As a smart marketer, RFM aids in the perfect and easiest analytical methodology useful for “segmentation of customers” based on their purchase behaviour. This method aids in building “Best Customer Relations” by allowing us to study and analyse the behaviour of customers based on their purchase history, enabling segmentation into a homogeneous group. It provides valuable insight into a customer's choice for a particular product, facilitating Digital Marketing of a particular product on a focused category of customers.
Table of content:
- What is RFM Analysis?
- Why Marketers Should Opt for RFM Analysis?
- What is the Method to Calculate RFM for Customer Segmentation?
- RFM Analysis and Traditional Segmentation: Which is Better?
- Questions our RFM Analysis App can Answer
- Conclusion: Leading Customer Segmentation and RFM Analysis in Odoo
What is RFM Analysis?
RFM(Recency, Frequency, Monetary) is a statistical technique used to analyse customers by grouping them based on their buying behaviour 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 Marketers Should Opt for RFM Analysis?
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 behaviour 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 behaviour 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 Behaviour 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 the Method to Calculate RFM 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
Why RFM Methodology is 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 analysing the previous buying behaviour 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 behaviour of the population, hence this method reduces market researchers’ ability to predict the behaviour of actual consumer sets and real-time customers.
Questions our RFM Analysis App Can 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 utilise 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?
Conclusion: Leading Customer Segmentation and RFM Analysis in Odoo
RFM analysis in Odoo is the most advanced Customer Segmentation, modelling and predictive, and analytical Solution that aids all Marketers to have a complete insight of Customer Purchase Behaviour 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 Optimising a complete, highly successful Marketing plan.
Have a glance and learn more about How RFM analysis in Odoo is the most optimised systematic Solution for any Business and Marketer that increases Sales to Optimum Quantity and possesses long-term customer loyalty and lifetime value.