RFM analysis saves you when you’re downing in an ocean of data. Do you find yourself in this scenario?
You are running reports, preparing presentations for the next marketing meeting, and wondering what the hell should be done in the next quarter to turn things around and engage your customers better.
It sounds like you need to at least try RFM segmentation and see what insights hide within each segment.
You know that the only way to get to your customer’s heart is by knowing who they are. But somehow, this always lands at the end of the list with zero priority.
The problem is, there’s just too much focus on reporting and not enough attention paid to understanding and engaging with the people who actually buy from you every day.
As a result, when the new quarter begins, and the marketing budgets are being released, you stick with the same old strategy and pray it’s going to work this time as well.
Essential questions when analyzing customer behavior
What if you stopped for a moment, put that “client-centricity” buzzword to work, and asked yourself some questions?
In not so many words, what’s your relationship with your customers? Is it a long-lasting love relationship, or are you heading towards a nasty breakup with half of the customers in your database?
Whether Customer Retention is a new concept for you or not, RFM analysis helps you start building your own customer retention strategy based on the customer behavior data you already have.
The RFM model
RFM model is a proven marketing strategy based on customer behavior segmentation. It groups customers based on their purchase history – how recently, with what Frequency, and what value they bought.
The RFM model became popular in 1995 with the publication of the “Optimal Selection for Direct Mail” article in “Marketing Science”.
Back in the ’90s, the RFM model was used for optimizing direct marketing campaigns designed for a specific customer segment. Companies saved a lot of money on printing.
For example, they could send highly targeted messages to their top customers – those who were more likely to place another order, become repeat and loyal customers.
Today, the RFM model is used widely and helps companies optimize their efforts throughout various marketing channels.
What is RFM (Recency, Frequency, Monetary)?
RFM represents a segmentation strategy that uses historical transactional data to help you segment your customers based on three variables: Recency (R), Frequency (F), and Monetary Value (M).
- Recency (R) – how recently a customer has purchased from you;
- Frequency (F) – how often a customer purchases from you;
- Monetary Value (M) how much a customer usually spends.
RFM is one of the most popular segmentation models in eCommerce. All you need to identify your RFM segments is the historical customer data that you already have within your CRM or eCommerce platform.
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What is RFM analysis?
RFM analysis is a technique for understanding and analyzing your customers based on three factors: Recency, Frequency, and Monetary Value. The goal is to predict which clients are more likely to buy again in the future.
You can perform RFM analysis:
- Manually – using the good ol’ data exports spreadsheets, or
- Automatically – using a tool that does all the work for you once you set the RFM scale for the R, F, M values.
For both alternatives, you first have to set the RFM scale and score according to your business size and customer lifecycle.
If you choose automated RFM analysis, setting the scale and scores represents all the manual work you’ll ever need to do. Your segments are constantly updated based on transactional data, so you can go straight to performing RFM analysis as often as you need.
What if I told you the RFM analysis is just a few clicks away, and it will help you shed some light on your customers’ behavior within your shop?
Automating RFM segmentation and RFM analysis
No hero has ever accomplished anything without a trusted sidekick. Harry Potter had Ron Weasley; Holmes had Watson. Here, at Omniconvert, we have REVEAL as our disruptive software (trust me on this and continue reading).
For the past seven years, we’ve been building Customer Retention strategies for eCommerce players from all around the globe. In a nutshell, by knowing (1) how recently a customer bought from you, (2) how many orders he placed, (3) and the total value of those orders, you can detect the love level this customer has for you and can prepare an appropriate experience for them.
We thus set out on a journey to using RFM analysis with our clients. While on that journey, we first stopped and asked ourselves:
How can we design a segmentation that saves time, is easy to use, and gives instant access to whoever is hiding in the data?
Our objective was to handle the heavy lifting of number crunching and give marketers time to breathe and focus on creating relevant marketing strategies for each customer segment.
We realized the magical powers of the RFM analysis – one of Customer Retention’s most powerful metrics. That is why we have integrated it into REVEAL, our Customer Value Optimization software.
RFM Analysis Example – Here is how it works
RFM analysis reveals data anomalies that will allow you to understand which are the most important groups of customers when you weigh your customer acquisition costs against the margin they generate.
REVEAL collects your data through a feed and accesses the data related to your customers, products, categories, and orders placed. It provides you with a smart reporting dashboard that gives you insights into how your eCommerce customers buy, how frequently they do so, who your top customers are, and which ones think of leaving you.
