RFM modeling is one of the most popular customer segmentation models used by data-driven marketing experts. RFM has been around for about half a century and is used to predict future customer behavior and measure customer value.
But even though RFM is such an old and popular model, it is still used to analyze data in the modern age and is still being researched and improved to help companies boost their performance. Strangely enough, however, many marketers are unaware of RFM, how it operates, and what it’s good for. The following in-depth guide will explore everything you need to know about RFM Modeling.
RFM analysis is a tool used by businesses to rank and segment their customers by value over a certain period by using the following criteria modes:
All in all, RFM provides you with detailed information about your most valuable customers and the rest.
The abovementioned criteria boxes are crucial for your success, as they correspond to the main points of customer behavior.
Even though there are various ways to rank customers, segmentation is the most potent and well-rounded option.
One of the most advantageous aspects of RFM modeling is that it gives businesses a clear view of customer behavior based on data you already possess — historical transaction data.
RFM has the following advantages:
Automatic RFM segmentation makes it much easier to stick to a single segmentation model that is easy to grasp and use by all employees in multiple optimization processes. So if you’re looking for a solution that can offer both segmentation and deep traceability, RFM modeling is the way to go.
While the acquisition is the main engine that drives growth in the first stages of a business website, in this phase, it is crucial to make yourself visible on the market and build awareness.
As soon as you have enough customers to sustain your business, you need to turn to retention and segmentation via deeply personalized customer journeys. RFM can help your business exponentially increase its retention rate by allowing you to access valuable customer insights you won’t usually have access to.
Thankfully, RFM calculation is easy enough for anyone to do. And while you can opt for a paid RFM model kid, doing it yourself is straightforward.
Below, we have listed four simple steps you need to follow to calculate your RFM:
First, you need to gather the raw transaction data of your business. Pull up every customer’s purchasing history and align them with the abovementioned RFM values:
If you are just starting, you can do RFM modeling in Excel. Alternatively, if you have the experience, you can also use Python, as well as configure any Excel integrations to use RFM data within other tools or for reporting.
Now it’s time to calculate the RFM score of each one of your customers. You can do that by sorting the three data categories and creating four tiers afterward. If you’ve ended up with tons of data, you could create five tiers, or if you have less data, you can create there. The optimal number is four, as it makes the process of manual calculation much simpler.
Sort each RFM value in a way that will show your most desired customer behavior first. For example, in the R-value, the most desired behavior would be recent purchases, which would naturally rank first.
The next step is to segment data into four tiers, which should be the following:
In the case of Recency, you could make an Excel sheet containing R data carefully sorted and segmented into four tiers.
Do this with the F and M values, and when you’ve completed the process, each one of your customers should have all the values respectfully tiered to their profile.
To accurately calculate the combined RFM score, you would need to place the tiered values alongside one another.
With all customer RFM scores in place, you can now split them into groups. This way, you can create a solid targeted marketing strategy. Giving them names would help you make things easier.
With RFM, you could focus on the following:
It’s ultimately up to you how you name your segments — it can be whatever works best for your particular needs.
After segmenting your customers in such a way, you can take the next step and start sending personalized messages that will resonate with your customers, depending on their categories.
For instance, you may want to target a specific group in the following way:
Originally developed for catalog marketing by direct marketers, the RFM model is one of the most potent ways to track and study user behavior.
Catalogs are extremely pricey to design, print, and distribute, and that was the driving force behind the marketers’ push to create a solution that would minimize the costs of sending direct mail and ensure it was getting to the most promising and high-quality customers.
Data scientists would extract precious customer data, calculate the metric value for each person, and assign RFM scores accordingly. Afterward, they would study the correlation between the scores and the likelihood of each customer segment buying the product or service in question. Then, they would create a mailing list with the RFM scores in mind.
By going for the customers with the best RFM scores, you can select those who have a higher probability of making a purchase, so you can avoid sending expensive catalogs to people who won’t buy. Hence, RFM segmentation is best for targeting people who are likely to make a buy and drive profits by not reaching out to those who likely won’t do so. In short, RFM segmentation drives profits by minimizing expenses.
Fortunately, RFM is helpful for all kinds of businesses. Customer segmentation can do wonders for helping you target promotions, elevate customer loyalty and retention, and help you strengthen your marketing performance in general.
With the RFM model, you can get to every customer and win their trust and loyalty. If you play your cards right, RFM can be extremely profitable.
However, like anything else, the model also has its weak points.
