Recency, Frequency, and Monetary (RFM)


RFM stands for Recency, Frequency, and Monetary. These three factors are used to analyze customer behavior and segment customers based on their history of interactions with your brand. Recency refers to the amount of time since a customer’s last interaction, frequency refers to the number of interactions, and monetary refers to the total value of those interactions. By analyzing these three factors, you can identify customers who are most likely to buy from you and target them with the right messages at the right time.

A common way of using RFM is to use a three-digit rating for the score, consisting of:

  • Recency: How recently the customer made a purchase
  • Frequency: How frequently the customers makes purchases
  • Monetary: The value of the purchases made

If using a 1-5 scale for each, a total RFM score where recency is 3, frequency is 4, and monetary is 5 would be calculated as 3+4+5 or 12.

Why is RFM important?

RFM is important because it helps you understand your customers better. By segmenting your customers based on their behavior, you can tailor your marketing efforts to their needs and preferences. For example, if you identify a group of customers who haven’t interacted with your brand in a while, you can send them targeted campaigns to re-engage them. Similarly, if you identify a group of high-value customers, you can create special offers and promotions to reward their loyalty and encourage more purchases.

How do you use RFM?

To use RFM, you’ll need to gather data on your customers’ interactions with your brand. This data can come from a variety of sources, including your website, social media, email campaigns, and sales data. Once you have this data, you’ll need to score your customers based on their recency, frequency, and monetary values. Then once you have these scores, you can segment your customers into different groups based on their behavior. You can then use this segmentation to tailor your marketing efforts to each group.

What are some best practices for RFM analysis?

When conducting RFM analysis, there are some best practices you should follow to ensure you get the most out of your efforts. First, make sure you’re using the right data sources. You want to gather data from as many sources as possible to get a complete picture of your customers’ behavior. Second, be consistent in your scoring methodology. You want to make sure that you’re scoring customers consistently across all three factors. Finally, use your segmentation insights to create targeted campaigns that resonate with each group. You want to create messaging that speaks to each group’s unique needs and preferences.

Average Order Value (AOV)

Customer Lifetime Value (CLV)

North Star Goal: Customer Lifetime Value Model


Book: House of the Customer (2023) by Greg Kihlstrom