RFM analysis

RFM segmentation is a method used to identify clusters of customers for special treatment. It is commonly used in database marketing and direct marketing and has received particular attention in many different industries. In order to differentiate customers from data sets RFM method uses three different attributes:

  • Recency of the last purchase (R) - refers to the interval between the time of the latest customer purchase and the current date. The shorter the interval between current date and last purchase, the bigger R is.
  • Frequency of the purchases (F) - refers to the number of transactions in a particular period. The bigger number of transactions, the bigger the F score is.
  • Monetary value of the purchases (M) - refers to monetary value of products purchased by the customer. The more the customer spends, the bigger the M score is.

This lets you isolate groups, identify new or most active clients, and target personalized messages based on transaction data. Based on RFM analysis you can then divide the customer base into three segments:

  1. RECENCY - RECENT buyers (new clients)
  2. FREQUENCY - FREQUENT buyers (loyal clients)
  3. MONETARY - Buying THE MOST (VIP clients)

You can use Synerise algorithms to divide your database into the 11 clusters presented below. These clusters can show you groups of customers based on their shopping behavior.

Screenshot presenting RFM analysis


Learning more about your most profitable clients, both in terms of money spent and engagement, is key to your success. It’s worth knowing more about who they are, what they are buying and why. After identifying these segments, it is necessary to examine their conversion paths and shopping habits, including:

  • How many times they visited your store before making their first purchase
  • The days and times when they make the most purchases
  • Whether they buy online or offline
  • How much time they spend on your site before making a purchase

All this allows you to optimize communication with your most important customers. Decisions regarding rebates and their forms are also easier when you have a more accurate picture of who your customers are and how they make their purchases.

Example of use

The main goal of our client from the automotive industry was to create automatic customer segmentation based on RFE ​(recency, frequency, engagement) analysis, which aims to assign customers to specific segments in an automatic manner.​

The main assumption of the RFE analysis is to optimize segments for specific campaigns. This is done in order to reduce costs related with, for example, retargeting, and for defining which customers should be re-activated, which should be qualified for lead nurturing campaigns, as well as identifying:​

  • How to find customers who are not responsive despite many attempts to contact them and which customers should be excluded from, for example, a performance campaign​,
  • How many customers are going to convert in near future​,
  • How to differentiate cold leads customer from hot leads​.

Screenshot presenting RFM analysis

Read more

Read more about our RFM segmentation

This use case belongs to the following categories:




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