Data Mining untuk Klasterisasi dan Klasifikasi Pelanggan Berdasarkan Pendapatan dan Transaksi Pembelanjaan Menggunakan Algoritma K-Means - AWALIYANI, IKNA; Yudha Pratama, Rendy (Universitas Aisyah Pringsewu, 2025)
In the era of technology and business, handling customers is very important as consumers and a significant source of marketing information. Customer evaluations have a major impact on marketing and service quality can be a benefit or a loss to a seller's reputation. Knowing key customer profiles supports effective marketing strategies with a deep understanding of customer preferences and behavior. This approach is supported by clustering technology for better analysis of customer information, improving management, and designing IT technology according to current developments. The results of data processing form four clusters, forming customer profiles based on family structure and income/expense levels. Understanding cluster characteristics is the basis for an effective marketing strategy. By customizing marketing tactics, targeting specific offers, and optimizing promotions, companies can build strong relationships, increase retention, and achieve business success. This approach provides deep insight, enables targeted decision making, and is responsive to evolving market dynamics.