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Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.

The purpose of this research is to analyze and determine possible risks that occur in car rental services, making it easier for companies to take action to prevent risks that can occur at CV Mawar Rental Car. The risks that arise come from internal factors that cause this company to experience losses. This research uses a quantitative research approach. The research location is on Jalan Setia Budi No. 12, Medan, North Sumatra. The data collection techniques used in this research were kuesioner. from the results of research at CV. Mawar Rental Car, the first thing to do is identify risks originating from operational risk, financial risk, external risk.