REVEAL is a Customer Value Optimization software that can help you monitor and nurture your customers and tailor your marketing actions based on their buying behavior. It also enables you to understand their satisfaction with your products and services.
RFM segmentation is a method that helps you identify the most important types of customers by grouping them and giving scores to their Recency, Frequency, and monetary values.
This allows companies to target specific clusters of customers with greater relevance for their particular behavior – thereby generating higher response rates, increased loyalty, and better customer lifetime value.
REVEAL applies RFM Segmentation and automatically displays customer groups such as VIP, Active, Dormant, and Lost customers so that eCommerce professionals can reward each group according to its value.
We display how many customers there are in the segment and their revenue as a group for each segment.
Let’s say you have a lot of “New Passions” in your database (bought recently, placed very few orders, or very high value) who bring you an important chunk of your revenue. Through an online survey, you may want to understand what made them choose you and what you can do to make them stay for more than a one-night stand.
How to calculate RFM metrics
To calculate the RFM metrics or variables, we use your historical data. More specifically, we look at the minimum and maximum values for Recency (R), Frequency (F), and Monetary values (M) from your store;
We then split the data for R, F, and M into five groups with the use of quintiles (see table below);
Each bucket will receive a score based on a scale that suits your business:
- From 1 to 5 (see table below) if you have more than 200k customers;
- From 1 to 4 if you have 30-200k customers;
- From 1 to 3 if you have less than 30k customers.
eCommerce stores have different sales cycles. Depending on what is being sold, a customer with 10 orders placed may receive a score of 5 (for a store where sales cycles are long) and a score of 1 (for a store where sales cycles are short)
Each one of your customers will receive points for Recency, Frequency, and Monetary based on buying patterns in relation to all the other customers;
|Points||Recency (days since last purchase)||Frequency / Monetary values (number of orders and orders value)|
|5||within the last month||customers who are in the top 5% in the database|
|4||within the last 3 months||customers who are in the top 20% in the database|
|3||within the last 6 months||customers who are in the top 30% in the database|
|2||in the last year||customers who are in the top 60% in the database|
|1||more than a year ago||the customers who spent and bought the least|
After points are being assigned, each customer in your database will receive a unique score. This score will constantly change based on the customer’s interaction with your store.
RFM score = 555 | RFM score = 234 | RFM score = 115 | RFM score = 313
We group similar scores into 11 RFM Customers Groups and display them on the Dashboard, where you can see them, and from where you can take a further business decision based on your conclusions;
For each group and sub-group of customers, we display how many customers there are in there and the revenue they have brought so far.
How to perform RFM analysis
Now you know who’s hiding in your database. Good, it’s time to act!
We have revealed your RFM segments in the image above. Once REVEAL is up and running, you will instantly see these groups in your Dashboard. This means that by simply applying a filter and downloading a CSV, you can get the “Soulmates” email addresses and prepare a pampering campaign just for them.
You know you have a lot of “About To Dump You” customers (R = 2 – 3, F = 1 – 5, M = 1 – 5). They are disengaging from you. Think about a re-engagement campaign that would bring them back to your website. Send them a personalized email asking what happened that made them stop visiting you.
You have your “Soulmates.” They are your ideal customers, valuable customers. Reach out to them and see your store through their eyes. Maybe think of re-designing your web experience with their help, or reward them for being loyal to you for so long.
REVEAL is also integrated with the Omniconvert web personalization platform. This means that you can apply A/B tests, overlays/pop-ups, or online surveys only on a selected customer segment. Our product has native integration with the Omniconvert platform allowing you to instantly jump from one platform to another and run your experiments.
Like the “Flirting” customers (R = 4, F = 1-2, M = 4-5), they are active and have placed 1-2 orders of high value. Use your charms and make them order more. Create an online survey and see what triggers them.
You also have your “Apprentices” (R = 4-5, F = 1-3, M = 1-2), those new customers who are very active but new to your store, and so they are not spending too much. Prepare an online survey and find out more about what they are looking for.
As with any love relationship, your customers go through many love stages with your store. When you know which is which, you can give them their special treatment in pricing, email campaigns, and website experience based on the value they bring you. Analyzing the user behavior with these segmentation techniques, sending personalized messaging, creating loyalty programs for the existing customers lead to more customer transactions and increasing conversions that improve business.
