RFM

RFM, short for recency, frequency, and monetary value, is a customer value segmentation method used in marketing and customer relationship management. It classifies customers according to three dimensions. Recency measures how recently a customer made their last purchase; Frequency tracks how many transactions they have in a defined period; Monetary reflects the total spend within that period or over the customer relationship. Together, they help identify customers with greater likelihood of responding to campaigns or of continued engagement.

Data typically drawn from transactional systems. For each customer, R, F, and M are calculated within a chosen time window, then scores are assigned, often on a 1–5 or 1–10 scale. A higher score generally denotes more valuable behavior. The three scores can be combined into an RFM code or used to create segments. Common segments include Champions (high R, F, and M), Loyal customers (high F and M, moderate R), High-Value new customers, At risk (recently inactive but previously valuable), and About to sleep or Lost (low R).

Applications include targeted email and loyalty campaigns, reactivation efforts, cross-selling and upselling, and general prioritization of customer outreach. RFM markets have advantages: simplicity, interpretability, and low data requirements, making it suitable for many organizations with transactional data. However, limitations include its reliance on past behavior without context (product categories, margins, or multi-channel interactions), potential misranking in seasonal businesses, and the need for regular data maintenance. Variants and enhancements—such as weighted RFM, time-decayed scoring, or integration with CLV models—address some shortcomings and improve predictive power.

RFM remains a foundational technique for customer segmentation and campaign planning, often serving as a baseline before adopting more complex predictive models.