Running a business means juggling relationships with all kinds of customers. You might have customers who shopped at your online store yesterday, while others haven’t purchased in nearly a year. Some customers buy once and disappear, while others faithfully return every month. Some are careful spenders, while others customers love to splurge.
Treating this diverse group as a single homogeneous mass will work against you. Sending the same “We miss you!” email to all customers wastes resources and can make your brand seem out of touch.
RFM analysis breaks down customer behavior into three dimensions: purchase recency, buying frequency, and monetary value of orders. Understanding these insights helps segment customers into different groups so you can craft marketing campaigns speaking to each group.
What is RFM analysis?
Recency, frequency, and monetary (RFM) analysis is a customer segmentation technique used to analyze customer behavior based on their most recent purchase, how often they buy, and how much they spend. Examining these three dimensions can help you predict future customer behavior and tailor your marketing efforts to specific customer groups.
How does RFM analysis work?
You score each RFM factor from 1 to 5, with five representing the highest-value customers in each category. Brands can customize scoring thresholds to fit their business model and customer behavior patterns.
Here’s how it works:
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Recency measures the time since a customer’s last purchase, signaling their current engagement level. A customer who bought a sweater yesterday might score a 5, while someone who hasn’t shopped since last winter would likely receive a 1.
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Frequency tracks how often a customer makes purchases, helping you identify your most consistent buyers. Someone who buys new shoes every month might earn a 6, while a customer who shops only during annual sales might score a 1.
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Monetary value measures how much customers spend per order, helping you pinpoint high-value customers. A shopper who routinely spends $200 on your premium products would score a 5, for example, while someone who sticks to $30 basics might get a 2.
Analyze scores individually or average them out for a more complete view of customer value. For example, a customer who scores a 4 on recency, a 3 on frequency, and a 5 on monetary would have an overall RFM score of four, calculated as:
RFM = (4 + 3 + 5) / 3
Keep in mind that scoring methods vary by tools—some add up the scores for a total of 3 to 15, while others calculate the average.
Why ecommerce businesses use RFM analysis
- Develop more effective marketing campaigns
- Identify at-risk customers before they churn
- Better direct your marketing and sales spending
You may already segment your customers in some way—perhaps by demographics or by classifying them as “active” or “inactive.” But RFM analysis is particularly useful in ecommerce because it considers how it reflects real shopping patterns.
Here’s why the three RFM analysis dimensions matter in digital retail:
Develop more effective marketing campaigns
RFM segmentation lets you create campaigns that reflect specific shopping behavior. You might:
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Launch a VIP preview sale for customers with high monetary scores, giving your biggest spenders first access to new products
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Offer subscription programs to high-frequency buyers with consistent purchasing patterns
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Send personalized add-on recommendations to recent customers while their interest is still high
Aligning your marketing to your biggest spenders can result in higher conversion rates because your offers match proven behaviors. For example, a subscription offer would likely be better received by a loyal, monthly customer than one who only shops twice a year.
Identify at-risk customers before they churn
RFM analysis not only helps maximize value from your best customers but also alerts you to those at risk of leaving. A drop in score can signal waning interest, with each metric pointing to a different intervention.
Here are a few ways you might get back on track:
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When a customer’s recency score falls, a well-timed re-engagement email with a discount might bring them back to your store.
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If you notice purchase frequency dropping—say from monthly customer transactions to quarterly ones—a quick SMS featuring “New arrivals in your favorite collection” could spark interest again.
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When a customer’s monetary value takes a dive—like someone who spent $1,000 last year and only spent $200 this year—reaching out with a customer satisfaction survey could help you understand what’s changed to rebuild the relationship before it’s too late.
Better direct your marketing and sales spending
Ideally, you could run every marketing experiment imaginable—from social media retargeting campaigns to direct mail marketing. But like most brands, you probably don’t have an unlimited marketing budget or boundless resources. Segmenting customers helps identify which customer groups are most valuable—letting you focus marketing efforts on retaining and growing your relationship with your highest-potential segments.
This approach brings practical efficiency. Rather than sending out a piece of direct mail to all 10,000 of your customers, you might find that focusing on your 2,000 most valuable customers often proves more cost-effective. While acquiring new customers through advertising certainly has its merits, data consistently shows that nurturing relationships with existing customers yields better returns on marketing investment.
How to use RFM for customer segmentation
Here’s how to get started with RFM analysis, from segmentation strategy first steps to measuring your campaign success:
1. Choose your RFM analysis tool
While you could theoretically calculate RFM scores manually, trying to track purchasing behavior across hundreds or thousands of customers in spreadsheets quickly becomes unwieldy and error-prone. Fortunately, dedicated customer analytics tools and modern ecommerce platforms have built-in RFM analysis capabilities to automatically score and segment your customer base.
For example, Shopify’s built-in RFM system automatically scores customers on a 1 to 5 scale for recency, frequency, and monetary value, then categorizes them into distinct groups like “Champions” and “Dormant” customers.
The platform calculates these scores based on your store’s specific data rather than industry standards, ensuring the segmentation reflects your unique customer patterns. You can also access detailed reports breaking down metrics like average days since last order, total number of orders, and total amount spent for each customer segment.
2. Set up customer segment lists
RFM customer segmentation divides your customer base into distinct groups based on their combined recency, frequency, and monetary scores. While platforms like Shopify automatically generate these segments, you can also create custom segments aligned with your specific business goals and customer patterns.
You can then match each group with targeted marketing initiatives. Here are common segments and personalized marketing strategies:
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High recency, frequency, and monetary value. Invite your most loyal customers to exclusive product launches or VIP events to cement their loyalty and generate sales.
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High monetary value, lower recency/frequency. Target them with premium product launches and luxury-focused messaging that speaks to their preference for high-value purchases.
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High recency, moderate frequency/monetary value. Nurture their budding interest with educational content about your products and early access to new collections.
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Declining recency, historically high frequency/monetary value. Deploy targeted win-back campaigns with special offers based on their past purchase categories.
3. Track segment performance metrics
Track how different RFM segments respond to your campaigns to get valuable insights into which customer groups truly drive your bottom line. Measure key metrics like repeat purchases, average order value, and customer lifetime value to know exactly which marketing tactics work for each group.
When you compare campaign results across segments, clear patterns emerge—like whether your VIP preview events outperform your win-back discount offers. This data takes the guesswork out of where to focus your marketing efforts and spend.
RFM analysis FAQ
How is RFM calculated?
RFM scores are calculated by ranking each customer from 1 to 5 on three dimensions: recency (how recently they purchased), frequency (how often they purchase), and monetary value (how much they spend).
How do you interpret RFM scores?
When interpreting RFM scores, look at each dimension individually (like identifying customers with high recency but low monetary value). Alternatively, you can also use the combined average score, where higher averages (closer to five) represent your most valuable customers and lower scores (closer to one) might flag customers at risk of churning.
What is an example of RFM?
A customer who made a purchase last week (recency = five), buys monthly (frequency = four), and spends an average amount (monetary = three) would have an RFM score of four [(5+4+3)/3], placing them among your best customers.
