The Synerise AI Developer Hub

Welcome to the Synerise AI developer hub. You'll find comprehensive guides and documentation to help you start working with Synerise AI API as quickly as possible, as well as support if you get stuck.
Let's jump right in!

RFM Segmentation


RFM segmentation is a method 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 set RFM method uses three different attributes:

Recency of the last purchase (R) - refers to the interval between the time that the latest customer's purchase happens and 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 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 M score is.

How does RFM Analysis work?

First, customers are divided into 5 equal sized groups (20% in each group). Customers are then given an R, F, & M score from 1 to 5. Using a score of 1 to 5, 20% of the most recent customers get an R score of 1. The second most recent get an R score of 2 and this continues until all 5 groups receive a score. The 5 groups are reorganized to repeat the procedure for the F & M scores.

Customers who purchased recently, are frequent buyers and spend a lot are assigned a score of 555 – Recency (R) – 5, Frequency (F) – 5, Monetary (M) – 5 -They are your best customers.
Customers who spent the lowest, making hardly any purchase and a long time ago – assigned a score of 111. Recency (R) – 1, Frequency (F) – 1, Monetary (M).

Once classification is done we split customer base into 11 clusters:

Advantages of using RFM Analysis

Advantages of applying RFM Segmentation to Your business:

  • RFM improves customers lifetime value by reducing churn, offering upsells and cross-sells to segments that are more likely to respond.
  • RFM makes email marketing better because it automates moving people between segmented list if they move from one RFM segment to another one.
  • RFM is great for new product launches, especially when you want to get initial traction and feedback. You have a possibility to contact with your best, and the most loyal customers even before creating a product.
  • RFM reduces customer churn by sending personalized emails and calls to reconnecting with these customers.
  • RFM increases user engagement and loyalty by sending personalized promotions, and educational content which will increase their relationship with your brand.
  • RFM minimizes marketing campaign costs and improve Rol by focusing on a smaller segment of customers will significantly reduce costs, allow you to do more experimentation, and make decision based on data.
  • RFM is a good idea for retargeting campaigns and remarketing especially when you want to export a part of your customers to another campaign management solution.

RFM Segmentation

Suggested Edits are limited on API Reference Pages

You can only suggest edits to Markdown body content, but not to the API spec.