RFM analysis is a customer segmentation technique used to categorize customers based on their purchasing behavior. It evaluates three key metrics:
- Recency (how recently a customer made a purchase)
- Frequency (how often they make purchases)
- Monetary value (how much they spend)
The goal of RFM analysis is to improve marketing strategies by understanding and targeting different customer segments based on their engagement and value to the business. This helps businesses tailor their marketing efforts, improve customer retention, and increase overall profitability.
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 your old data exports and spreadsheets. You manually assign scores to each customer based on their recency of purchase, frequency of purchase, and monetary value.
- Automatically – using a tool that does all the work for you once you set the RFM scale for the R, F, M values. The tool handles the rest of the calculations for you.
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.
Transitioning from understanding RFM analysis to implementing it effectively requires a shift in focus towards truly comprehending your customer relationships. Instead of drowning in data, consider asking yourself fundamental questions:
- Did my customers enjoy browsing through my store?
- Did they come back to buy more?
- How do people feel after interacting with my product?
- How much money are they spending on my site?
- Are they talking about me with their friends?
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.
How RFM Analysis Works
To calculate and analyze 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;
- First, gather historical data on all your customers’ purchases. Identify the date of the last purchase, the total number of purchases, and the total amount spent.
- Identify the minimum and maximum values for R, F, and M across your customer base.
- Divide the data for Recency, Frequency, and Monetary value into groups using quintiles. Quintiles are statistical values that divide the data into five equal parts, each containing 20% of the data set (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.
- Consider the sales cycle of your store. Depending on what is being sold, a customer with 10 orders placed may receive a score of 5 (for a store with long sales cycles) and a score of 1 (for a store with short sales cycles).
- Each customer will receive points for Recency, Frequency, and Monetary value based on their buying patterns in relation to all other customers. For example, customers in the top quintile for Recency (most recent purchases) will score 5, while those in the bottom quintile (least recent purchases) will score 1.
| 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 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.
Using Omniconvert’s REVEAL tool, 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.
Like what you're reading?
Join the informed eCommerce crowd!
We will never bug you with irrelevant info.
By clicking the Button, you confirm that you agree with our Terms and Conditions.
RFM Analysis Example
Let’s delve into a practical example to understand how RFM analysis works in practice:
Scenario: Consider a retail company specializing in online fashion apparel. They’ve collected transactional data over the past year and want to optimize their marketing efforts to increase customer engagement and loyalty.
1. Recency: The retail company assigns a recency score to each customer based on the number of days since their last purchase. For instance:
- Score 5: Customers who made a purchase within the last 30 days.
- Score 4: Customers who made a purchase between 31 to 60 days ago.
- Score 3: Customers who made a purchase between 61 to 90 days ago.
- Score 2: Customers who made a purchase between 91 to 120 days ago.
- Score 1: Customers who made a purchase more than 120 days ago.
2. Frequency: The retail company assigns a frequency score based on the total number of purchases made by each customer over the past year. For instance:
- Score 5: Customers who made more than 10 purchases.
- Score 4: Customers who made 7 to 10 purchases.
- Score 3: Customers who made 4 to 6 purchases.
- Score 2: Customers who made 2 to 3 purchases.
- Score 1: Customers who made only 1 purchase.
3. Monetary: The retail company assigns a monetary score based on the total monetary value of each customer’s purchases over the past year. For instance:
- Score 5: High spenders who spent more than $1000.
- Score 4: Customers who spent between $501 to $1000.
- Score 3: Customers who spent between $251 to $500.
- Score 2: Customers who spent between $101 to $250.
- Score 1: Low spenders who spent less than $100.
RFM Segmentation: Once the scores for recency, frequency, and monetary value are assigned to each customer, the company can segment its customer base into different groups using combinations of these scores. For example:
- Soulmates: Customers with high scores in all three categories (5-5-5).
- Lovers: High frequency and monetary value, but slightly lower recency (5-4-4).
- Ex-Lovers: Low recency score indicating they haven’t made a purchase recently, despite past high frequency and monetary value (1-5-5).
- Apprentice: High recency score but low frequency and monetary value (5-1-1).
Actionable Insights: By analyzing customer segments derived from RFM analysis, the retail company can tailor marketing strategies to effectively engage each group. For instance, they can offer exclusive discounts to ex-lover customers to incentivize repeat purchases or create personalized loyalty programs for soulmates to maintain their engagement.
Applying RFM Analysis with REVEAL
REVEAL, Omniconvert’s tool, 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 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 identify a group of “New Passions” – customers who bought recently, placed very few orders but made high-value purchases. These customers bring in significant revenue. To understand their behavior better, you can conduct an online survey to understand what made them choose you and what you can do to make them stay for longer.
