Customer segment model with the purchase recency, frequency and monetary amount

This paper utilizes customer transaction data to segment customers based on their purchase recency, frequency, and monetary amount. By employing an empirical approach, a stochastic model is proposed to predict customer segmentation into categories such as new, active, potential, and lost. The model also constructs indices for customer equity and loyalty. This approach allows companies to practically analyze and categorize their customers, calculating the probability of segment characteristics. The study highlights the importance of customer segmentation in strategic planning, emphasizing the role of RFM (recency, frequency, monetary amount) analysis in identifying high-value customers and optimizing marketing strategies. The proposed model integrates customer equity and loyalty metrics, providing a comprehensive framework for businesses to enhance customer relationship management and targeted marketing efforts. The empirical data from a credit card customer database in Taiwan demonstrates the model's effectiveness in segmenting customers and predicting their behavior, offering valuable insights for businesses to allocate resources strategically and maximize revenue potential. This research contributes to the field by presenting a robust method for dynamic customer segmentation, applicable across various industries.