The RFM Model has been in use since 1970 for direct sales and mailing. Yet, it’s still one of the least explored – and most effective! – segmentation strategies.

Given that acquiring a new customer costs almost 7 times more than retaining one and that RFM segments help you understand your customers’ behavior and reasons to buy, not using this model is a missed opportunity for online stores.

While some years ago, the argument for not doing RFM modeling was the amount of manual work involved in such an RFM analysis, today, there’s really no excuse left!

RFM tools like REVEAL are doing the RFM segmentation and analysis automatically for you, so all you have to do is connect your webshop data to our software and dive into your customer insights!

What is RFM?

RFM stands for Recency, Frequency, and Monetary value, three variables used for behavioral segmentation. Based on RFM variables or RFM metrics, companies can segment their customer base, identify their best and high potential customers, create better experiences and offers to keep them close and happy.

All the information you need for RFM segmentation is already in your historical transactional data. With RFM, you can make sense of all the data you’re automatically collect as customers make new purchases and have a clear overview of how your customers are distributed. 

You’ll be able to distinguish loyal and happy customers from those who used to be loyal but are about to churn, or differentiate newly acquired customers with high purchase values from one-time customers who buy one and never turn back.

What is the RFM model?

The RFM model is a behavioral segmentation method that allows you to segment and analyze customers based on three variables in your historical data: Recency (R), frequency (F), and monetary value (M).

  • Recency shows you how recently a customer bought from your store. 
  • Frequency reflects how often a customer purchases from your brand.
  • The monetary value represents how much a customer usually spends with your store.

To segment your customers based on the RFM model, you need to set a scale for each variable. Depending on your store’s size, you will use one of the scales below:

  • 1 to 5 scale if you have more than 200k customers;
  • 1 to 4 scale if your store has 30k – 200k customers;
  • 1 to 3 scale if you have below 30k customers.

For example, if your store has over 200k customers and you want to know who your top customers are, you would have to look at those with the maximum RFM score, respectively 555. They are the biggest spenders, with the highest Recency and frequency. You want to keep them close and loyal.

What is an RFM model analysis?

By definition, the term RFM stands for Recency, Frequency, and Monetary Value.

It focuses on the lifetime value of customers, and it’s the preferred customer segmentation methodology for eCommerce businesses that focus on retention strategies more than on client acquisition.

The typical approach to RFM modeling is collecting large amounts of transactional data and, based on it, segmenting customers into specific groups as per their purchase history.

Each customer group is then addressed separately according to their own needs and behavior patterns.

The best part of the RFM model is that it helps you focus on customers with high recency and frequency scores. These clients are critical because they are likely to return and buy again.

If you’re serious about defining your ideal customer profile and buyer persona, you need to segment your customers based on RFM. Why?

You might find out that 1% of your customers generates up to 30 times more revenue. And that only 20% of your clients return after the first order.

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Pareto principle in RFM model

If we apply the Pareto principle to the RFM model, then 80% of a business’s revenue should be driven by the top 20% of the customers, your best customers.

Does this principle apply in real life? Let’s look at the example below, which captures the distribution of Revenue vs. Margin by RFM group of a subscription-based online store.

revenue vs. margin rfm

Revenue vs. Margin by RFM group as seen in Reveal

The Soulmates represent the customers with a perfect RFM score. In this company’s case, the share of Soulmates is 21%, and they bring 54% of the total revenue. It would be 80% if the company had fewer customers in the Ex-Lovers group, represented by customers who used to be Soulmates.

The Pareto principle applied to the RFM model only emphasizes the importance of attracting and retaining the right customers. If you’re aiming for sustainable growth, you could use the 80:20 principle to guide your future initiatives.

The RFM model’s role in customer retention

There was a time when advertising was everything.

People would spend huge budgets getting attention towards their brand and acquiring new customers. Over and over again.

Wouldn’t you like to have access to such customer insights? To get a clear picture of who these customers are and what makes them come back?

Advertising was everything until terms like “retention,” “loyalty programs,” and “building a relationship” became popular.

We find ourselves in an era where the relationship between consumers and providers is cold. There’s no emotion or commitment between the two parts.

As marketers, we need to shift our attitude from transaction-focused to customer-centric.

We’ve seen many online businesses focusing on reducing their Customer Acquisition Cost (CAC). They disregard retention entirely, which is similar to cold-calling and expecting everyone to buy at the first interaction.

That rarely happens, so focusing on acquiring the average customers instead of acquiring those who are more likely to become repeat customers sabotages your business’ long-term growth.

The RFM model helps you get the most out of your retention and acquisition efforts. When customer retention optimization is your main goal, the RFM model helps you:

  • Find who are your loyal, repeat customers;
  • Analyze key performance indications among all customer segments to understand where your company is compared to your customer retention goals; 
  • Perform qualitative analysis on your top customers so you can give them the best treatment as part of a loyalty program;
  • Create different approaches for your active, high-potential, and at-risk customers;
  • Become better at attracting new customers that resemble your current top customers.

Screenshot from RJMetrics E-commerce Growth Report 2015

I’ve worked with a company that spent 20 times more on getting new customers. They had substantial advertising budgets. Since the focus was on the acquisition, they tried to reduce CPMs and CPAs, but they didn’t do anything about the Customer Lifetime Value or retention rates.


Benefits of the RFM model

One of the biggest advantages the RFM model has is that it gives you clarity based on the data you already have, your customer historical transactional data. 

Once you start using RFM for your business, you can:

  • Better understand customer behavior;
  • Identify how customers are distributed among the RFM segments;
  • Analyze customer segments and gather insights on what your priorities should be;
  • Create better customer experiences and messaging for your marketing campaigns;
  • Support your paid efforts with better custom audiences and lookalike audiences.

When you perform RFM segmentation automatically, it’s easier to stick to one segmentation model that is easily understood and applied by the entire company in various optimization processes. If you’re looking for consistency and traceability for segmentation, RFM is the right segmentation model for you.

Why you should implement an RFM Model

Surely, the acquisition is the main growth driver in the first months or years for an eCommerce website. In this phase, it is essential to let people know you exist on the market and build awareness.

But once you’ve acquired enough customers, you need to start focusing on segmentation and retention through highly personalized customer journeys.

It’s how we helped one of our clients in the fashion industry to increase their retention rate by 6% in only 6 months!

I’ll show you how to use an RFM model and build a strong relationship with your current customers.

The process below is suitable for webshops built on custom platforms. If you’re using Shopify, you can skip the manual work and let REVEAL do all the RFM modeling, segmentation, and analysis for you!

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Step-by-step guide to creating your RFM model

Remember that the following steps are suitable for performing RFM analysis manually. If you want to dedicate less time to performing manual work and more on analyzing the insight that results from an RFM analysis, we strongly recommend an automated RFM segmentation solution that suits your company’s needs.

Step 1. Tracking

First of all, keep in mind that you need to find logic when you try something new for your business. I recommend building a database containing the history of the customer purchases from the past 3 years.

From a first look at the customer data, you will observe some patterns. Analyzing a customer database, I observed that customers bought once but never purchased again from the website. On the other hand, we had customers that purchased every time we launched a new product.

The first significant insight was that these customers had different habits, and we needed to approach them differently.

When you have the database, you need to select criteria that will help segment the customer database and give scores. The most important metrics that you should consider are:

  • Revenue (1)
  • Number of orders (2)
  • Average Order Value (3)
  • Last Order Date (4)
  • Last order in days (5)
rfm analysis

Right after deciding what you need to track, you have to flag all of the previous purchases according to:

  • Recency – the time when they last placed an order;
  • Frequency – how many orders they have placed in the given period;
  • Monetary value – how many dollars they spent since their first purchase (Customer Lifetime Value).

Step 2. RFM segmentation

The second step in building the RFM Model for your eCommerce site is defining the categories of customers. You will have to define them based on Recency, frequency, and monetary value.

Use the RFM model to discover the most important clients. I am sure that you don’t want to let clients with high monetary value and high frequency fly away.

To decrease the churn rate, you have to understand who they are and the underlying factors that drive their purchase intentions.

Another segment you can look into includes the clients that used to be your best customers. Of course, the risk is to get lost in the data and create too many groups.

> Discover the 11 RFM segments according to the CVO process.

Acquiring valuable customers isn’t a hit-and-miss incident. It’s a deliberate, intentional process.

One way to have even more loyal clients is to continuously launch NPS surveys to check the pulse of all your customers. The Net Promoter Score allows you to determine what customers think about you.

Further on, develop a strategy based on the NPS segmentation: promoters, passives, and detractors. For example, you can send a special offer to your passive ones and transform them into advocates.


Let’s say your company has over 200K customers. List all the purchases from the most recent to the least recent and divide the time equally. You will get five segments:

  1. Old
  2. Lost
  3. Potential
  4. Regular
  5. New
RFM Model

Then, assign numbers to each customer: 1, 2, 3, 4, 5.

This is an example with four-time intervals. That’s why you see only max. 4 points assigned to Recency.

In this phase, you should have significant insights into your customers. For instance, I once discovered that users who bought more recently were more inclined to open our newsletters and visit the website. They were more engaged.

You have to repeat the process for Frequency and Monetary Value as well.


Sort the entire file containing the purchases from the highest number to the lowest number of purchases. Then, divide it into equal parts as you did with the Recency.

For example…

If you have divided the time interval into 5 parts, assign points from 1 to 5 for the frequency.

Pro tip: Create customer surveys triggered to determine how much they are willing to spend on your site.

Here are some questions to ask in customer satisfaction surveys:

  • How much are you willing to spend for [product X]/ [Special event], etc.?
  • What would you like to improve in the products on [your site]?

When you analyze the answers, pay attention to the differences between the users who make recurring purchases and those who purchase only once. If the difference is significant, the frequency is essential to your business.

When I analyzed the results, the difference between the two was not as significant as in the Recency research. Therefore, it was more important for that business to have recent purchases rather than having a good frequency score.

Monetary Value

The third factor in the customer database segmentation using the RFM Model is the monetary value. The monetary value is the overall amount of dollars that your customers have spent in their lifetime.

All you have to do is sort the list from the highest amount of dollars spent on your site to the lowest and divide the database into 4 or 5 equal parts (depending on how you decided to do it). Then attribute points to each part.

After applying these three segmentation criteria, you’ll get a list of three-digit codes representing the RFM score for each customer.

Step 3. Testing

Now that you have segmented the customer database based on Recency, frequency, and monetary value, how do you know which segments are the most profitable?

You test them!

First, you must extract a sample size from the total number of customers (segmented as discussed below) and start a campaign. Choose a sample size of 5% or 10% of the total number of customers included in your database.

170,000 clients, divided by 8,500 (5% of total) equals 20. This means you have to pick every 20th client in the database.

Use this rule if you want to have a representative sample for the entire list.

By testing 5% or 10% of your list, you minimize the risks of launching a promotion campaign that would fail.

If you need help with setting up A/B testing experiments and website overlays for your segments, Omniconvert is the right technology to do it. I know I’m biased, but it’s the only way to do it without installing multiple codes on your site.

Step 4. Analysis

To analyze the results of your campaign, you should measure the Conversion Rate of each segment that has an RFM score attributed. The segments with the highest conversion rate are the ones that will get you the highest amount of money for your business.

When you analyze the results, use this simple procedure that works anytime, for anyone. You don’t need sophisticated tools for this.

First, you need to divide the total costs of the campaign with the total revenue.

If Revenue/Cost is 1 or greater than 1, then it’s profitable to keep investing in that customer.

Draw a line below the row that contains the last value greater than 1.

If it’s below one, it means that it’s ineffective to invest in that type of customer or that you should run more tests to find out what’s making him behave like the ideal (read most profitable) customer. I call this method “waking up the customers that sleep.”

Step 5. Iterating and growing

If you build the RFM model the right way, you will be more persuasive in your communication with your customers. It will most likely convince them to continue as your client. Check out these ideas to increase sales if you feel like you don’t know where to start from.

For example, if your customers are in the “1” category on Recency, you can send them a re-engaging newsletter. This newsletter could mention all the new offers you have on your site, any changes in design, or how the overall experience has been improved since they last visited your site.

If you want to talk to customers in your “5” segment of your Monetary Value, let them know how much they mean to you and your business. Give them special discounts if you can, tell them they’re VIPs, make them know they are appreciated. This will only bring them back to your site and buy more.

Finally, I encourage you to be creative. You can use the RFM model in any way it suits your business. But remember that just doing the RFM analysis and segmentation will not influence your website’s results.

You will have to act based on customer insights and start testing approaches and targeting them in a more emotion-based way. In the end, you’ll get more conversions.

And, of course, the fewer resources you spend on manually implementing the RFM model, the more you can invest in applying the new insights generated through RFM analysis to optimize your marketing processes and campaigns.

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Frequently asked questions about the RFM model

How do you use RFM models?

The Recency – Frequency – Monetary Value model or the RFM model helps you use existing customer data segmentation. Based on the scores you give for each variable, you’ll identify your best and worst customers and have more clarity on which segments you should focus on to improve retention and acquisition.

How does RFM analysis work?

The RFM analysis uses existing transactional data to segment customers and helps you predict which customers are more likely to keep buying from your brand in the future. You need to attribute a score for each RFM variable and use the values you’ve got to calculate the total RFM score.

Is RFM a predictive model?

RFM is a predictive model because it predicts how customers might act based on historical data. Grouping customers into RFM segments gives you more clarity about what to expect from your future interactions, considering the current RFM score of your customers.

How does the RFM model work?

The RFM model assigns a numerical score to each customer based on their recency, frequency, and monetary value. Customers are typically divided into segments or groups using these scores. For example, customers with high scores in all three categories may be classified as “best” or “most valuable,” while those with low scores in all three may be considered “inactive” or “least engaged.”