K-Means Clustering as a Method for Identifying Consumer Behavior Patterns In Taqimart - Urba Yesha Hasibuan; Arridha Zikra Syah; Sudarmin (ITEJ (Information Technology Engineering Journals), 2026)
Taqimart, as a grocery store developing amid competition from modern retail, still faces challenges in analyzing consumer data, where the transaction data generated has not been optimally utilized to understand consumer shopping behavior patterns. This study aims to identify and classify the shopping behavior patterns of Taqimart consumers by applying the K-Means Clustering method. The data used consist of consumer data and transaction data that reflect shopping behavior characteristics, such as purchase frequency and total spending. The K-Means Clustering method is used to group consumers into several clusters based on the similarity of their shopping behavior. The results of this study can provide more structured consumer segmentation information, helping Taqimart develop more targeted marketing strategies, increase the effectiveness of promotions, and support data-driven business decision-making to enhance business competitiveness