Abstract. Customer segmentation is the marketing practice of grouping customers according to certain characteristics. This paper presents a thorough exploration of customer segmentation using machine learning techniques, Logistic Regression, and Support Vector Machine (SVM), applied to data obtained from a mall customers database. By labeling the customer groups and analyzing their characteristics to gain deeper insights into their shopping behavior and preferences, the goal is to develop targeted marketing strategies and allocate resources efficiently to meet the specific needs of each customer segment. Applying statistical analyses and data visualization techniques, the study seeks to derive valuable insights from the data and identify discernible patterns and trends. Utilizing logistic regression yields a remarkable model accuracy of 98%. Subsequently, we employ another machine learning technique for data classification, namely the Support Vector Machine, which achieves an equally notable accuracy of 96%. Using these classification models, potential customers can be effectively converted into loyal ones and enhance the satisfaction of existing customers through tailored marketing strategies for each segment. The research offers insights into effective strategies for distinct customer groups. Applying these methods in a business setting can yield valuable information, forming a basis for informed decision-making and improving customer relationships through customer relationship management strategies. application/pdf 305092 bytes 2024 Information Society of Serbia - ISOS info:eu-repo/semantics/conferenceProceedings Proceedings of the 14th International Conference on Information Society and Technology (ICIST 2024) startpage: 1 endpage: 10 Keywords: Customer segmentation, cluster analysis, classification Radojičić, Dragana Milunović, Bojana Some possibilities for the utilization of machine learning methods for customer segmentation based on consumer habits eng https://phaidrabg.bg.ac.rs/o:35641 ISBN: 978-86-85525-32-2 All rights reserved