Audience segmentation is a powerful way to analyze your customer data, define your target customer persona(s), and tailor your acquisition and retention efforts for the best-performing results. RFM segmentation is a method of segmenting your customer audiences by grouping them based on three key vectors of their past purchasing behavior: recency, frequency, and monetary value. This is a simple framework anyone can implement to enable more personalized marketing strategies.

Keep reading to learn more about RFM segments and RFM scoring and to see some helpful RFM analysis examples.

What Is RFM Segmentation?

RFM segmentation is a customer segmentation technique that categorizes your brand’s shoppers based on three vectors of their past purchasing behaviors: recency, frequency, and monetary value. 

This allows marketers to divide a larger audience into smaller groups of shoppers with similar characteristics. By doing this, businesses can identify different customer groups and develop targeted marketing strategies that cater to each group's specific wants and needs. When done correctly, this leads to higher engagement, conversion rates, and customer loyalty.

Here’s a closer look at each of these three vectors:

• Recency: How much time has elapsed since a customer last made a purchase. In most cases, customers who have purchased more recently are valuable customers likely to engage with the brand again.
• Frequency: How often a customer makes purchases. Customers who make frequent purchases are typically a brand’s most valuable shoppers, demonstrating higher brand loyalty than those who purchase less often.
• Monetary value: The amount of money a customer has spent on purchases over a given period of time. Customers who spend a comparatively high amount of money are valuable and tend to have higher brand loyalty, making them likely to purchase again. Customers with high monetary value are typically the most valuable customers for a given brand.

Using the RFM model, customers are ranked based on each of these three vectors and then divided, or segmented, into groups based on their scores.

How RFM Scoring Works (with Examples)

Ready to build out your own RFM analysis? Triple Whale offers pre-built, plug-and-play RFM segments, but you can also do this manually using the steps below.

Step 1: Gather Data

Start by collecting transactional data from your customer database or CRM system. This should include purchase histories, such as date, frequency, and transaction value for each customer.

Then, sort that data by recency, frequency, and monetary value into a dataset that looks something like the following:

Step 2: Score Recency

Most marketers will then score this dataset along all three metrics using a scale of one to five. These scores typically correspond with common RFM segments, such as:

• 5 = Champions
• 4 = Loyalists
• 3 = Big spenders with low frequency and/or new but promising customers
• 2 = At risk
• 1 = Low-value, low-engagement or lost

Start with recency. Sort your dataset by recency, and assign each customer a score. The customers with the most recent purchases score higher:

Step 3: Score Frequency

Next, score recency. Sort your dataset by frequency, and assign each customer a score. The customers who shopped from you most frequently score higher:

Step 4: Score Monetary Value

Lastly, assign scores for monetary value. Sort your dataset one last time, and assign each customer a score. The shoppers who spent the most money with your business score higher:

Step 5: Combine Scores into an RFM Profile

Then, combine the three scores above. This is usually done by simply finding the sum of all three numbers. In other words:

RFM score = R + F + M

For example, customer 7 above has an RFM score of 15, the highest score possible. Customers with scores closer to 15 are more valuable; those with scores closer to 3 are less valuable.

As you can see from this dataset, customers who purchased within the past few days, purchased your product frequently, and spent a comparatively high amount of money with your business are sorted into a high-value segment. Customers who haven’t purchased from you in a long time or who have made only a handful of low-value historical purchases are sorted into a low-value segment.

Once you’ve established your segments, you can analyze the characteristics of each group to reveal patterns and trends about their purchasing behavior. You can use these learnings to improve marketing and customer retention strategies. Just be sure to regularly monitor and refine your RFM segmentation and marketing strategies to improve customer engagement and maximize customer value.

Common RFM Segments and What They Mean

There are different ways to plot out your RFM customer segmentation. In fact, here at Triple Whale, we have some segment-specific language we like to use. Here are some of the most popular segments brands develop alongside our unique spin, as well as some RFM segmentation examples to help illustrate this concept.

High-value Customers

Also sometimes called champions or loyalists, these are customers who score high on all three metrics of recency, frequency, and monetary value. They are typically the most engaged customers, likely to make frequent, high-value purchases. 

