RFM Analysis Methodology For Ideal Customer Segmentation
March 31, 2020
RFM Analysis is a substantial marketing model that analyzes customer’s purchase behavior and formulates Customer Segmentation. The objective of RFM Analysis is to segment customers according to their purchase history, and turn them into loyal customers by recommending products of their choice.
The analysis of customer RFM initiates by grouping customers based on their buying behavior, in terms of how recently they bought, how often did they bought, and how much monetary value they contributed to the store.
RFM Analysis analyzes three parameters each denoted by the letters R, F, and M. To satisfy the need of knowing true customer value, analysis of just one parameter will give an inaccurate report of the customer base, so the customer’s lifetime value can’t be reliable. This is why at least three parameters of customer’s purchase behavior are analyzed; with the freedom to add other analytical parameters too.
Let’s find out what are these three parameters and why are they so important for your e-commerce business.
Power of the RFM Parameters
RFM stands for Recency, Frequency and Monetary Value. Recency means how recently a customer bought from your store, Frequency means how frequently a customer is buying from you, and Monetary Value means what is the amount of money a customer spent in your store. These three parameters altogether determine the importance of customers for the retail shop.
RFM Analysis is useful due to its simplicity, intuitiveness, and utilization of objective on numerical scales. This yields a comprehensive and informative depiction of customers. The output of this segmentation method is easy to interpret and adapt.
Let’s check out the questions that RFM answers with reliable accountability. And then know the method to calculate and analyze RFM.
What are the questions that RFM Analysis answers?
Using RFM Analysis, a store retailer can find answers to following questions, which have always placed a question mark between retailer and customers:
- Who are the loyal customers of a store?
- Which customers a store must retain?
- Which customers have the potential to convert into regular profitable customers?
- Who are those inconsistent customers who don’t engage regularly with the store?
- Which group of customers is most probable to respond to your campaign?
- Who are the churned out customers and who are about to churn?
RFM Analysis hands out the answers to these questions and brings affront the true customer value to the business.
As we have discussed the Importance of RFM Analysis, it’s time to know How RFM is calculated, and How it is analyzed to segment customers for promotion.
How RFM values are calculated for Customer Segmentation
RFM scores are calculated to know the purchase behavior of any 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 contributing one.
Following are the methods to evaluate the R, F, and M Values:
Recency (R) Value
Recency (R) Value represents how recently a customer has made a purchase. The number of days since the last purchase is acknowledged and a score of 1-5 is assigned to customers. The customers who made a purchase most recently are given a score of 5, while the customer who bought far back is 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. Every customer who contributes to the revenue of store can be assigned a value of M.
For example, if there are 10 customers and they contributed revenue of $10,000, then this total amount can be divided into 5 segments of $2000 each. If a customer contributed something between $500-$1500, then it will get the M score of 1. Similarly, if a customer contributed $2500 in the total revenue, then it will get a score of 2 and so on.
After figuring out the RFM Values of customers, proper customer segmentation is targeted to achieve. Let’s consider an example dataset of customer transactions to know 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 get 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 increasing order for all customers and respectively score them with values of 1-5 in accordance with their related Recency.
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 Value in order of Most Frequent and Big Spenders to Least Frequent and Low Spenders.
Let’s see how
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
Similarly, we will score the customer’s Monetary Value in order of Big Spenders to Low Spenders.
Let’s see how
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 we are done with assigning the RFM scores to each customer, we’ll now rank Customers 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 figuring out the RFM Score, the customers having relevant scores can be combined to form a Customer Segment and then the related campaigns and promotions are made for each customer segment.
The implementation of RFM Analysis helps to figure out the most loyal customers, potential customers, new customers as well as those customers who perform differently throughout the sales channel.
RFM Analysis is an easy as well as impressively fruitful analysis method to extract reliable insights. Although, the future of RFM Analysis includes various updates, which primarily includes the addition of other analytical dimensions.
This type of Analysis drives productive results in comparison with those models that consider less than 3 parameters of purchase behavior, but the further models which analyze additional aspects are much more insightful and reliable.
The Future of RFM Analysis
The future of RFM Analysis is as bright as it’s past and the present because it’s functionality and results, are trustable as far as analysis of three aspects is concerned. However, one of the other major Analytical model LRFM, which also considers the L parameter (Measure of how long a customer has been associated with the store) is much more productive than its predecessor.
RFM Analysis is capable of Customer Segmentation for Lifetime of the store, which isn’t the case with the LRFM Analysis Model. LRFM considers time-variant and is able to analyze and segment Customers in response to different timeframes.
LRFM gives the flexibility which remained untouched in the RFM Model. This is why, the traditional RFM Model is not abandoned, but it is revived with an effective update.
Enalito, through its LRFM Analytics Tool, provides a simple intuitive way of abstracting customer behaviors to form effective Customer Segments. It recommends relevant products to customers who are most probable and interested to buy, and contribute to the increased sales and Revenue.
We assist to provide expert guidance in pitching customers as per their Monetary Potential and every related buying attribute. Our Predictive Analytics engine helps to recommend the ideal product suggestions, which may drive the sales flow to the heights you could only imagine so far.
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