If you’re an eCommerce marketer or have your own online business, then you are probably constantly focusing on ways to improve and grow. Understanding the meaning of RFM is essential for your online store customer retention and can help improve customer lifetime value.
If this is the case and you’re still not using the RFM model, then you’re missing out on a great growth opportunity. It’s easier to implement than you think and definitely worthwhile.
In this article I’ll explain what you need to know about this model, starting with the RFM meaning, its benefits, and moving towards customer segmentation examples that you can start using right now.
So what is RFM?
RFM stands for Recency, Frequency, and Monetary value. RFM represents a method used for measuring customer value.
The RFM model implies analyzing past transactional data and using that research to identify different segments of customers based on their purchase history.
RFM specialists usually use it in database marketing and direct marketing, but lately RFM Analysis received big attention and is widely used in eCommerce.
That’s why the main benefit of an RFM analysis consists of being able to address each segment separately, according to what you know about them based on their score on each of Recency, Frequency, and Monetary value indicators.
What are Recency, Frequency and Monetary?
Now that we know that RFM is a model that uses the combined power of three important metrics, let’s see what each of these metrics means.
Recency – How recent was a customer’s latest purchase from you?
Questions to keep in mind: What is the lifecycle of a customer in your industry? Whatever happened to those customers who haven’t made any transactions in a long time? Is there any way to get them to come back and place an order?
Frequency – How often does a customer purchase from you?
Questions to keep in mind: What is the desired frequency rate in your industry? How about your business? Are there things that you can do to increase the frequency in some customers?
Monetary – How much does a customer usually spend?
Questions to keep in mind: What is your Average Order Value? What are the biggest transactions you’ve had so far?
We usually look at purchases but the RFM modeling applies to other kinds of conversions, as well: app use, subscriptions, and so on.
What is the RFM analysis?
After you’ve identified the three metrics and listed some questions they might provide the answer for, it’s time to learn how to do the RFM analysis.
An RFM analysis can show you who are the most valuable customers for your business. The ones who buy most frequently, most often, and spend the most.
And it all starts with proper segmentation.
That’s why the first thing you have to do is to group your customers into different behavior and purchase patterns based on the RFM variables. You can create customer clusters and even separate them in several tiers:
Frequency: very frequent buyers, medium frequency buyers, one transaction only buyers.
Recency: most recent customers, medium recency customers, least recent customers
Monetary: customers who spend the most, above-average monetary value, average, low monetary value
Combining the above segments you’ll get more advanced ones, such as:
- Clients who come back frequently but spend very little (high frequency, low monetary, maybe deal hunters)
- Clients who only ordered once but spent above average (they could help you identify pain points that prevented them from ordering again and their higher than average monetary value is worth digging for insights)
- VIP clients (those who have a high RFM score overall, especially the monetary value, and who bring your business the most revenue)
- Clients who used to have high frequency and monetary value but have stopped ordering from you, so they have low recency (a sign that they might have switched to the competition although they had been loyal to you, and it’s worth finding out why
RFM modeling can shed light on some potential pain points related to your brand, products, or shopping experience.
Let’s take the pet shop industry as an example because it’s the cutest industry of them all.
Let’s say you sell cat food and you just ran a big discount campaign. Many of your customers might have taken advantage of the low price and stocked up with treats at that low price to last their pets for longer than usual.
In this case, you’ll see a general decrease in order recency in the deal hunters segment.
Also, you can survey customers with low frequency and recency to find out what would make them come back. Is their cat’s favorite food out of stock? Did the order arrive later than expected? Were there any other problems?
You could also analyze how a customer goes from a low monetary value score to a high monetary value one. Was it the effect of those cat toys recommendations that you added in your newsletter as a cross-sell?
Could you target other low monetary value customers with the same offer and give them a reason to make their other cat related purchases on your store?
There’s a lot to learn from a proper RFM analysis. But you also have to act based on what you learn.
The recency frequency monetary model can help you take segmentation to a whole new level.
