RFM Analysis
16 May, 2022 by
Payal Kheradiya
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A Perfect Analytical Methodology for Customer Segmentation

As a smart marketer, RFM aids in the perfect and easiest analytical Methodology useful for “segmentation of customers” based on their purchase behavior. This method aids in building “Best customer Relations” by allowing us to study and analyze the behavior of customers based on their purchase history and thus segment them into a homogenous group, have a valuable insight into a customer's choice for a particular product which then helps us in direct digital Marketing of a particular product on a focused category of customers.

Table of content:

  • Why does a Market need it?
  • What is RFM Methodology
  • Why is RFM Methodology better than the traditional segmentation method?.
  • The questions RFM analysis is able to answer.
  • What is the method to calculate RFM values for customer Segmentation?
  • Analysis based on RFM Segmentation.
  • Leading Customer Segmentation and RFM analysis in Odoo

Why does a Marketer need RFM?

To know the true “customer’s value”, analysis based on just one parameter gives an inaccurate report on the Product.
Say for example as a Business Owner you wish to decide to give offers to your customer like

  • Discount Coupons 
  • Brand Products Awareness Coupons and Rewards.

The decisive analysis that becomes complex here is 

“Which Coupons to offer to which Customers?”

As a Smart Marketing Strategist, it is a very Straight forward analysis that Discount Coupons should be given to Newest Customers

Brand Products Awareness Coupons should be offered to Highest Paying Customers.
If because of unavailability of any methodology that helps to identify the Highest Paying Customers and the Newest Customers, both coupons will be offered to all customers, and because of that now even Highest Paying Customers will use Discount Coupons who were capable enough of Purchasing costly Brand Products generating highest Revenue.

This instead of generating more Profit is leading your Business to a Loss in profit. Thus dividing Customers into Segments based on their behavior becomes a mandatory process for any Business owner to Sustain in this most competitive World.

Thus, dividing the Customers into Segments based on their activities and behavior is a necessity at any Cost.

But then, the most problematic and unsolvable Question here arises is 

“How to know Which Customers are Highest Paying and Which Customers are Newest?”
Not only Highest Paying and Newest, but other types of Behavior of Customers like
Which group of customers are the Regular Customers though not Highest Paying?
Which group of Customers have now become Non-Regular Customers?
Which group of Customers is Highest Paying as well as Regular Customers?
Which group of Customers are not Regular but purchase from your Store only?

Various Techniques and Solutions are available that help to divide customers into Segments implementing various technologies. One such technique among all is RFM Segmentation which considers all the parameters necessary for Marketers to know and implement while dividing Customers into various segments. 

Here Let us see How RFM Customer Segmentation divides customers into various Segments, and which parameters it uses that makes it a top-notch analytical solution among all methodology for Customer Segments.

What is RFM Methodology?

RFM(Recency, Frequency, Monetary) is a statistical technique used to analyze customers by grouping them based on their buying behavior from recent transactions, in terms of

Recency(R)- How recent does a customer purchase i.e. Last purchase date?

Frequency(F)-how often do they purchase i.e Total no. of Purchase? and

Monetary(M)- How much they contributed to the store i.e. Total Monetary value

Why is RFM Methodology better than the traditional segmentation method?

The RFM model is built on transactions between the user and the business, it creates a robust data-backed method based on hard numbers.
This customer data can be used for further analysis and segmented in order to target customers as distinct groups.
This model helps businesses effectively by analyzing the previous buying behavior of each customer and then helps to predict and shape future interactions with customers.

In the Traditional method used by Market Researchers of companies in absence of Data Analytics, variables like demographic and psychographics were used to create customer groups. Researchers here create sample audiences to predict the behavior of the population, hence this method reduces market researchers’ ability to predict the behavior of actual consumer sets and real-time customers.

The questions RFM analysis is able to answer.

  • Who are the best customers?
  • Which customer has the potential to buy more?
  • Which customer has been cranked out?
  • To Which customer can this business afford and to whom to ignore to effectively utilize budgets?
  • Which customer can be converted by creating value through promotions?
  • Which customer is likely to be loyal in the near future?
  • Which customers are Highest Paying but not Regular Customers?
  • Which Customers are both Highest Paying and Regular Customers?
  • Which Customers are not Regular but Purchase from your store only?
  • Which Customers were Regular Purchasers previously from your store but now do not Purchase from your store?

What is the method to calculate RFM values for customer Segmentation?

Now that we’ve known the basics and benefits of RFM methodology, let us now understand the steps involved in conducting RFM analysis practically on customers.

“RFM Analysis: A 4 step approach”

For our better understanding, we have divided RFM into 4 steps

STEP 1: Collection of relevant data.

RFM model involves analysis based on a customer's transaction history. The very first step is to sort customers’ data in descending order based on the Recency value.

STEP 2: Deciding the RFM scales.

As mentioned earlier, businesses need to create custom filters in order to effectively create segmentation of the customer group. Sample filters are created below, in order to understand easily but the thing here to note is an important aspect is this data varies based on the nature of their business.

Step 3: Assigning Score.

Now based on the RFM scale each customer is given a grade.

STEP 4: Labeling Segments.

Now from step 3 we need to label each customer and can then divide customers into various segmentation groups giving them labels like Champion customers, Highest Paying Customers, Loyal customers, Promising customers, Sleeping customers, Recent Users, and then based on the label apply different marketing strategies for different customer groups.

Analysis based on RFM segmentation.

The Leading Customer Segmentation and RFM Analysis in Odoo Solution

RFM analysis in Odoo is the most advanced Customer Segmentation, modeling and predictive, and analytical Solution that aids all Marketers to have a complete insight of Customer Purchase Behavior finally What Strategy should be implemented on which Customers that proves to be the best throughput achieving Optimum Sales and Optimum Profit. This Customer Segmentation Software not only divides Customers into Segments but various analytical reports help your Business to analyze Marketing Strategies implemented on Customers whether Progressive or there should be some changes in Strategies. Also aids Marketers to implement a systematic approach of ideation to planning, executing, and Optimizing a complete, highly successful Marketing plan.
Have a glance and learn more about How RFM analysis in Odoo is the most optimized systematic Solution for any Business and Marketer that increases Sales to Optimum Quantity and possesses long-term customer loyalty and lifetime value.

Payal Kheradiya 16 May, 2022
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