clusters were identified: "Balanced Spenders with Teenagers" (Cluster 0), "Budget-Conscious Families" (Cluster 1), "High-Value Engaged Buyers" (Cluster 2), and "Active Mature Buyers" (Cluster 3). Visualization using Principal Component Analysis (PCA) further confirms significant characteristic differences between these segments. Cluster 2, being the most valuable and responsive segment, requires premium marketing strategies, while Cluster 1, the largest segment, demands a value-oriented approach. The results of this segmentation provide deep strategic insights, enabling companies to allocate marketing resources more efficiently, craft more relevant messages, and ultimately enhance customer satisfaction and business profitability. These findings demonstrate the potential of unsupervised learning in enhancing data-driven customer profiling systems in marketing and business informatics.