<![CDATA[Business competition compels companies to understand customer characteristics in order to maintain and enhance their competitiveness, especially in the pharmaceutical industry, which involves various customer segments such as hospitals, pharmacies, patients, and end consumers with diverse needs. Customer segmentation becomes crucial in developing effective strategies, with K-Means algorithm being one of the commonly used methods due to its simplicity and efficiency in clustering large datasets. This study combines the K-Means Clustering algorithm with the elbow method to determine the optimal number of clusters in segmenting the customer profiles of a pharmaceutical company. The analysis results reveal two main clusters: the first cluster is dominated by hospitals with higher medication purchase volumes and longer delivery distances, ranging from 8 to 131 km, while the second cluster is dominated by pharmacies with smaller purchase volumes and shorter delivery distances. These findings enable the pharmaceutical company to better understand customer characteristics and design more effective strategies to compete in the market. It is recommended that the company adjusts its marketing strategies and products based on the needs of each cluster, enhances customer relationships through loyalty programs, and optimizes distribution routes to improve operational efficiency.]]>
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