• Example: Your customer buys your coffee almost every month. She regularly orders multiple bags of beans, subscribes to a limited-edition roast release, and recently purchased branded merchandise. She spent $600 over the last year and made her most recent purchase last week.
• Strategies to try: Target these customers with exclusive offers, loyalty programs, and personalized marketing campaigns to keep them engaged and encourage them to continue buying from your brand in the future.
• The Triple Whale take: We call these customers your loyal shoppers and your core audience; they’re the shoppers who buy the most often from your store and/or who have bought the most recently. We also track your whales, or your customers who have generated the most revenue for your store.

New Customers

‍These are customers who have made a recent purchase, so they have a high recency score, but have not yet established a high level of loyalty or spending, so they have low frequency and monetary value scores. 

• Example: Your customer made their first purchase after discovering your skincare brand through an Instagram ad. They bought a $28 face serum two weeks ago. They haven’t made any additional purchases yet, but they signed up for your email newsletter after checking out.
• Strategies to try: Target these customers with welcome offers and promotions to encourage them to make another purchase and/or establish a stronger personal relationship with your brand. 
• The Triple Whale take: We call these customers your newbies, or first-time buyers on your site. We also track promising customers, those who return often but don’t spend a lot.

At-risk Customers

‍These are customers who haven’t made a purchase recently, so they have a low score for recency. Also sometimes called high-risk customers, they’ve previously made frequent and high-value purchases, so they have high scores for frequency and monetary value. 

• Example: Your customer used to buy from your online clothing boutique every season, often placing $150 to $250 orders for shirts, pants, and outerwear. He was very active last year, but he hasn’t made a purchase in the last six months, even though new collections have launched.
• Strategies to try: Segmenting customers at risk of churn flags them as likely to need some nurturing to get them to return to your brand. Try launching re-engagement campaigns, personalized offers, and loyalty programs to encourage future purchases.

Low-value Customers

‍These shoppers score low on all three metrics. They’re typically the least engaged and loyal and are unlikely to make frequent or high-value purchases. 

• Example: Your customer made a one-time purchase of a $9 office accessory during a clearance sale eight months ago. They never opened follow-up emails and haven’t returned to your site since that first order.
• Strategies to try: You can target these customers with promotions and special offers to encourage them to make another purchase, or they can be excluded from marketing campaigns to focus on higher-value customers.
• The Triple Whale take: We refer to these shoppers as lost; they’ve made one purchase with your brand but never returned.

How Ecommerce Brands Use RFM Segmentation

Once you’ve established your segments, you’re ready for RFM segmentation analysis. The learnings you come up with can be applied to various use cases that ultimately help you drive success.

Improve Retention and Win-back Campaigns

Your RFM analysis can be a boon for customer retention for a number of reasons. You might focus on former high-value customers who haven’t shopped in a while, sending personalized reminder emails or exclusive SMS deals, for example, as part of targeted win-back campaigns to recapture their business.

Nurture Relationships with High-value Customers

Or, you might use RFM segmentation to identify your core and/or promising new customers. Then, you can keep them engaged with your brand and encourage repeat business with special access to sales or promotions through targeted email, social, or SMS campaigns, for example. 

Spot Customers at Risk of Churning

You can also use RFM scores to help you avoid losing those high-value customers to begin with, before they ever need to be added to win-back campaigns. Instead, you can personalize messaging to re-engage at-risk customers based on behavioral data you’ve gathered about their preferences and decision-making.

Onboard New Customers

Make sure to add promising new customers to nurture retention flows (just one example of how you can personalize email flows by segment). Offer onboarding support through email campaigns and consider including new shoppers in special offers to encourage repeat purchases. 

Tailor Offers and Bundles by Customer Value

Your higher-value customers are already making more frequent purchases at a higher monetary value, so encourage them to continue this behavior with strategic offers, discounts, and promotions. When appropriate, you might also offer them upselling, cross-selling, or bundling options that increase total order value but also provide value.

Prioritize Campaigns and Reallocate Spend 

RFM analysis can also help you use your budget more efficiently. Suppress campaigns being sent to users who are lost, and instead allocate that spend to campaigns targeting customers who are more likely to purchase from you again.

RFM vs. Other Segmentation Methods

RFM is just one type of customer segmentation, but it’s among the most actionable and measurable. Marketers often use the following methods, too. Here’s how four types of segmentation work and how they compare to the RFM model.

