"Recency, Frequency, Monetary Value" or commonly known as RFM analysis is the key tool for many smart marketers who understand the importance of "Know thy Customer". Using segmentation you divide the audience into homogenous groups in order to send targeted messages.
Let’s first understand what is RFM?
RFM is a strategy for analyzing and estimating the value of a customer, based on three data points:
- Recency - How recently did the customer make a purchase?
- Frequency - How often do they purchase?
- Monetary Value How much do they spend?
When was it first used? How did it come to light?
The concept of recency, frequency, monetary value (RFM) is thought to date from an article by Jan Roelf Bult and Tom Wansbeek, "Optimal Selection for Direct Mail," published in a 1995 issue of Marketing Science. RFM analysis often supports the marketing adage that "80% of business comes from 20% of the customers."
What are the benefits?
For implementing RFM segmentation analysis, it is recommended to use data collected over a long time frame.
RFM allow you to segment your client base based on their buying behavior and define how active and profitable each group is. It is thereby considered to be one of the most effective market segmentation techniques used to target the right customers with the right solutions.
RFM Analysis in practice
RFM analysis is a good churn indicator because it examines how recently a customer has purchased, how often they purchase and how much they usually spend. You can easily detect if there's a drop-off in a customer's purchases or average spends and identify which customers are most likely to fade away or close accounts.
Answering these questions is a good starting point:
- Who are your best customers?
- Which of your customers may contribute to your churn rate?
- Who has the potential to become valuable customers?
- Which of your customers do you have the best chance of retaining?
- Which of your customers are most likely to respond to engagement campaigns?
Let’s get down to understanding the three RFM (Recency – Frequency – Monetary) variables & how they work:
RFM (Recency, Frequency & Monetary) analysis is a behavior based technique used to segment customers by examining their transaction history such as:
- How recently a customer has purchased?
- How often do they purchase?
- How much does the customer spend?
It helps to illustrate these facts:
- The more recent the purchase, the more responsive the customer will be to promotions
- The more frequently the customer buys, the more engaged and satisfied they are
- Monetary value differentiates heavy spenders from low-value purchasers
As it stands:
- Recency: Time elapsed since the last interaction a customer did with your business.
- Frequency: The number of interactions a customer had within a specified period.
- Monetary: The contribution per transaction
How RFM values are calculated for Customer Segmentation
RFM scores are calculated to quantify the purchase behavior of each customer. It provides a simple intuitive way of calculating each of the three aspects in a simple rating of 1-5, where 1 is the least important and 5 is the most important one. For example, a customer with R=5, F=5, and M=5 is the most profitable and loyal customer, while a customer with R=1, F=1, and M=1 is the least valuable.
Now let’s examine the process step-by-step
In the first step of the workflow, we collect transaction data. This should include a unique customer id, transaction date and transaction amount. In case of ecommerce, we need to decide how to treat visits that did not result in a transaction. If data is aggregated and made available at the customer level, it must include a unique customer id, last transaction date and total revenue from the customer. The last transaction date may be replaced by days since last visit as well. The details available in data supplied depends on the data pipeline and the rfm package can handle any of the above 3 scenarios.
In the second step, we generate the RFM table from the raw data available. The RFM table aggregates data at the customer level. It includes the unique customer id, days since last transaction/visit, frequency of transactions/visits and the total revenue from all the transactions made by the customer.
In the third step, we generate scores for recency, frequency and monetary value, and use them to create the RFM score for each customer.
In the final step, we use the recency, frequency and monetary scores to define customer segments and design customized campaigns, promotions, offers & discounts to retain and reactivate customers.
Let’s see an example for an ecommerce business:
Recency (R) Value:
The number of days since the last purchase is determined and a score of 1-5 is assigned. Customers who made recent purchases are given a score of 5, while customers who have not purchased for a while are given a score of 1.
Days since last purchase= R
- 0-20 = 5
- 20-100 = 4
- 100-300 = 3
- 300-1000 = 2
- >1000 = 1
Frequency (F) Value
Frequency (F) Value represents the number of purchases made by a customer. If a customer purchased 10 times over a period of time, the second customer purchased 7 times and the third customer purchased 5 times, then the first one will be assigned the F score of 5, second with 4 and third with 3 and so on.
Monetary Value (M)
Monetary Value (M) ranging from 1 to 5 denotes the monetary contribution of an individual customer.
For example, if there are 10 customers and they contribute revenue of $10,000, then this total amount can be divided into 5 segments of $2000 each. If a customer contributes between $1-$2000, then they will receive an M score of 1. Similarly, if a customer contributed $2500 in the total revenue, then they will receive a score of 2 and so on.
Let’s consider an example dataset of customer transactions to show how Customer Segmentation can be implemented.
Customer ID R F M
1 3 5 520
2 5 10 920
3 45 1 35
4 22 2 65
5 14 3 159
6 31 2 56
7 6 3 120
8 49 1 930
9 33 14 2610
10 9 5 171
The above table contains the Recency, Frequency and Monetary Values for 10 customers based on their transactions with a store. Now let’s move ahead and find out how RFM analysis is made for Customer Segmentation.
RFM Analysis for Customer Segmentation
After R, F, and M Values are taken from the transactional history, each of them is categorized in increasing order for each customer. First, we’ll arrange all the (R) Values in an increasing order for all customers and respectively score them with values of 1-5 in accordance with their related Recent. Let’s see how
Customer ID R R Score
1 3 5
2 5 5
7 6 4
10 9 4
5 14 3
4 22 3
6 31 2
9 33 2
3 45 1
8 49 1
We’ve divided the Score in 5 quintiles of 20% each (which gives scoring of 1-5 to every two customers as per their Recency). Similarly, we will score the customer’s Frequency and Monetary Value in order of Most Frequent and Big Spenders to Least Frequent and Low Spenders.
Customer ID F F Score
9 14 5
2 10 5
1 5 4
10 5 4
5 3 3
7 3 3
4 2 2
6 2 2
8 1 1
3 1 1
Customer ID M M Score
9 2610 5
8 930 5
2 920 4
1 520 4
10 171 3
5 159 3
7 120 2
4 65 2
6 56 1
3 35 1
When the RFM scores have been assigned to each customer, the customers are ranked by combining their individual R, F and M Scores. The RFM scores utilized for each customer will be the average of all the three individual values of RFM.
Customer ID RFM Cell RFM Score
1 (5,4,4) 4.3
2 (5,5,4) 4.6
3 (1,1,1) 1.0
4 (3,2,2) 2.3
5 (3,3,3) 3.0
6 (2,2,1) 1.6
7 (4,3,2) 3.0
8 (1,1,5) 2.3
9 (2,5,5) 4.0
10 (4,4,3) 3.6
By creating a RFM Score for each customer, segments can be formed and targeted with campaigns designed to achieve specific objectives. The implementation of RFM Analysis helps marketers to understand who the most loyal customers are and which customers contribute the most value to the business. In addition, scores can be tracked over time to highlight changes in customer behavior, changes that could trigger customer support activities or targeted offers.
Recency, Frequency and Monetary value (RFM) is a marketing analysis tool used to identify a firm's best clients, based on the nature of their spending habits.
An RFM analysis evaluates clients and customers by scoring them in three categories: how recently they've made a purchase, how often they purchase, and the value of their purchases.
RFM analysis helps firms reasonably predict which customers are more likely to make purchases again in the future, how much revenue comes from new versus repeat clients, and how to turn occasional buyers into habitual ones.