How to Calculate RFM Score: The Key to Unlocking Customer Loyalty and Growth

RFM stands for recency, frequency, and monetary value, three key indicators used to analyze customer behavior and identify groups of customers that share similar characteristics.

Understanding RFM analysis can help you get a pulse on your current customer engagement levels (recency), identify loyal and regular customers (frequency), and highlight high-value customers (monetary value). 

With RFM, you can segment customers based on their purchasing patterns to help you predict future behavior, target your marketing efforts effectively, find your best customers, and increase customer retention. Customer loyalty is a critical factor that can make or break your business.

RFM analysis allows you to zoom out and look at the bigger picture of your customer base, helping you make informed decisions on how to allocate resources and prioritize your marketing strategies to improve customer engagement and retain customers.

Here's a quick overview of the three cornerstones of RFM analysis: 

Recency provides insights into current customer engagement levels by measuring how much time has passed since their last purchase.

Frequency lets you see which customers are loyal and engage with your brand or company regularly by measuring how often customers interact with you within a specific period of time. 

Monetary value helps to highlight which customers are most valuable and generate the most revenue for your business. This number represents the total amount of money a specific person has spent on your company as a customer. 

Understanding recency, frequency, and monetary value will help you make informed decisions about how to optimize your marketing strategies, increase retention, identify your best customers, and ultimately drive business success.

Let's understand RFM metrics  

Now that we’ve covered the basics, let's take a deeper dive into understanding the RFM score.        

What is RFM Score?

An RFM score is a numerical value representing customer recency, frequency, and monetary value metrics.

Calculating RFM scores allows you to get a better overview of customer performance and assign a value based on a numerical scale, typically 1-5, with 5 being the highest and 1 being the lowest, for each customer. You can use this scale to organize your customers by their value, with customers in tier 5 being your best customers. 

Combining the individual recency, frequency, and monetary value scores creates the RFM score, which represents the customer’s overall value or level of engagement.

Higher scores typically indicate that the customer is more valuable and engaged than those with lower scores. 

Why use RFM customer segmentation in behavior analysis?

RFM segmentation is important in customer behavior analysis because it helps businesses gain insights and understanding into their customers’ purchasing behavior and preferences.

Using RFM analysis, you can identify different groups of customers with similar characteristics and tailor marketing strategies accordingly. RFM helps businesses understand which customers are most valuable, which are at risk of churning, and how to engage and retain customers effectively. 

Research has shown that it's generally easier for businesses to sell to existing customers than to new ones. Existing customers are more likely to make repeat purchases because they’ve already shown an interest in your brand.

In fact, the chances of selling to an existing customer can range from 60-70%, whereas the chances of selling to a new customer are significantly lower, ranging from 5-20%. This is why it's crucial for businesses like yours to focus on retaining existing customers and providing them with a positive customer experience.

How to measure recency

Recency represents the length or duration of time that has passed since a customer’s last purchase. Recency is an important measurement because customers who have made recent purchases are more valuable than those who haven’t.

To measure this, recency can be calculated as the difference between the current date and the date of the customer’s last purchase. 

For example, if today is April 21, 2023, and the customer you’re measuring recency for made a purchase on March 15, 2023, the recency would be 37 days because 37 days have passed since their purchase on March 15th.

At Patch, we consider any recency that goes beyond 365 to be a potentially “lost” customer, so measuring recency regularly is important. 

How to measure frequency

Frequency is an important measurement because customers who make more frequent purchases are more valuable than those who don’t. Frequency represents how often a customer has made purchases over a given period of time.

This can be calculated by counting the number of unique purchases made by a specific customer during a certain time period. 

For example, if a customer made three purchases over the course of six months, their frequency would be three. 

How to measure monetary value

Monetary value is an important measurement because customers who continually spend more money are more valuable than customers who spend less. It represents the total amount of money a customer has spent on your brand and can be calculated by adding up the total purchase amount for a customer over a particular period of time. 

For example, a customer who spent $100 over the last month has a monetary value of $100.

So, how do I calculate my customer's RFM score?

