“When psychology meets data, magic is obvious!”

Normally marketers segment users based on gut intuition. But is it the best practice? How can we know what those segments should be? And, how to decide when to revisit segments for contextual accuracy? Moreover, in some unique cases, how should we prioritize or deprioritize customers not falling into any of the pre-stated segments? Enter RFM Score Analysis.

Customer segmentation, on the one hand, is a standardized process that helps today’s decision-makers justify the customer reach-out strategy. On the other hand, RFM Score Analysis helps satisfy to what degree those decision-makers choose to pursue specific customer segments. In short, RFM Score Analysis is a pretty neat methodology to maximize the customer outreach strategy’s efficiency.

In this article, we will dive deeper into the how and why of RFM Score Analysis. We will talk just, and declutter the nuances to better communicate with each customer segment. 

What is RFM Score Analysis, and What does “RFM” Stand for?

RFM stands for Recency, Frequency, and Monetary value. 

Recency: A high recency score indicates that a client recently evaluated your brand for a purchasing decision. Depending on the nature of the business, recency can be evaluated by grading on custom-built filters such as purchased in the previous 7 days/1 month/3 months, and so on.

Frequency: A high-frequency score indicates that a client buys your brand regularly and is likely to be a brand loyalist. Businesses must assess the total number of purchases made by clients in a given time period to compute frequency. Depending on the nature of the business, the frequency can be assessed by grading on custom-built filters such as bought three times in a year/bought once a month, and so on.

Monetary Value: A high monetary value score indicates that a consumer is one of your brand’s most frequent spenders. Depending on the nature of the business, the monetary value score may be rated using custom-built filters such as spent more than Rs.10,000/30,000/50,000 and so on.

The RFM Score Analysis objectifies customer segments based on most recent purchases, the frequency of purchases in the past, and total spending. All three of these indicators have been shown to be good predictors of a customer’s readiness to engage with marketing communications and offers.

In fact, these RFM metrics are critical indicators of consumer behavior because they influence lifetime value (LTV), customer retention, and brand engagement.

So, to sum up. Here are some conclusions that can be made using RFM factors:

  1. If your customer is a recent purchaser, your current promotion is effective
  2. If your customer is a regular buyer, she/he is a loyal customer with a growing LTV.
  3. If your customer has a larger Average Order Value (AOV) than a low spender, he is a heavy spender or a fan of your brand.

There also exist some exciting factors which today’s industry leaders should consider based on their industry lineage:

  1. R and F are crucial characteristics to consider while selling staple goods.
  2. R and M are crucial aspects to consider when selling home appliances.
  3. F and M are critical aspects to consider when selling subscription-based services.

How RFM Score Analysis Aids in Business Understanding?

RFM Score Analysis divides potential consumers into various groups, allowing firms to speak to them individually. And it improves a company’s capacity to reduce churn by utilizing basic marketing principles such as segmentation, targeting, and positioning. It aids in answering the following questions:

  1. What distinguishes customers from each other?
  2. Who is most likely to become the customer?
  3. Are all the customers similar?

Why Choose RFM Score Analysis Over Traditional Customer Segmentation Models?

The RFM model is based on transactions between the user and the company, resulting in a strong data-backed technique based on concrete figures. This consumer data is graded, studied further, and segmented in order to engage clients as various groups. The RFM methodology assists organizations in efficiently analyzing each customer’s past purchasing behavior in order to forecast and affect future customer encounters.

On the other hand, Traditional techniques of segmentation, utilized by market research firms prior to the introduction of data analytics, categorize their customers based on criteria such as demographic and psychographic features. Because researchers always employ sample audiences to forecast population behavior, market researchers’ capacity to predict user behavior of narrow consumer sets and unique consumers is limited.

How to Perform RFM Score Analysis, Step by Step?

Step 1: Assigning RFM Score Factors to Each Customer

The initial stage in developing an RFM model is to assign each customer Recency, Frequency, and Monetary values. The raw data for this can be compiled in an Excel spreadsheet or database, which should be easily available in the company’s CRM or transactional databases:

  1. Recency will define the duration of time since the most recent transaction with the consumer (most businesses use days, though for others it might make sense to use months, weeks, or even hours instead).
  2. Frequency is defined by the total number of transactions made by the consumer in a defined duration of time.
  3. Monetary value is the total amount spent by the consumer across all transactions in a defined duration of time.

