RFM modeling, which stands for recency, frequency, and monetary value, is a method used in marketing to analyze consumer behavior and segment customers based on their transaction history. By evaluating how recently a customer made a purchase, how often they buy, and the total amount they spend, RFM analysis helps marketers identify valuable customer segments for targeted marketing campaigns. This approach is more effective than relying on a single parameter to assess customer value. The blog post provides a tutorial on implementing an RFM model using Datagran, a platform that integrates various data sources such as Microsoft SQL Server, to process and analyze customer data. By running RFM models on historical data, businesses can gain insights into customer behavior, identify causes of churn, and personalize marketing efforts to enhance customer satisfaction and increase revenue. The process involves setting up data pipelines, running SQL queries, and utilizing RFM operators to generate scores that can be used to tailor communications to different customer groups. The tutorial emphasizes the importance of integrating RFM segmentation into marketing strategies to deliver personalized messaging and improve customer lifetime value.