Benefits of RFM Analysis
Performing RFM analysis allows you to understand customer behavior and predict how segments could evolve in the future. The predictive character is one of the most important benefits of using RFM analysis for your business.
Although manually performing RFM analysis is better than nothing, it has a lot of shortcomings (we covered them in this article).
If you plan to use RFM segmentation for your future initiatives, performing automated RFM analysis comes with multiple advantages:
Save valuable resources
Instead of spending hours aggregating data and preparing spreadsheets, you focus solely on analyzing your RFM segments. Automation allows your teams to extract valuable insights to optimize strategies and increase customer lifetime value.
You can’t afford errors in customer segmentation and making decisions around RFM analysis. Automated RFM solutions eliminate errors associated with manual work and offer reliable data for your growth plans.
Up to date
Using automated RFM segmentation ensures that all your segments are constantly updated. You can perform regular analysis knowing that you’re looking at fresh reports, or you can select a certain period to search for trends or anomalies.
Having up-to-date information allows you to take advantage of opportunities that arise – like nurturing new high-value customers, or prevent negative trends – like an increasing number of complaints from a category of newly acquired customers.
Consistency and traceability
Segmenting your customers based on the RFM model helps you maintain consistency in analyzing segments and traceability over the evolution of your RFM segments. This way, all departments share the same view over the segments using the same reference system.
Wrapping up the RFM Analysis story
OK, if you are reading this it means you have reached the end of the RFM Analysis story.
Whether you skimmed the article or actually read it, it doesn’t matter. If there is one thing you should take away with you, it is that you need to reach out and know your customers for a long-lasting relationship.
Ready to meet your customers?Give REVEAL a try!
I am leaving you with Valentin’s video on what RFM analysis is about and how it can help marketers like you.
Frequently asked questions about RFM analysis1. What does RFM mean? / What is the meaning of RFM?
Recency, Frequency, and Monetary value (RFM) is defined as a method that helps you analyze and understand your customers based on their score on each of these three factors – Recency, Frequency, and Monetary Value. Understanding the meaning of RFM is vital for your customer retention strategy. RFM model is a proven marketing strategy based on customer behavior segmentation. It groups customers based on their purchase history – how recently, with what Frequency, and what value they bought.2. How does RFM analysis work?
The RFM analysis starts with proper segmentation based on the RFM variables. The first thing you need to work on is assigning a score from 1 to 5 for Recency, Frequency, and Monetary values. The results of your RFM analysis will allow you to group customers based on their purchase history. Now that you have these customer segments, you can see your most valuable customers and find ways to increase customer retention among different RFM groups.3. How do you use RFM segmentation?
You can use RFM segmentation to target specific clusters of customers with greater relevance for their particular behavior. Your growth marketers can generate better strategies based on these segments, thereby generating higher response rates, increased customer loyalty, and better customer lifetime value. RFM Marketing is the newest technique in customer retention.4. What is a good RFM score?
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, where 1 represents the lowest and 5 is the highest score for each variable. If you use this scale, a good RFM score is 555.5. How do you analyze customer segmentation?
You can use the RFM model to analyze customer segmentation. As a result, you will have an accurate representation of your customer database by leveraging the full potential of your first-party data. For a proper customer segmentation, use RFM variables: Frequency (very frequent buyers, medium frequency buyers, one transaction only buyers), Recency (most recent customers, medium recency customers, least recent customers), Monetary value (customers who spend the most, above-average monetary value, average, low monetary value).6. What is an RFM analysis, and how can it improve ROI?
RFM analysis helps you analyze and understand your customers based on three factors: Recency Frequency Monetary values. Using this method generates valuable insights to change your strategy and improve ROI. The customer segmentation based on RFM modeling signals some potential pain points related to your brand, products, or shopping experience.7. How is Recency calculated?
Recency refers to how recently a customer bought from you. You can set your own scale to calculate Recency depending on your sales cycle. For example, you could use a scale from 1 to 5: 1 – more than a year ago, 2 in the last year, 3 within the last 6 months, 4 – within the last 3 months, 5 – within the last month. If one of your RFM segments has low Recency, it’s a sign that they might have switched to the competition although they had been loyal to you, and it’s worth finding out why. Also, you’ll see a general decrease in order recency in the deal hunters segment.