Before you begin calculating, it's important to take some time to prepare your data and ensure it’s accurate for the best results. To ensure your data is ready for calculations, you’ll want to make your way through some data preparation steps first. 

Step 1: Prepare your data

Data preparation involves identifying relevant data sources, cleaning and preprocessing data, and dealing with missing values. It's important that you prep your data prior to calculations to make sure you get the most accurate score.  

Here’s a quick overview of how to prep data for RFM calculations.

• Identify: Determine which data sources are relevant for your calculations. This might include transactional data like purchase history, customer identification, the date of the transaction, and the value of the transaction. 

• Data cleaning: Clean and preprocess data to ensure that it is accurate and consistent. This could involve removing any duplicate data, correcting data entry errors, and standardizing data formats. 

• Missing values: Deal with missing values appropriately by inputting missing values or removing records that are missing data.

Without prepping your data, you could be making a crucial mistake by calculating irrelevant, inaccurate, or missing data. Getting your calculations done right with accurate data is the best way to ensure you aren’t wasting any time.

Studies show that the average financial impact that poorly prepared data has on businesses is around $9.7 million per year, but the costs of poor data aren’t just financial; poorly prepped data can impact your business’s reputation, increase risk with decision making, and cause you to miss out on opportunities for improvement. 

Step 2: Calculate RFM scores

Now that your data is clean and ready for calculations, you’ll need to determine the scoring methodology.

Scoring methodology

You can determine the scoring methodology by assigning scores based on percentiles and ranking customers based on their RFM values. You’ll need to decide which type of percentiles you want to organize customers into, whether they’re quintiles (equal groups of five), quartiles (equal groups of four), or deciles (equal groups of ten), and then organize the customer segment percentiles into different groups, aka buckets, based on their percentile value. 

Percentiles

The first scoring methodology involves calculating percentiles for each RFM component based on the distribution of values in the data. 

Percentiles are the percentages of values that fall below or at a certain number. For example, if there is a group of 20 people and you are scoring based on height and you are the fifth tallest person in the group, you are at the 75th percentile because 75% of the people in the group are shorter than you are. 

Quartiles are four equal ranges of the percentile, like 25%, 50%, 75%, and 100%.

In the same group of 20 people, the first 1-5 people would be in the 1st quartile (25%), 6-10 in the 2nd (50%), 11-15 in the 3rd (75%), and 16-20 would be in the 4th (100%). If you are the fifth tallest person in the group, you are in the 75th percentile.   

Remember, deciles are the same as quartiles, except they are measured in ten equal groups; 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 

Bucketing 

After you’ve organized customer segments based on percentiles, you will want to organize the RFM values into buckets (also known as bins) based on discrete groups like quartiles or deciles. This may look like having five different buckets, each at a 20% interval, like 20, 40, 60, 80, and 100, or ten bins at 10% intervals, depending on the percentile you choose. 

By organizing the values into different buckets based on the percentile they’re at, you’ll be able to get a clear overview of customer engagement and purchasing behavior based on RFM scores. Using the bucketing method, you’ll be able to determine which customers have a higher score and will be easier to retain in comparison to those with lower scores, who may be more difficult to reengage. 

Step 3: Determine the optimal number of buckets

After bucketing your data, you’ll need to examine and evaluate different bucketing methods, like equal width or equal frequency bucketing, to determine the optimal number of buckets for each RFM component.

Once you’ve decided on your optimal number of buckets, you’ll want to adjust the bucket sizes based on the specific characteristics of your business and the desired level of detail in the RFM scores.  

Step 4: Assign scores to each customer

You will need to assign a score to each RFM component and each customer based on the buckets or percentiles calculated previously. Higher scores typically indicate customers with higher recency, frequency, and monetary value, and these are the customers you’ll want to go after first as they’re the most likely to reengage. 

Step 5: Combine RFM Scores

Combine the individual RFM scores into one by adding them together or taking a weighted average of the individual scores. Combining scores is a simple way to get an aggregated score for each customer. Next, we’ll discuss the different methods used for calculating the combined RFM score. 

What are the different RFM score calculation methods?