Through an RFM customer segmentation analysis you’ll be able to see:
- Who are those 1% of customers that bring most of your revenue
- Which of the loyal customers return the most often
- Who are the customers with big monetary value who placed big orders in the past, but have a low recency score, meaning they haven’t ordered in a long time
Based on these segments you’ll be able to ask yourself questions such as:
- How can I make the high monetary value but low recency customers come back?
- What makes the high recency and frequency customers be so loyal? What is it they appreciate in my products or services?
- What other traits (geographical, demographical, behavioral) do my high monetary value customers have in common? How can I use those to create a segment of potential new customers and address them accordingly?
With better RFM segmentation, you’ll be able to address certain segments in a personalized manner, based on their needs and preferences.
Also, depending on your industry, you might be able to notice and use behavior and consumer patterns to predict the needs of your existing customers.
Let’s go back to the pet shop example, to make things a bit more cute and clear.
If you get a new customer who ordered different types of cat food for juniors, he might be someone who just got a young cat and is in the phase of testing foods to see which ones the pet prefers.
You could target that segment of customers with content about how to introduce new foods to a cat or create a bundle of cat food samples that they can buy and try.
By checking the frequency score and monetary value score of a customer you might be able to tell whether he owns only one cat or several, and create some personalized offers for those buying in bulk.
And you also know that in the case of a customer with high monetary value and frequency who has stopped ordering (meaning he has been buying cat food from your store regularly for a long time but has low recency) it might mean that his cat might have ran away or died. Which brings up a few questions:
- How long does it usually take for runaway cats to come back? Do they come back pregnant, meaning that their owner might need junior cat food soon?
- Or, if the cat went to cat heaven, how long does it usually take before your customer moves on and gets a new baby cat?
If your customer is one of your VIPs (based on their RFM score), it might be a good idea to send him a survey (followed by a condolence card if that’s the case) to ask what the problem was and let him know you’re there for him with some suggestions of cat adoption organizations and some junior cat food for his new pet, whenever he decides to get a new one.
Why does RFM matter? Why is RFM important?
As a marketing manager or e-commerce owner you might find yourself obsessing over so many little details about the new clients you acquire in order to optimize targeting and costs.
You might find yourself looking at the age, sex, average income, purchase behavior, and so much other information, to make sure you hit the right target with the right proposition.
But how about the customers who have already been through this process and you already invested lots of time, money, and effort in acquiring them?
Why let them walk out the door that easily? Especially since the ice has already been broken – they’ve given you their trust at least once, and some of them have brought you important revenue.
It’s like never going to a second date and build a long-lasting relationship with someone who already liked you because you’re busy persuading other potential first dates only.
I’ve worked for companies where most efforts were directed towards the cheapest cost of acquisition they could get.
Some would then move to optimizing ads, landing pages, UX, but completely ignoring an existing customer or visitor, even if there was room for a lot of personalization, cross-selling, upselling, and of course, repeated transactions.
But most of all, there was room for creating a stronger bond with those existing customers and making them loyal.
Thinking of all the opportunities going to waste makes me cringe even now.
What can RFM do for me?
The RFM model’s main benefit is a potential for optimization in what you already have in place – current customers, UX, customer service, product, and thus hitting the target instead of wasting resources.
This means that it can save you a big part of the money, time, and effort that you put in acquisition, only to see those one time customers go and never return.
Businesses that use RFM marketing can apply the insights they get from their RFM analysis and increase their retention rate.
And retention is up to 6-7 times cheaper than the acquisition of new customers, as well as a good way to maximize the longevity of your business.
Plus, increasing your retention rate by 5% can lead to a 25% – 95% increase in profit, according to this study by Frederick Reichheld of Bain & Company.
So basically RFM is a great way of knowing your customers better by analyzing their transactional behavior.
If you’re always wondering “what’s my best segment”, then an analysis of your top-rated customers based on their recency, frequency, and monetary score will give you the answer.
If you use it to its true potential, and RFM based research can give you a lot of insight into your business and what the obstacles and opportunities are for you.
It can show you what you should focus on fixing, what you should be investing in, how you can cross the chasm and scale, how you should be addressing different segments, and how well your business rates for loyalty and top of mind.
