This study utilizes advanced analytics techniques in conjunction with the R programming language to offer an efficient approach to client segmentation. Customer segmentation is a crucial approach for firms aiming to enhance marketing efficiency, allocate resources effectively, and ultimately improve customer satisfaction and profitability. The study employs a multi-phase methodology starting with data preprocessing and collecting, followed by exploratory data analysis and the use of unsupervised learning methods such as k-means clustering. The results demonstrate the effectiveness of the proposed method in identifying distinct customer types based on demographic and behavioral traits. The information acquired through this segmentation strategy offers valuable guidance for strategic decision-making and tailored marketing efforts. This work contributes to the increasing amount of research on consumer segmentation methods and highlights the need of utilizing advanced analytics tools such as R to derive valuable insights from customer data.