Benefits of RFM Analysis
RFM analysis can save you when you’re drowning in an ocean of data. If you’re unsure how to engage your customers better, RFM segmentation can provide valuable insights. Here are some key advantages:
Enhanced Customer Segmentation
RFM analysis allows you to segment your customer base into distinct groups based on their purchasing behavior, allowing you to tailor your marketing efforts more effectively to meet the needs and preferences of each group.
Improved Customer Retention
By identifying customers who are at risk of churning, RFM analysis helps you develop targeted retention strategies.
Increased Marketing Efficiency
With RFM analysis, you can focus your marketing resources on high-value customers who are more likely to respond positively to your campaigns. This targeted approach leads to higher conversion rates and a better return on investment.
Better Customer Insights
RFM analysis provides deep insights into your customers’ purchasing behavior, including how often they buy, how much they spend, and how recently they made a purchase.
Optimized Customer Experience
Understanding your customers through RFM analysis allows you to create more personalized and engaging experiences. By addressing the specific preferences and behaviors of different customer segments, you can enhance overall customer satisfaction and loyalty.
Strategic Decision-Making
RFM analysis provides a clear, data-driven view of your customer base, helping you make informed strategic decisions. Whether it’s launching a new product, adjusting pricing strategies, or planning marketing campaigns, RFM insights ensure your decisions are grounded in real customer data.
Benefits of Automated RFM Analysis
Although manually performing RFM analysis is better than nothing, it has a lot of shortcomings. 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.
Eliminate errors
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.
Stay 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.
Enable Real-time actions
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.
Ensure 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.
Automating RFM segmentation and RFM analysis with REVEAL Tool
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.
Your Ideal Customer Profile isn’t 100% reliable. But your first-party data is.
Find the truth about your best and worst customers
How to Use the REVEAL Tool to Perform RFM Analysis
Now you know who’s hiding in your database. Good, it’s time to act! With Omniconvert’s REVEAL, you can instantly see your RFM segments (as seen in the image above) on your Dashboard. Here’s how you can leverage these insights for effective customer engagement:
Access Your RFM Segments
Once REVEAL is up and running, you will see your RFM segments on the Dashboard. By applying a filter and downloading a CSV, you can get the email addresses of specific segments like “Soulmates” and prepare targeted campaigns.
Re-Engage Customers
If you have many “About To Dump You” customers (R = 2-3, F = 1-5, M = 1-5), they are at risk of disengaging. Consider a re-engagement campaign to bring them back. Send personalized emails asking what made them stop visiting and offer incentives to return.
Reward Your Best Customers
Your “Soulmates” are your most valuable customers. Reach out to them to see your store through their eyes. Consider redesigning your web experience based on their feedback or reward them for their loyalty.
Utilize Omniconvert’s Integration
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.
Engage with High-Value Customers
For “Flirting” customers (R = 4, F = 1-2, M = 4-5) who are active with high-value orders, try to make them order more. Create online surveys to understand what triggers them.
Nurture New Customers
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.
Personalize Customer Experience
As with any 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 user behavior with these segmentation techniques, sending personalized messaging, creating loyalty programs for existing customers lead to more customer transactions and increasing conversions that improve business.
Limitations of RFM Analysis
While RFM analysis offers valuable insights into customer behavior and segmentation, it’s important to acknowledge its limitations:
- Lack of Context: RFM analysis focuses solely on Recency, Frequency, and Monetary Value metrics, overlooking other crucial factors such as demographic data, psychographic insights, and customer preferences. Without context, it may provide incomplete or misleading conclusions about customer behavior.
- Homogeneous Segments: RFM analysis segments customers based on quantifiable metrics, resulting in relatively homogeneous groups. However, customers within the same RFM segment may have diverse needs, preferences, and motivations, leading to generic marketing strategies that fail to resonate with individual customers.
- Limited Predictive Power: While RFM analysis can identify high-value customers and at-risk segments, it may not accurately predict future customer behavior or lifetime value. Factors such as market trends, competitive dynamics, and external influences can impact customer purchasing decisions, rendering RFM predictions less reliable.
Despite these limitations, RFM analysis remains a valuable tool for understanding customer behavior, segmenting your customer base, and informing marketing strategies.
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 through the article or read it thoroughly, the key takeaway remains the same: building lasting relationships with your customers is paramount.
As you embark on your customer-centric journey, remember to leverage tools like RFM analysis to gain deeper insights into customer behavior and preferences. By understanding your customers on a granular level, you can tailor your marketing strategies effectively and foster long-term loyalty.
To further enhance your understanding of RFM analysis, I recommend watching Valentin’s insightful video on the topic:
