RFM Analysis: What It Is and How to Manage It

Date: 2023-04-27 | Time of reading: 10 minutes (1830 words)

RFM Analysis: What It Is and How to Do It

There are two main ways to work with customers: attracting new ones and communicating with existing ones, motivating them to make repeat purchases. The second option is cheaper, but it is important to understand how to communicate with the customer base to generate more revenue.

This is where RFM analysis comes in. It evaluates users based on the sum, frequency, and recency of purchases. As a result, marketers can understand which category of customers to make individual offers to, which customers to show additional products to, and which customers to reactivate. This simple method is used to find the most profitable customers and those who are at risk.

In this article, we will look at what RFM analysis is, its advantages and disadvantages, how to conduct it manually and with the help of technical tools.

What is RFM analysis and why to manage it

RFM analysis is a customer segmentation technique based on their purchasing behavior. The goal is to identify the most valuable customers who bring the company the most revenue. Typically these are loyal customers.

The database is divided into three categories based on:

  • Recency: the time since the last purchase. Different products may have different recency thresholds. For example, if a customer last bought your brand of shampoo six months ago, it is significant because shampoo is a consumable product. However, buying winter tires for a car one year ago is not considered an old purchase because tires are typically used for more than one season.

  • Frequency: how often a customer makes purchases.

  • Monetary value: how much a customer spends on the company's products.

RFM analysis is reuired to develop an action plan and communication strategy for each user category.

RFM is compared to the Pareto principle, where 20% of efforts give 80% of results: loyal customers bring in more money than new and casual customers, although marketing budget is also spent on them. Therefore, customer analysis is important for cleaning up the database and redistributing resources to avoid wasting efforts.

Additionally, RFM segmentation is useful for planning future advertising campaigns, specifically for:

  • personalizing offers;

  • reminding customers about a new purchase if the previous one was a long time ago;

  • presenting additional products when a customer orders at a low price point.

This way, the company spends less on increasing its customer base, as the existing audience is profitable.

Pros and Cons of RFM Analysis


  • Opportunity to gather data for predicting customer behavior. If you know that a group of customers makes purchases every 2-3 weeks with a low average check, you can remind them of a new purchase when the time comes.

  • RFM analysis is a helper for personalization. If a customer does not make high-check purchases, there is no need to offer them expensive products.

  • Savings in marketing budget. Attracting and warming up new leads is expensive, while working with existing ones reduces costs.

  • Selection of an audience for advertising campaigns. Based on historical RFM data, marketers can predict which offers a customer will respond to and which ones they will ignore. If a user only chooses luxury segment goods, showing them advertising of a low-cost product is not effective.

  • Easy analysis, which does not require special efforts and skills. Only time is needed for data collection and processing, if you do segmentation manually. RFM automation tools make the process faster.


  • RFM analysis of the customer database does not take into account the context of purchases (such as seasonality) and customer characteristics (such as demographics). Such data is also important for offering products.

  • RFM segmentation will not bring results when there is a small amount of initial data. The database for analysis should be at least ten thousand people.

  • It is not suitable for companies that sell large and expensive products. For example, real estate is usually purchased once or twice in a lifetime, so customers are unlikely to return for a new apartment after a year.

  • Data has to be updated and the analysis repeated, as customers can move from one segment to another. The standard period is once every six months, but if there are rapid changes in sales, it is worth doing it once every two months.

How to do RFM analysis

Step 1. Collect the database

Data can be accumulated in Excel, Google Sheets, or marketing management systems. The database for analysis should contain customer names, contacts, information on frequency, recency, and the amount of all purchases made by that person.

Step 2. Indicate the scale for segmentation

Each of the three RFM criteria is ranked on a scale from 1 to 3 (a standard variant). Before the analysis, a description for the ratings should be made. An example is given in the table.

CriterionDescription of the scale
Recency1 – the client made a purchase long time ago; 2 – average recency; 3 – a recent purchase
Frequency1 – buys seldom; 2 – average frequency of purchases; 3 – buys often
Monetary value1 – insignificant amount of money; 2 – average amount; 3 – expensive purchases

Sometimes criteria can be further detailed. For example, for the description "the client made a purchase a long time ago", it is convenient to add a specific time frame, such as "1-2 years since the last purchase", and so on for the others.

