Over the past few years, we have seen how data has revolutionized the world, especially in sales and marketing.
The high demand for data by business leaders, sales reps, and marketers is largely fueled by the desire to understand customers better and develop the right product for their needs.
For this purpose, marketers conduct an RFM analysis on their customers to analyze their behaviors and segment them into groups where they can create and send targeted messages to each customer.
In this guide, we will explore ways to use recency, frequency, and monetary data from your customers to increase engagement and sales.
Table of Content
- What is RFM analysis?
- How does RFM Analysis work?
- What can RFM analysis do for you?
- Step-by-Step Guide to performing an RFM analysis in Excel?
- RFM analysis for customer segmentation.
What is RFM analysis?
RFM analysis is a data-driven segmentation of customers based on some behavioral and purchasing data. RFM stands for recency, frequency, and monetary value.
It involves using data based on existing customers’ behavior and characteristics to predict how a potential customer will most likely interact with the business in the future.
RFM analysis allows marketers to target specific RFM segments with content and marketing campaigns that are most suitable for these sets of customers to increase their engagement with the brand, loyalty, and customer lifetime value.
RFM analysis is popular for three main reasons;
- It is simple to use.
- It is easy to interpret and understand.
- It uses numerical values to predict high-level depiction of customers.
Marketing teams use RFM models to identify and prioritize predicted high-value customers in their marketing strategy to give them special attention to drive sales.
These models are built using three key factors:
- Recency – How recently a customer has transacted with a brand
- Frequency – How frequently they have engaged with a brand
- Monetary value – How much money they have spent on a company’s products and services
How does RFM Analysis work?
RFM analysis enables marketers to segment their current customers into groups by the frequency and monetary value of their purchases and use the data to identify new customers that are likely to spend the most money.
Before RFM models, marketers manually gathered and applied their customer’s demographics and psychographic data to segment them into groups and predict their likelihood to make a purchase.
Like the traditional lead scoring method, marketers assign point values to predict customer behavior across a large population with the same traits.
However, with more advanced systems and software like CRM software and customer data platforms, a large part of customer research and sales prediction can be automated and help create customer segments much faster.
Marketers now have access to more refined and accurate customer data that they can use to determine their most valuable customers and predict their future customer behavior.
Looking at the functionality of the RFM analysis, we have identified the three quantifiable characteristics that contribute to RFM analysis,
1. Recency value
Recency value focuses on how recent a customer’s last interaction was with a brand. It specifically looks at their last purchase, the last visit to the company’s website, the last time they used a mobile app, liked or commented on a post on social media, or the last transaction they performed.
Recency is one of the key metrics used in the RFM analysis to create an RFM segmentation. It measures how recently a customer interacted with a company via social media, website, or other touchpoints to identify high-value customers.
Marketers use this information to determine or predict the likelihood of a customer to respond to more marketing messages in the future. Recency value believes that the more recent a customer’s purchase was, the more responsive they are to new product promotions.
Marketers ask questions like how much time has elapsed since a customer’s last activity or transaction with the brand to assign a recency score.
2. Frequency value
Frequency value focuses on the number of times a customer made a purchase or interacted with a company within a specific period.
It is an RFM metric that shows how deeply connected or engaged a customer is with your brand. Marketers ask questions like how often or frequently a customer transacted or interacted with the company during a particular period to assign a frequency score.
The frequency metric believes that customers who frequently interact or engage with a brand (repeat customers) are more likely to be more loyal customers than other customers who don’t interact with the company.
Under frequency, one-time customers (customers who make a purchase once) are grouped into different customer segments. Like recency value, Frequency value also aids the creation of RFM segmentation.
3. Monetary Value
Monetary value is the total amount of money a customer has spent purchasing products or services from a company over a particular time.
It is one of the RFM metrics used to identify and create RFM segments for customers who have spent the most money – big spenders, in the past and are more likely to spend more money on products or services in the future.
Aside from this, it also helps marketers to differentiate and target customers based on their monetary value (amount of money they spend or have spent). The idea of the monetary value is that big spenders should be treated differently than customers who spend less.
Essentially, RFM analysis works by measuring the recency, frequency, and monetary value of customers to determine the best customers by their RFM score.
What can RFM analysis do for you?
RFM analysis is a powerful marketing technique used by marketers to research and create customer segments based on the recency, frequency, and monetary value of their transactions to identify the best customers and improve targeted marketing campaigns.
Aside from the above functions, an RFM analysis can help you achieve the following;
1. Identify high-value customers
If you are looking for a means to identify your best customers and target them with tailored campaigns, RFM analysis offers you the best and the easiest approach to achieve this.
High-value customers are customers with the highest lifetime value and are most likely going to add the highest revenue to the company.
RFM analysis finds and analyzes the recency of purchase, frequency of purchase, and monetary value of a customer purchase to determine valuable customers and add them to a segment.
2. Identify the Top 20% and bottom 20% of customers
As we have emphasized in the earlier part of this guide, RFM analysis helps identify high-value customers but, that’s not all there is to them.
RFM analysis also helps you identify your best 20% of customers, including the top 10% who contribute the highest revenue to your company and the bottom 20% who contribute the lowest.
With an RFM model and analysis, marketers can develop a more effective strategy to retain the top customers and encourage the bottom 20% to purchase more products. Check out the related guides, marketing qualified lead and MQL vs SQL.
