<![CDATA[In today’s competitive retail environment, understanding customer behavior is essential for shopping malls to maintain loyalty and increase sales. Many mall managers still face challenges in identifying customer spending patterns because available data is underutilized. Based on field observations and related studies, marketing strategies often miss their targets due to limited analysis of customer characteristics, leading to wasted budgets and low campaign effectiveness. The root of the problem lies in the lack of data analytics implementation to objectively map customer behavior. To address this, the K-Means Clustering algorithm is applied to segment mall customers based on annual income and spending score. The research process involves collecting secondary data from public sources, performing data cleaning and normalization using the Min–Max method, and evaluating cluster quality using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The results divide customers into five distinct groups with varying income and spending patterns. The purpose of this study is to help mall management create more targeted and efficient marketing strategies aligned with each segment’s behavior. The findings show that K-Means Clustering provides valuable insights into customer shopping patterns and can serve as a foundation for improving promotional effectiveness and customer satisfaction through data-driven decision-making.]]>
Copyrights © 2025
