ANALYSIS OF CUSTOMER DATA IN SELECTING POTENTIAL CUSTOMERS USING DATA MINING WITH THE K-MEANS ALGORITHM

Randi Rian Putra, Sri Handayani, Fahmi Kurniawan, Cendra Wadisman

Abstract


Abstract:  In an increasingly competitive business era, understanding customer characteristics and preferences has become essential. This research aims to analyze customer data to find potential customers using data mining techniques and the K-Means algorithm. The data used comes from the company's database which includes transaction history, demographics and customer interactions. The K-Means algorithm is applied to cluster customer data into several groups based on similar characteristics. The clustering results show that there are customer segments with different profit potential for the company. By identifying potential customer segments, companies can design more targeted marketing strategies, increase the efficiency of allocating resources, and ultimately increase ROI (Return on Investment). This study provides guidance for companies in optimizing their approach to reaching potential customers and achieving marketing success.

Keywords: Data Mining, K-Means Algorithm, Customers, Data Analysis, Potential Customers, Marketing Strategy


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References


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