Step 2: Segmenting Customers into Tiered Groups

The second step is to use Excel or another application to segment customers into tiered groups for each of the three dimensions — R, F, and M. Unless utilizing specialist software, it is suggested that consumers should be segmented into four levels for each dimension, with each customer allocated to one tier in each dimension:

R-Tier-1 (most recent)F-Tier-1 (most frequent)M-Tier-1 (highest spend)
R-Tier-4 (least recent)F-Tier-4 (only one transaction)M-Tier-4 (lowest spend)

Customers will be divided into 64 unique categories (4x4x4) as a result of this. Three layers can also be utilized (resulting in 27 parts); however, employing more than four is not advised because the difficulty in use outweighs the small benefit gained from the extra granularity.

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Step 3: Selecting Customer Segments Based on RFM Analysis

The third stage is to determine which groups of consumers will get various sorts of messages depending on the RFM segments in which they appear.

It is beneficial to provide names to areas of interest. Here are a few instances to demonstrate:

  1. Best Customers: This category includes customers who are in R-Tier-1, F-Tier-1, and M-Tier-1, indicating that they transacted lately, often, and spent more than other customers. This section may be written as 1-1-1; we’ll use this notation from now on.
  1. High-spending New Customers: This category includes customers in 1-4-1 and 1-4-2. These are consumers who only transacted once, but very recently, and spent a lot of money.
  1. Lowest-Spending Customers:  Segments 1-1-3 and 1-1-4 are considered active loyal customers (they transacted recently and do so often, but spend the least).
  1. Churned Best Customers: This sector includes customers from groups 4-1-1, 4-1-2, 4-2-1, and 4-2-2.

Step 4: Crafting Messaging to Target Niche Audiences

The fourth phase really goes beyond RFM segmentation by generating a specialized message for each target category. RFM marketing enables marketers to interact with customers in a much more effective manner by concentrating on the behavioral patterns of certain groups.

Again, using the above-mentioned groupings as examples, here are a few more:

  • Best Customers: When communicating with this group, they should feel respected and appreciated. These clients are likely to account for a disproportionately large share of overall revenue, therefore keeping them satisfied should be the goal. Analyzing their particular tastes and affinities will open up new avenues for even more targeted messages.
  • High-spending New Clients: It’s usually a good idea to properly “incubate” any new customers, but because these new consumers spent so much money on their initial transaction, it now becomes much more crucial to retain them. As with the Best Customers category, it’s critical to make them feel respected and appreciated.
  • Lowest-Spending Customers Who Are Active and Loyal: These repeat customers are active and loyal, yet they are low spenders. Marketers should build campaigns that make this segment feel appreciated and motivate them to improve their spending levels. It typically pays to reward loyal consumers with special deals if they spread the word about the company to their friends, for example, through social networks.
  • Best Customers Churned: These are important clients who have not transacted in a long time. While re-engaging churned clients might be difficult, the tremendous value of these consumers which makes it important to attempt. As with the Best Customers category, it’s critical to engage with them based on their individual preferences, as determined by previous transaction data.


Hence, the Winning Sales Strategy = BI + Psychology. For those who want to master the art to sell, a concise mix of behavioral data and applied psychology does wonders. With this mix, today’s organizations can experience community building, which will help them sell to customers that truly love their products. With this mix, organizations can also target customer segments with correct messaging and offerings that satisfy their immediate needs.

Knowing your customer’s pulse is critical; then, everything you say becomes the prescription, and the sale is automatic. Magical sales figures are created by the careful synchronization of strategy and tools. Make your own magic instead of waiting for it to happen.

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And, do share your experience of learning about RFM Score Analysis in the comments below.

Yash Arora
Content Manager, Hevo Data

Yash is a Content Marketing professional with over three years of experience in data-driven marketing campaigns. He has expertise in strategic thinking, integrated marketing, and customer acquisition. Through comprehensive marketing communications and innovative digital strategies, he has driven growth for startups and established brands.

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