RFM analysis and Customer Lifetime Value (CLV) are two important analytical approaches used by retailers to understand customers, evaluate their importance, and develop effective marketing strategies. Both models use customer data, but they differ in their purpose, measurement methods, time perspective, and applications. RFM analysis mainly evaluates customers on the basis of their past purchasing behaviour, whereas CLV estimates the total economic value that a customer is expected to generate for the retailer throughout the entire relationship. RFM stands for Recency, Frequency, and Monetary value. Recency refers to how recently a customer made a purchase. A customer who purchased something very recently is generally considered more engaged than a customer who has not purchased for a long period. Frequency measures how often a customer purchases from the retailer during a particular period. A customer who purchases frequently is usually more valuable and loyal than someone who purchases only occasionally. Monetary value measures how much money a customer has spent. Customers with higher total or average spending are generally given higher monetary scores. In RFM analysis, retailers assign scores to customers for each of the three dimensions. For example, customers may be ranked from 1 to 5 for recency, frequency, and monetary value. A customer with a score of 5 in all three categories would be considered a highly active and valuable customer. Customers can then be divided into groups such as best customers, loyal customers, recent customers, promising customers, customers needing attention, and inactive or lost customers. RFM analysis is therefore a practical segmentation technique based primarily on historical transaction data. Customer Lifetime Value, on the other hand, is an estimate of the total profit or revenue that a customer is expected to generate for a retailer over the entire period of the customer relationship. CLV takes a longer-term perspective. Instead of asking only what the customer has purchased recently, CLV asks how much economic value the customer may generate in the future. It can consider factors such as average purchase value, purchase frequency, customer retention probability, expected relationship duration, profit margin, and future purchasing behaviour. The first major difference between RFM and CLV is their basic purpose. RFM is mainly used for customer segmentation and identifying differences in recent and historical purchasing behaviour. CLV is primarily used for evaluating the long-term economic worth of customers. RFM helps retailers answer the question, “Which customers are currently active and valuable based on their past purchases?” CLV helps answer, “Which customers are likely to generate the greatest value over the entire relationship?” The second difference concerns the time perspective. RFM generally focuses on a defined historical period, such as the previous six months or one year. Its measures are strongly connected with what customers have already done. CLV has a future-oriented dimension because it attempts to predict future purchases, retention, revenue, and profitability. Thus, RFM is more descriptive, while CLV is more predictive. The third difference lies in the type