CUSTOMER SEGMENTATION BASED ON AGE, GENDER, PRODUCT AND TOTAL CUSTOMER BALANCE AT BANK XYZ USING THE K-MEANS CLUSTERING MODEL

Abstract

Competition in the banking industry is getting tighter, along with the rapid growth in the number of customers. Therefore, it is important for banks to understand the characteristics of their customers better in order to develop effective marketing strategies. Customer segmentation (STP) is one method that can be used to understand customer characteristics. This research aims to build a customer segmentation model (STP) using the K-Means Clustering algorithm on Bank XYZ customer profile data, with a focus on available products. This research uses Bank XYZ customer profile data which includes variables such as age, gender, outstanding balance and product type. Data is analyzed using the K-Means Clustering algorithm to group customers into different segments. The results of this segmentation can be a reference for Bank XYZ to develop a more targeted and effective marketing strategy. Banks can focus on offering products and services that suit the needs and characteristics of each customer segment.