CUSTOMER SEGMENTATION AND BUYER TARGETING USING K-MEANS CLUSTERING: A CASE STUDY OF THE REAL ESTATE INDUSTRY
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Abstract
This study aims to analyze customer segmentation and targeting buyers using the K-Means Clustering method in the real estate industry, with the case study of PT Sembilan Bintang Lestari in Jember. The company faces challenges in understanding the buyer's specific characteristics, so that it requires a data -based approach to increase marketing effectiveness. The analysis process is carried out based on a crisp-DM framework which includes stages of business understanding, data understanding, data preparation, modeling, and evaluation. Customer data used includes age variables, income, jobs, house types, payment methods, and marital status. The modeling stage uses the K-Means algorithm with the help of the Elbow method to determine the optimal number of clusters, as many as three clusters. The segmentation results are then evaluated using the Silhouette Score and visualized through the scatter diagram, boxplot, and pie chart to provide a comprehensive picture of the characteristics of each cluster. This result is used as a basis for designing a more specific and targeted buyer targeting strategy. This study contributes to the application of data mining -based segmentation methods to support marketing decision making more effectively in the real estate sector. The novelty of this study lies in the integration of the K-Means, Elbow, and Silhouette Score methods in real estate customer data for a decade, as well as the presentation of applicative segmentation visualization to support buyers targeting at the company's operational level.
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