Let us first go through the reasons why RFM can be a great option.
Backed by hard data, the RFM system provides its users with precise analytics. The calculations are quantitative, so human bias is out of the equation. More traditional methods of doing customer analyses don’t have this advantage. For example, other segmentation models might unjustly skew towards certain variables like demographics. Also, methods like sampling can mistakenly pick people who do not paint an accurate picture of your representatives.
The RFM method allows you to send out large-scale brand materials that are personalized for each customer group. It helps you put a tighter focus on individual groups instead of sending out generic materials en masse. Personalized marketing is the perfect option if you don’t have much time and money available.
This approach allows for your message to let your customers know that you understand their wants and needs. A warm, personalized message can bring old customers back and incentivize the best ones to make a purchase, creating a loyal customer base in the process.
Market research can be a complicated and costly process. Fortunately, with the RFM model, you can do your research in a simple and low-cost way. Of course, you can also purchase RFM model kits to do the busy work for you — and some of the tools available are quite handy. However, with some practice, RFM calculation can be pretty straightforward, which means that both large and small businesses can adopt it.
Attracting customers is extremely important to your business. But what’s even more important is being able to keep them. Due to high competition, there is an overwhelming amount of options for customers to choose from. And that’s why customer acquisition has taken a backseat to customer retention.
The RFM approach allows you to gain a deeper understanding of who your most valuable customers are and reward their positive behavior accordingly. Naturally, this drives retention up, and people are much more likely to stay when they feel appreciated and happy with your service.
Nothing is perfect, and the RFM model also has its weak points. If used in isolation, the analysis can be inaccurate. Here’s what to be wary of when using this approach:
RFM is not a know-all tool. It relies heavily on three core pillars, unlike other methods that can take advantage of a deeper set of data. Needless to say, by limiting yourself in such a way, you may miss out on crucial data such as location and demographics.
A thoughtful walkaround approach would be to upgrade your RFM system — e.g., with quantitative data research, which can provide you with a clearer view of people’s spending habits. You can also ask your customers for feedback to understand how they came to form their spending habits the way they did.
It can be easy to conclude that your customers will keep on spending as they’ve always done into the future. But that can’t always be the case.
The RFM model only analyzes past customer data, which might not be very useful for predicting future habits and activities. You can get around this by using other tools as sidekicks for your RFM system.
Sadly, the RFM approach can deliver misleading results.
Here are some factors that might produce metrics that may not be useful for certain business models:
When it comes to customer behavior segmentation, Smartico.ai is a trusted and globally recognized leader in the iGaming/Casino/Sports Betting industry and beyond.
Recently, we introduced a segment based on the behavior of the user.
It combines data from both the user profile state and their behavior. The segment is based on the historical behavior of the user, e.g., users that did total wagering of more than 100 EUR on Slot games in the last 30 days.
This type of segment is updated by a defined schedule. For example, once a day at 5 PM.
From all other perspectives, this type of segment can be used as any other in all possible contexts of the Smartico platform.
To set up the segment, you must fill in the following sections:
To make your segment even more precise, you can also add a user state condition like brand, registration country, language, etc.
Behavioral segments, like other segments, can be exported and have a scheduled export if needed.
This type of segment can be used in any context in CRM Automation or Gamification modules:
In addition, Smartico.ai provides state-of-the-art Gamification and CRM automation software solutions. Its CRM tool combines player analytics data with machine-learning algorithms to help sports betting and iGaming businesses have a deeper understanding of players and their needs while also providing valuable data insights to ensure retention and loyalty, and much more.
As a leading Gamification & CRM Automation solution, Smartico offers:
And that’s just a small sample of what’s on offer. Smartico can help your business grow exponentially by supplying the solutions needed to bring the motivation in your company to a whole new level. Book your free in-depth demo today at: https://smartico.ai/request-a-demo
Despite it being so old, when it comes to analyzing your customers, the RFM method is not to be underestimated. And with a little tweaking here and there, you’ll soon be able to take a personalized and results-yielding approach to your customer base.
However, you need to remember that promotions, seasons, and holidays largely influence data. If a loyal customer with a solid buying history for the current month doesn’t make a purchase the following month, it doesn’t mean that you should move them straight away to another segment. The reason might be seasonal; eventually, they will return to their regular purchasing habits.
Share this article with your friends!
Want to find out how our event triggered campaigns can raise your customer engagement through the roof? Contact one of our experts for a free demo.