RFM Analysis: Explanation & Tips for Customer Segmentation

When working with a customer database, how can you find the data of those who buy regularly? How do you track when a loyal customer makes orders less or more rarely, and how do you correct the situation? These challenges can be solved by RFM analysis.

RFM analysis is suitable for better understanding customer needs, offering them what they really want, developing effective marketing strategies, increasing their coverage and ROI, and segmenting customers. In this article, we show you what RFM analysis is, how to create and customize segments for mailings based on the RFM model, and how you can use it to increase conversion into orders.

What is RFM Analysis?

So, what is RFM? RFM (Recency, Frequency, Monetary) analysis is a method of customer segmentation. RFM meaning is to divide customers into non-intersecting groups, as well as to understand who among customers buys a lot and often, who buys a lot but rarely, and who hasn't bought anything for a long time.

RFM segmentation is important for effective interaction with customers, increasing ROI and LTV. Based on the results of the RFM analysis, you will know which ads to show, which email marketing to plan and which bonuses to offer.

For example, you can send letters to loyal customers with information about new products and services of the company and give motivating discounts to customers who haven't made orders for a long time.

RFM analysis is based on three indicators:

• Purchase recency. It shows the time that has passed since the last transaction. A customer who recently ordered from you is more likely to read an email or answer a manager's call than a customer who ordered over a year ago.
• Frequency of purchase. It shows how often a customer makes a purchase, applies for a service, or performs other targeted actions in a certain amount of time.
• Check amount (monetary). It shows how much money, time, or other resources a client spent over a specific period. The check amount is less predictive of consumer trends when compared to the length of time and frequency of purchases. Often customers who order large amounts are busy people, so they rarely respond to calls and promotional mailings.

Benefits of RFM Analysis

A business that divides customers into different segments, optimizes advertising costs, and successfully launches new products has better contact with consumers because it knows how and which tools to use.

The key benefits of RFM modeling are:

• Personalization. It's important not only to increase your reach, but also to improve the quality of your work with your existing audience. RFM analysis focuses on determining customer needs — based on that, you'll develop truly compelling, personalized offers.
• Reduced advertising costs. You can generate leads even with minimal investment if you optimize your marketing strategies, e.g., by developing separate scripts for sales and targeting.
• Automation. Segmentation with RFM analysis takes about 20-25 minutes, even if the customer base includes 50,000-70,000 contacts. All you need is a few simple formulas and raw data.
• Improved conversion rates. RFM helps you multiply and retain your existing customers and stop them from leaving for your competitors.
• Versatility. RFM analysis can be combined with other tools for working with your customers.

The Role of RFM Analysis in a Customer Retention Process

The RFM in marketing is suitable for e-commerce, direct sales, and non-commercial interactions. It is used if the need for the product is not one-time but recurring and therefore satisfied customers make purchases on a regular basis. In particular, this type of customer analysis allows us to answer a number of essential questions:

• Who are your best customers?

The results of the RFM scoring will show the most loyal group of customers, which brings the most profit. The company can adjust the assortment or the advertising display for this group.

• Who has the potential to become valuable customers?

After the analysis, you get segments of new customers and those who buy frequently but a little bit at a time. To move these customers to the group of regulars, the company develops attractive offers for them, such as a loyalty program.

• Which of your customers could contribute to your churn rate?

If the company divides customers into groups and studies their needs and interests through RFM analysis, it can create helpful and interesting content for those people. The content will generate a response and lead to the target action: make a purchase, register for a personal account, or subscribe to the newsletter.

• Which of your customers can be retained?

Customers who have lost activity are likely to leave. But if the company conducts analysis and discovers such customers in time, it can take measures to retain them.

How to Implement RFM Analysis: A Step-by-Step Guide

Now you know what RFM stands for and other important info apart from the definition. Let's get down to use cases and explore what RFM segmentation is and how to perform it.

Collection and Collation of relevant data/values

Start with the collection of data and determine the analysis period. There is no strict framework, but it all depends on the organization's characteristics (including the direction of the work — B2B or B2C). Usually, you have to gather RFM data for the last year.

There are some obligatory requirements for the source data. They should contain the following information:

• Customer ID (ID, phone number or order number, full name, email, etc.);
• Date of the transaction;
• Number of purchases made (or other targeted actions);
• Amount of money spent.

