Recency, Frequency, and Monetary are three behavioral attributes and are quite simple, in that they can be easily computed for any database that has purchase history, and are easy to comprehend, yet very powerful in their predictive ability.
Segmentation based on RFM (Recency, Frequency, and Monetary) has been in use for over 50 years especially by direct marketers to target a subset of their customers, save mailing costs, and improve profits. RFM is based on three piilars of customer attributes: Recency of purchase, Frequency of purchase, and Monetary value of purchase. So, in order to conduct RFM, we need to know -
how recently a customer has purchased (recency) how often they purchase (frequency) how much the customer spends (monetary)
Pareto 80/20 Principle is at the core of RFM model which says, 20% customers contribute to 80% of the total revenue. These 20% represent the high-value, important customers the business would want to protect. Therefore, RFM helps to identify customers who are more likely to respond to promotions by segmenting them into various categories.
Here I have used R language for the analysys.