• Demographic segmentation: This method groups customers based on demographic factors, such as age, gender, income, education, occupation, and family status. This information can still be helpful for tailoring marketing messages, but it doesn’t account for actual shopping behavior. For example, shoppers of the same age or gender may interact entirely differently with your brand.
• Geographic segmentation: This method groups customers based on their location, such as country, region, city, or neighborhood. While it can be helpful to target American shoppers for a Black Friday sale when other countries don’t observe Thanksgiving, all customers from the same country will still display vastly different shopping behaviors.
• Psychographic segmentation: This method groups customers based on personality traits, values, beliefs, interests, and lifestyles. This is significantly harder to measure because it often relies on making assumptions about your shoppers or collecting survey data from them, whereas RFM relies on purchasing data you already have.
• Behavioral segmentation: RFM is a highly measurable and easy-to-implement form of behavioral segmentation, which groups customers based on their behaviors, such as buying habits, product usage, brand loyalty, browsing patterns, and decision-making processes.

Challenges and Limitations of RFM Segmentation

All that said, there are still some areas where RFM falls short.

It ignores the customer’s perceived value of your product

RFM segmentation generally only considers the perceived value of your brand and product from your business’s perspective, rather than from your customer’s perspective, according to 2024 research in Scientific Reports. While it shows you their past purchasing behavior, it doesn’t tell you anything about how they weigh the benefits of your product or service against the cost.

It doesn’t account for other factors that affect frequency

How often your customer purchases from you could be due to your fantastic marketing campaigns and their deeply rooted loyalty to your brand. But it could also be influenced by the economy, word of mouth, seasonality, and more. These nuances aren’t reflected in a simple RFM score.

It prioritizes big spenders over loyal ones

A customer who made one large purchase very recently can skew your scores. Over-emphasizing monetary value can hide some of the importance of your most loyal customers, who may not spend as much in a single transaction but interact with your brand more frequently.

It’s static

RFM scores are calculated based on historical data that reflects your customers’ behavior at a precise moment in time, so it can’t help you predict future behavior. Plus, people change — and so does the market. It’s crucial to update your RFM data regularly or you might be making marketing decisions based on an outdated analysis.

It doesn’t take into account other ecommerce metrics

Recency, frequency, and monetary value are important aspects of your business to track, but they’re not the only markers of success. RFM doesn’t factor in other ecommerce metrics you might prioritize, such as customer lifetime value, return on ad spend, or click-through rate, for example. It also ignores measures of customer retention rate, such as customer satisfaction, and non-transactional interactions with your brand, including email open rates, social media engagements, and website visits.

How Triple Whale Enhances RFM Segmentation

While this may all feel like a lot if you’re new to RFM model segmentation, keep in mind you don’t have to go it alone. Triple Whale’s Data Platform for Shopify-based brands comes pre-loaded with our six powerful RFM segments. We build these audiences by examining your customers’ historical data and splitting them into buckets based on RFM scoring.

We’ll help you unlock detailed insights into your customers’ behavior and preferences based directly on your transactional data. Ultimately, this helps you achieve every brand’s main goal: making more money to grow your business.

Conclusion

The RFM model is a way for marketers to simply and effectively segment customers based on the recency, frequency, and monetary value of their past purchases. 

Grouping your shoppers by these characteristics helps you identify opportunities to personalize marketing efforts to each unique group. This, in turn, can improve engagement, conversions, and loyalty. RFM analysis also provides you with the data you need to determine which channels, campaigns, and marketing tactics result in the highest return on investment.

Ready to dig in? Book a free demo today to learn more about Triple Whale’s RFM segmentation and start exploring your brand’s audiences.

RFM Segmentation FAQs 

What does RFM stand for?

RFM stands for recency, frequency, and monetary value, three vectors of customers’ past purchasing behavior that are used to segment shoppers based on their value to your business.

What is a good RFM score?

RFM scores are typically calculated by adding together recency, frequency, and monetary value scores. If you’re using a simple one-to-five scale to score each of these three vectors, then a good RFM score is as close to 15 as possible.  

Is RFM better than other analyses?

It can be. It relies on behavioral data, which is objective, reliable, and actionable, compared to other types of segmentation data that’s often self-reported by your customers. But it still has drawbacks, such as not accounting for nuanced factors that may affect purchase frequency, over-prioritizing monetary value, and relying only on historical data.