The scale from 1 to 3 is considered standard, but it can be expanded to 10. However, this will increase the number of segments, and such an RFM analysis will take more time.

Step 3. Evaluate each customer by RFM

This is the most time-consuming step if the analysis is done manually. Using the chosen scale, assign scores to each user based on recency, frequency and monetary value.

As a result, each customer receives a three-digit score. For example, a customer with an RFM score of 221 has made purchases relatively recently, occasionally returns to make purchases, but spends very little.

Here’s an example of the table for evaluation.

Client A112A customer who buys seldom and with long intervals between orders. Brings an average amount of money.
Client B221This customer purchases with average frequency and without particularly long gaps. They only choose inexpensive items.
Client C333It’s the most profitable customer who makes high-cost purchases frequently and with a short time lapse between them.
Client D132They haven't made purchases in a while, although previously ordered frequently with a decent average order value.

Step 4. Divide segments into groups

With a scale of 1 to 3, there can be 27 segments. Similar ones should be combined into a single one to simplify the work.

Possible groups of customers:

  • Most profitable. They buy a lot and often, are loyal and with a sufficient LTV. The highest score in the category is 333.

  • Regular customers with average performance. They do not always buy often and a lot, but they are loyal to the brand and still come back. The highest score in the category is 222.

  • Risk group. People who bought only once or order very rarely and spend little. The most unprofitable customer will receive a score of 111.

Step 5. Make a decision

After evaluating and classifying customers, marketers decide which approach to take for each group.

For example, the most profitable customers can be offered exclusive loyalty conditions (333, 332, and so on). An activation email campaign can be sent to users who haven't made a purchase for a long time (111, 112, and others). For regular customers with a small average purchase amount (such as 321), additional products and conditions can be selected to encourage them to buy more.

RFM-analysis in Altcraft Platform

Altcraft Platform is a platform that automates RFM analysis and collects data on the context of purchases. The tool collects information on the frequency, recency and monetary value of orders, as well as on the activity of customers across different channels (SMS, email, push notifications, and others). Additional data on the opening of promotional mailings, actions in applications and on websites, and participation in loyalty programs will indicate what led the customer to make a purchase and what hindered it.

The purchase amount for analysis is tracked on external resources: websites, social networks, applications, CRM. Altcraft Platform pixels record the completion of goals (for example, making a purchase at a certain value), which are configured inside the platform. The monetary value selection condition for the RFM analysis scale is also set here: total goal value is less than/greater than/equal to a certain value N.

Configuring based on monetary value criterionConfiguring based on monetary value criterion

The frequency of actions and the duration between them are tracked. To do this, goal completion parameters are set. For example, purchases that occurred at least once within twenty days are taken into account in the analysis.

Configuring based on frequency of actionConfiguring based on frequency of action

Next, segments are created based on the data. Customers are assigned to a particular segment based on the following criteria:

  • whether the user currently meets the selection criteria now or met the criteria in the past;

  • how many times during a period the customer met the specified criteria.

Configuring the segmentsConfiguring the segments


RFM analysis is used to evaluate customers based on recency, frequency, and monetary value. The most profitable customers are selected from the database and offered exclusive deals, while special promotions are offered to less important customers to increase their average order value. Passive customers are usually reactivated.

The main advantages of RFM analysis are increased revenue from existing customers and cost savings in marketing budgets (it's cheaper to work with existing customers than to attract new ones). However, this method is suitable for companies with a sufficiently large customer base and those whose customers make regular purchases.

Customers are evaluated on a scale from 1 to 3, where 1 is the worst score and 3 is a high score. The resulting segments are then divided into groups: the most profitable customers, less important customers, and the risk group. A strategy is developed for each group.

The analysis can be done manually (using Excel or Google Sheets) or through marketing automation tools (such as Altcraft Platform), which speed up and simplify RFM segmentation.







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Author: Lyudmila Kovalenko






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