When it comes to calculating RFM scores, there are a few different methods you can use, like concatenation, addition, or a weighted average. 

Concatenation

Calculating RFM scores with concatenation involves combining the individual scores (recency, frequency, and monetary value) into one single score to create a string or code that represents the RFM score for each customer. 

For Example: 

RFM score = RScore + FScore + MScore 

Recency 5 & Frequency 3 & Monetary 100 = 53100 

5 & 3 & 100 = 53100

The string of code representing the score would be 53100. 

Addition

Another way that you can calculate the RFM score is by using addition. To do this, you will need to add all of the individual scores together to get one score. 

For Example: 

Recency 5 + Frequency 3 + Monetary 100 = 108 

100 + 5 + 3 = 108

The sum of this formula will represent the RFM score of 108. 

Weighted average

To calculate a weighted average RFM score, you will need to assign weights to the individual scores based on their relative importance.

After assigning weights to each individual score, you can calculate the weighted average to make a single score that represents the RFM for each customer. 

Weights are typically assigned based on industry knowledge, domain expertise, or data-driven approaches like statistical analysis or machine learning algorithms. The key is to ensure that the assigned weights align with your business goals and priorities.

Depending on which factors are most important to you, you can assign different weights to give a higher value to what you prioritize and a lower value to less prioritized factors.

For example, maybe your business sells luxury items, and monetary value and frequency are more important than recency. 

Once you’ve determined the weights for each RFM factor, you can use them in the weighted average RFM score formula.

Each weight represents how much each factor contributes to the overall score; RWeight, FWeight, and MWeight.

The average weight is calculated by multiplying each individual score by each individual weight, summing up all the results, and then dividing the number by the sum of weights to get an average score. 

For Example: 

Let's say you already calculated your individual scores, and these are the results: 

RScore 4 

FScore 5 

MScore 7 

Next, you will want to assign weights to each score, which could look like this:

RWeight 0.3 

FWeight 0.4 

MWeight 0.3 

To calculate the weighted average using these numbers, the formula would look like this: 

RFM Score = (RScore x RWeight) + (FScore x FWeight) + (MScore x MWeight) 

= (4 x 0.3) + (5 x 0.4) + (7 x 0.3) 

= 1.2 + 2 + 2.1 

Weighted Average RFM Score = 5.3

How can I select the most appropriate method for my business?

Now that you know the importance of RFM segmentation and how to calculate your RFM score, you’ll need to select the most appropriate method for your business.  

Consider business objectives

As with any business decision, you’ll have to consider your specific business requirements and objectives when selecting the right calculation method for you. 

If recency is more important for your business, maybe you’re a restaurant owner; for example, concatenation or addition may be suitable for you. If all individual scores are equally important, you may consider the weighted average method. It all depends on what your company’s goals and values are, so taking your own business objectives into consideration is crucial. 

Test and validate 

Another way that you can select the right method for your business is through trial and error. You can test and validate the chosen RFM score calculation method using historical data or by comparing it with other performance metrics to ensure it’s beneficial to your business. 

At the end of the day, calculating RFM scores is key to unlocking customer loyalty and growth as they will help you get a better understanding and perspective of your best customers that are the most valuable to your business, which are likely to reengage, and which are likely to churn.

You can use RFM scores to develop your own strategies or partner with a retention platform like Patch which is redefining how ecommerce companies interact with customers.

Partner with Patch to streamline retention

With over a decade of experience, Patch is the world's first retention platform with an integrated RFM segmentation model.

We’ve paved the way to customer retention for businesses like yours and made it easy for you to create a personalized, automated journey for every one of your customers.

You can streamline your retention efforts with Patch’s cutting-edge platform through various integrations like a customizable loyalty program, comprehensive analytics, and access to industry-leading hands-on support to help make sense of it all. 

With Patch as your trusted partner, you can analyze your buyer's journey, identify your best customers, and construct an eCommerce marketing strategy that fosters lifelong loyalty.

Schedule a demo with Patch today to discover how RFM segmentation can benefit your business and enhance customer lifetime value.