RFM Score formula. RFM Calculations Simplified
The RFM Score formula is a relatively simple one. It’s based on giving a score to each customer for each of the three variables, based on their transactional history. You can use a scale from 1 to 5 or from 1 to 10.
For example, on a scale from 1 to 10, you can assign a customer whose last purchase on your website was less than 48h ago a score of 10, the maximum, on RFM Recency.
Then, decrease the RFM scoring for Recency as the time elapsed since the latest customer order increases.
At the other end you will assign a value of 1 to your customers who haven’t returned in a very long time (6 months, a year, depending on several details such as your industry and the customer lifetime cycle).
The same goes for RFM frequency and RFM monetary value, where you can give the highest score to your customers who order most often and spend the most, and create a few lower score segments for the rest.
The final step of your RFM calculations is to decide whether you give each of the three RFM variables the same weight. This also depends on your business.
If you sell mobile phones, for example, you can expect your customers to have relatively high monetary values and low frequency scores, because people don’t buy new phones as often as they buy cat food.
That’s why you might want to give frequency less importance by coming up with an RFM score formula that considers monetary value to a higher extent, followed by recency, and keep frequency as a smaller piece of the pie.
However, if you decide to give recency, frequency, monetary value each the same importance, then it’s simple: the RFM score for each specific customer will be the average of their score for each variable.
For example, with this RFM tehnique, a customer with a score of 7 for Recency, 3 for Frequency, and 5 for Monetary value will have an RFM score of 5.
Software for RFM Analysis
If you want to dig deeper into your consumers’ behaviour, you don’t need an RFM specialist, you can use an RFM software to make the most out of your transactional data.
Reveal by Omniconvert – a complex customer intelligence platform that helps you understand your customers’ behaviour, segment them, and nurture all the potential by personalizing their experience.
REVEAL is your go to solution in understanding your eCommerce customers
Our RFM tool delivers automated insights into your customers’ buying behavior. You will no longer need to invest resources and time to dig into your data because once installed, REVEAL will be able to give the most important Customer Retention metrics:
- RFM segmentation
- NPS Score Monitoring
- Buying Behaviour
- Ongoing Personalization
Empower your Growth Team with unified insights about your customers behaviour!
To sum it up, the RFM model is a great opportunity to see better results in your online store.
By taking action based on the RFM analysis you can create such a level of personalization that will make an impact and generate conversions for much less time, money, and effort than it takes for acquiring new traffic and new conversions.
Frequently asked questions
RFM is the acronym for Recency, Frequency, and Monetary Value. These factors help companies understand customer behavior and segment customers by calculating an RFM score for each customer in their database. Based on this segmentation, companies can create different retention strategies according to the particularities of each customer group.
RFM model is frequently used by marketers to generate an accurate representation of the customer distribution. They attribute a score for the three factors that are at the base of RFM analysis: Recency, Frequency, and Monetary Value. After the total RFM is calculated, marketers get a better image of how healthy the customer base is and what they need to do to generate more loyal and valuable customers.
To calculate the RFM score, you need a scale that sets the lowest and the highest value you can give a customer for Recency, Frequency, and Monetary Value. If you’re using a scale from 1 to 5, the latter being the highest, then the ideal RFM score is 555. But all scores that reflect high recency, frequency, and monetary value are an indicator of good customer retention strategies.
RFM analysis uses first-party data to help you generate an accurate representation of your customer distribution. First, you need to define the lowest to the highest value a customer can receive (it could be a scale from 1 to 5) for her/his recency, frequency, and monetary value. Then, depending on their total score, you can start distributing customers in different custom or predefined segments. Analyzing segments and distribution helps you identify the most valuable customers your company has and gives you insights on what marketing tactics you should apply for each group to increase the overall score.
Recency is one of the three factors in the RFM model that reflects how recently a customer ordered from your company. To calculate recency, you need a scale that sets the maximum and minimum values you can attribute based on the customer’s buying history. You can have a maximum score of 5 for customers that purchased within the last month and a minimum score of 1 for those who purchased more than 12 months ago.