If you are going to create a table manually, it will take much time. To speed up the process, use a CRM or analytics system that automates the exporting of the necessary information.

Setting the RFM scales

Create groups based on time of purchase (R), frequency of purchases (F), and money spent (M). Each group is assigned a value from 1 to 5, where 1 is the best and 5 is the worst. 

• R (Recency) — grouping by date of last purchase

We estimate the date of purchase, select the group of customers with the highest Recency, and assign it position 1. The rest are evenly split into segments, depending on activity.

A scale of 1 to 5 separates new and loyal customers from those who are almost lost. Customers from segments 1 and 2 will always be fewer than those from 3, 4, and 5.

For example:

• 360 days and more — 5;
• from 180 days to 360 days — 4;
• from 90 to 180 days — 3;
• from 30 to 90 days — 2;
• up to 30 days — 1.

• F (Frequency) — grouping by frequency of purchase

We determine how many purchases we can consider profitable for the company. 

For example:

• 1 — 5;
• 1 to 3 — 4;
• 3 to 5 — 3;
• 5 to 10 — 2;
• more than 10 — 1;

• M (Monetary) — grouping by the number of purchases.

Customers who spend the most money are divided into a separate RFM segment. They play an important role, even if they are not regular customers.

For example:

• up to $1,000 — 5;
• from $1,000 to $3,000 — 4;
• From $3,000 to $5,000 — 3;
• From $5,000 to $10,000 — 2;
• from $10,000 — 1.

Assigning an RFM score

Then we number clients by each parameter. For example, a customer made two purchases 40 days ago for $5,000. In this case, his value will be 243.

Based on these values, the so-called "triples" are formed, i.e., values 111, 333, 232, and so on. For example, "333" — is a recent customer who rarely shops and spends an average budget. This customer isn't bad, but customers from value 111 would still be the best, since they constantly come and invest more money in the product. 

Labeling segments

You can further divide your customers into specific groups:

• Lost. Long-time customers who have made only one purchase — 555, 554, 553.
• At the risk of loss. Long-time customers who have made more than one purchase — 535, 433, 432.
• Previously loyal. Consumers who used to buy frequently on different checks but stopped over time — 131, 132, 133.
• Newcomers. Customers who have recently done a transaction — 153, 154, 155.
• Promising. New customers with a big check — 151. Regular customers with a big check — 131. This same group can include consumers who frequently order on an average check — 132. 
• Ideal. Customers who order a lot and often — 111. 

Creating customized strategies/tactics for relevant segments

Based on the information in the table, you can develop a strategy for each group separately.

Lost

You can notify them about current promotions, sales, and discounts. However, you shouldn't put a lot of effort and time into returning your long-time customers, as the outflow is inevitable. If there is no response, remove them from the database.

At the risk of loss

This group deserves attention because previously these people have bought many times for large sums. To get customers back, you should offer a personal selection of products, discounts, and bonuses, and inform them about the sale. Get them interested in useful content (an article or video). Find out the reason customers stopped buying.

Previously loyal

It is recommended to remind them of the benefits of the company, the purchase, and the product/service. Tell them about your fortes that your competitors don't have.

Newcomers

It's important to move customers from newcomers to loyal. Offer educational text or video content, advise on products (how to choose, what to look for), provide background information, congratulate them on their purchase, etc.

Promising

Consumers who regularly buy for a small amount or have recently bought for a large amount may become ideal clients in the long run. Your job is to hold their interest and increase user engagement.

Ideal

It is essential to convince this segment of their importance to the company. Ask your ideal customers to leave a review and offer a form of personalized service. Encourage customers to order regularly, and provide customized product selections. Don't be overly intrusive, as unnecessary communication is tiring. Give only helpful information so as not to annoy people.

To Sum Up

RFM segmentation is a great tool for a company's marketing analytics, with the help of which you can separate your audience and achieve effective sales. Above, we looked at how to work with it and when it's best to use it. Finally, we'd like to add that RFM analysis is much more convenient to carry out with the help of special automated tools such as Reteno. Reteno is a no-code platform for gathering information and setting up marketing campaigns. The platform allows marketers to take full control of customer-centric communication and avoid routine. In Reteno, RFM is created automatically based on the activity of contacts. Contact us now, and we will make your marketing truly effective.