3. Increase personalization
The only reason you want to identify your best and worst customers is to develop the best strategy to maximize your revenue and increase your customer base.
Marketers can create personalized marketing messages and campaigns that are best suited for each customer segment.
When you know your customer’s buying behavior ( recency frequency monetary value of their purchase) and factors that influence these behaviors, you can send them personalized content and promotions to motivate them to buy more and stay loyal to your company.
4. Improve conversion rates
Conversion rates indicate the success of a marketing campaign. It shows the number of customers that take the desired efforts – download content, like a post or calls to make inquiries.
When your customers get the content, messages, and promotion they care about, they are more likely to engage with it, thereby increasing their conversion rate.
5. Increase revenue and profits
It is pretty obvious that when you create the right content for the right customer segments, you increase their interest and engagement with your products or services to increase your chances of making more sales.
More sales and more customers mean more money for the company.
6. Increase customer loyalty
Good use of the information and insights generated from RFM analysis can help improve customer loyalty.
When the customer gets more promotions of the content they like, they are likely to stay and buy more from the company – making them more loyal to your brand. Find the top differences, gross sales vs net sales.
Step-by-Step Guide to performing an RFM analysis?
Now that you know what you can achieve with RFM analysis, it’s imperative that you also know the steps to performing an RFM analysis.
As a result, we have provided a step-by-step guide to performing an RFM analysis below;
Step 1: Gather relevant data
RFM analysis is a straightforward process. It reviews customers’ purchase records to identify their most recent, frequent purchases and highest monetary value.
Thus, the first step to performing an RFM analysis is to assemble all the RFM data for every customer to determine the recency frequency monetary value of their purchase within a specific period.
In this stage, you are yet to determine the value of a customer – high or low value, so you need to fill in their RFM purchase data in a pivot table to input their RFM values.
Step 2: Set up RFM scales
This second stage involves creating custom-built filters to support RFM customer segmentation.
Consider using custom-built filters such as the amount spent, email subscription status, last order dates, number of orders, product subscription status, products purchased, customer added date, predicted spend tier, etc., to describe your customers’ recency, frequency, and monetary values.
This will give you a deeper understanding of your customers’ RFM values to know which segment they belong to.
Step 3: Assign RFM scores to the customers
In this third stage, you will assign a score to each customer based on the RFM scales and data that you have gathered and created in the first and second steps.
RFM score is a numerical score that helps you to determine the value or rank of your customers, from the best to the worst. The RFM value of each customer ranges from 1 – 5 point score, and you assign these to the customer based on their RFM value within a period.
Step 4: Classify customers into segments
After assigning them a score based on their RFM value, the next thing to do is to classify them into a segment based on how high their RFM score is.
You can classify them into segments like the champions, potential loyalists, recent users, can’t lose them, needs attention, loyal customers, price-sensitive, about to sleep, hibernating, and finally lost customers.
We will discuss more on these segments more in the subsequent paragraphs. Explore the finest guide, inbound vs outbound marketing.
Step 5: Personalize strategies for the customer segments that are most relevant to you
Now that you have classified your customers into RFM segments based on their scores, the next and last step is to create personalized strategies to target and motivate your most relevant customer segment to make more purchases.
You should consider giving your champion customers more access to your products, develop a better customer service strategy for your loyal customers, provide more product awareness and promotion content for your recent customers, and offer freebies and discounts for your At-risk customers.
RFM analysis example
If you are looking to rate one of its customers that purchased your products or services over six months ago, you can do so by checking their purchase record to assign scores for their RFM. On a scale of 1-5, the customer will get 1 point for recency (considering that it’s been a long they purchased), they get 5 points for their purchase frequency if it’s more than five times, and another 5 points if the monetary value is over $40.
Based on the RFM segment score, the above customer can be classified into segments and be targeted for more sales opportunities.
RFM analysis for customer segmentation
Customer segments are important in marketing to help you understand your customer buying behavior and to develop effective strategies to target and motivate them to buy more.
Some of these segments include:
1. Champions: are the best customers in the RFM segments. They are the customers that bought most recently, most often, and are the heavy spenders. They are most likely to accept and buy your new products.
2. Potential Loyalists are recent customers with average purchase frequency and spend a good amount on your products or services.
Consider offering them memberships or loyalty programs to encourage them to become your loyal customers or champions when they spend more on the products.
3. New customers: have an overall high RFM score but it’s hard to tell their purchase frequency just yet. Provide onboarding support and special offers to build a relationship with these new customers.
4. At-risk customers: are customers who purchase frequently and spend big on products but haven’t purchased recently.
Consider sending them personalized reactivation campaigns to offer them renewals and new products to encourage them to make another purchase.
5. Can’t lose them: are customers who used to visit and buy a lot of times but haven’t visited you in a while. Consider sending them surveys to identify the problem with promotional content to bring them back.
The key benefit of RFM analysis is to divide customers into segments based on when they recently made their last purchase, how often they buy products or services, ad the monetary value of every purchase.
It enables marketers to improve their marketing performance by understanding their different customers and sending them the best-suited personalized messages.
With insights gotten from RFM analysis, marketing teams can improve customer engagement and retention to maximize business revenue. Also check out the related guides, B2B sales, SaaS sales and tech sales.