What Is Recency, Frequency, Monetary Value (RFM) in Marketing?

What Is Recency, Frequency, Monetary Value (RFM)?

Recency, frequency, monetary value (RFM) is a model used in marketing analysis that segments a company’s consumer base by their purchasing patterns or habits. In particular, it evaluates customers’ recency (how long ago they made a purchase), frequency (how often they make purchases), and monetary value (how much money they spend).

RFM is then used to identify a company’s or an organization’s best customers by measuring and analyzing spending habits to improve low-scoring customers and maintain high-scoring ones.

Understanding Recency, Frequency, Monetary Value

The RFM model is based on three quantitative factors:

1. Recency: How recently a customer has made a purchase
2. Frequency: How often a customer makes a purchase
3. Monetary value: How much money a customer spends on purchases

RFM analysis numerically ranks a customer in each of these three categories, generally on a scale of 1 to 5 (the higher the number, the better the result). The “best” customer would receive a top score in every category.

These three RFM factors can be used to reasonably predict how likely (or unlikely) it is that a customer will do business again with a firm or, in the case of a charitable organization, make another donation.

The concept of recency, frequency, monetary value (RFM) is thought to date from an article by Jan Roelf Bult and Tom Wansbeek, titled “Optimal Selection for Direct Mail,” published in a 1995 issue of Marketing Science.

Recency

The more recently a customer has made a purchase with a company, the more likely they will continue to keep the business and brand in mind for subsequent purchases. Compared with customers who have not bought from the business in months or even longer periods, the likelihood of engaging in future transactions with recent customers is arguably higher.

Such information can be used to get recent customers to revisit the business and spend more. In an effort not to overlook lapsed customers, marketing efforts might be made to remind them that it’s been a while since their last transaction, while offering them an incentive to resume buying.

Frequency

The frequency of a customer’s transactions may be affected by factors such as the type of product, the price point for the purchase, and the need for replenishment or replacement. If the purchase cycle can be predicted—for example, when a customer needs to buy more groceries—marketing efforts may be directed toward reminding them to visit the business when staple items run low.

Monetary Value

Monetary value stems from how much the customer spends. A natural inclination is to put more emphasis on encouraging customers who spend the most money to continue to do so. While this can produce a better return on investment (ROI) in marketing and customer service, it also runs the risk of alienating customers who have been consistent but may not spend as much with each transaction.

Significance of Recency, Frequency, Monetary Value

RFM analysis allows a comparison between potential contributors and clients. It gives organizations a sense of how much revenue comes from repeat customers (vs. new customers), and which levers they can pull to try to make customers happier so they become repeat purchasers.

Despite the useful information that is acquired through RFM analysis, firms must take into consideration that even the best customers will not want to be over-solicited, and the lower-ranking customers may be cultivated with additional marketing efforts. It works as a snapshot of the clientele and as a tool to prioritize nurturing, but it should not be taken as a license to simply do more of the same old, same old sales techniques.

The Bottom Line

The recency, frequency, monetary value (RFM) model assigns a firm’s customer base a particular trait, which can be used to improve marketing analysis. For each attribute (recency, frequency, and monetary value), customers are given a score from 1 (lowest) to 5 (best) based on their observed purchasing behavior. The ideal customer would therefore have a score of 5, 5, 5 for these three factors. Other customers with lower scores can be identified for improvement.