RFM Analysis: A Complete Guide | Revised 2023


RFM analysis answers a number of urgent questions that consumer brands have about their businesses. 

For example:

  • How can I find my high-value customers (HVCs)
  • How can my brand be more profitable? 
  • How can I improve my retention marketing program? 
  • How can I build customer segments based on purchasing behavior? 
  • What’s a reliable and data-driven way to optimize my marketing spend? 
  • What is the meaning of life?

Well, maybe not the last one.

While RFM analysis can’t do everything, it is a powerful way for brands to reliably segment their customers and learn a great deal about them.

At Daasity, we love RFM and automate it for our brands, so we wanted to put together a complete guide to walk through what it is and what makes it so powerful.

What is RFM Analysis?

RFM analysis allows consumer brands to segment and rank their customers by value over a time period, using three dimensions: Recency (how recently someone purchased), Frequency (how frequently someone purchased), and Monetary (how much they spent, on average).

In short, RFM analysis shows who your best and worst customers are​​—and everyone in between. 

These three dimensions are key, as they correspond to core pillars of customer behavior. 

  • Recency: Customers who have bought more recently are more likely to respond to marketing content, which means they may be more likely to take advantage of an offer or read more about your brand and its products. 
  • Frequency: Higher repurchase rate is a reliable indicator of customer enthusiasm and engagement.
  • Monetary: Customer segments based on amount spent allows you to understand which customers are bigger spenders than others.

The traditional approach to RFM

Traditionally, marketers using RFM (which originates from the direct mail-only days) would rank order customers based on the three dimensions and bucket the lists into pentiles (i.e., 5 groups), where group 1 is the most valuable and group 5 is the least valuable. 

To see how this works, let’s examine Recency.

From a customer list, you would rank order those customers based on their last purchase date (over a timeframe) from most to least recent. Then, you would break the customer ranking into 5 buckets. Group 1 would include the top 20% most recent purchasers, group 2 would include the top 21%-40% of purchasers, and so on. 

You would then do the same for Frequency and Monetary over the same timeframe.

The rating system would include a three digit grouping for each customer. For example, customers A-C in this chart are rated 1-5 based on where they fall in each dimension:

chart showing three customers with different RFM scores: A (121), B (445), C (221)

Based on this grouping, customer A is the most valuable, customer C is the second-most valuable, and customer B is the third-most valuable. 

This RFM approach was manageable in the early days of direct mail, but it is now extremely difficult to manually track, calculate, and manage. 

Daasity’s Approach to RFM

To make RFM easier to track and interpret, Daasity breaks customers down into deciles (i.e., 10 groups) and automatically calculates all RFM Scores, every day, via the following formula:

rfm score formula: F times square root of M, all divided by R

In this system, the deciles correspond to RFM scores of 1-10. So, an RFM score of 1 is the best and covers the top 10% of customers. Likewise, a score of 2 corresponds to 11-20%, a score of 3 corresponds to 21-30%, and so on. 

The result?

A clean and easy-to-interpret “Top 10” ranking system, which can be visualized and analyzed, which we’ll show in the next section.

How do you benefit from using RFM Analysis?

Identifying High Value Customers (HVCs)

RFM analysis is the clearest way an eCommerce brand can identify its high-value customers: the most-engaged customers who have an outsized impact on a brand’s profits (specifically, gross margin per customer, a.k.a. customer lifetime value).

We think of customers with RFM scores of 1 and 2 (the top 20%) as a brand’s HVCs, as they universally have an extremely outsized impact on your revenue and profitability. And, quite frequently, RFM score 1 customers tend to be ~1.5x to ~5x more valuable than RFM score 2 customers.

  • Some brands find they have outliers for their score 1 customers, in that score 1 customers contribute much more value.

We’ll run through a few examples (with real data) about how this looks in practice.

Consider the visualization below.
visualization showing RFM scores. RFM 1 has ltv of 406.74


  • Customers with a score of 1 have a 2.3x greater Customer Lifetime Value than customers with a score of 2, which, in the case of this brand, translates to about 4 million dollars in gross margin.
  • The differences among RFM segments’ value becomes dramatic as the RFM Scores decrease. Customers with a score of 1 have a 43x greater LTV than customers with a score of 10. 
A second viz example:
rfm visualization showing rfm score 1 customers $2951.28


  • Of note, this brand has a fairly extreme difference in score 1 value compared to all others. Score 1 customers are worth 6.3x more than score 2 customers, and 37x more than score 5 customers.
  • Score 10 customers are actually contributing negative LTV, at -$2.73/customer.
A last viz example:
rfm visualization showing score 1 customers $224.09 and score 10 customers -$66.06


  • Score 8, 9, and 10 customers are contributing negative LTV.
  • Although this and other brands find that they have multiple RFM segments contributing negative LTV, in practice, those customers with positive LTV contributions may still be unprofitable, depending on the brand’s CAC.

Increasing CLV & driving more profitable revenue

Customer lifetime value is one of the most important metrics to track and increase in order to keep your eCommerce business thriving. 

While one-time customers often end up costing your business in the long run, building long-lasting relationships with more valuable customers is fundamental to a long-term eCommerce strategy.

  • With RFM, you can easily target your HVCs, tailor your messaging to them, and send them your very best offers via email and SMS. 

Other potential insights:

  • You may find that some of your score 3 and 4 customers have certain product affinities making them more likely to repurchase and become HVCs when you offer them those products. 
  • You may find that a subset of your score 1 customers is not price sensitive and has a higher likelihood of buying your most expensive products when you feature those products in messages they receive. 

Move more of your product catalog

Most of your acquisition campaigns probably focus on promoting your hero product, but they may not promote products that pair well with the hero product: enter retention and RFM marketing. 

Customers might come in to buy a popular product that they see in an ad (likely one of your hero products). By building campaigns geared toward your more valuable customers, you can move your product catalog by recommending relevant and data-driven bundles, cross-sells, or upsells.

Decrease churn

Via RFM, marketers can reduce customer churn by communicating with more valuable customers and better understanding why each segment stays with the brand and working to prevent churn in the first place.

  • If your most loyal customers are churning, you can conduct surveys or reach out to them to understand why they're not buying. Once you determine why they're leaving (product issues? pricing? lack of a rewarding loyalty program? or something else?) you can begin rectifying those issues and keep your customers as long as possible.
  • You can retain your customers for longer by collecting zero-party data from them and creating more personalized experiences. E.g., Do they have a particular style of shirt, or a favorite flavor of a beverage? Show them what they love more often.

Leveraging RFM Analysis for your brand

Retaining the top 20% (and especially the top 10%)

As a general rule, the more valuable a customer (or, in this case, customer segment) is to your business, the more you should do to ensure that they remain regular customers for as long as possible.

rfm visualization showing score 1 ltv $1648.01 and score 10 ltv $42.43

In the case of the brand above, RFM analysis reveals that the Top 20% (scores of 1 and 2) of its customers are responsible for 65% of its LTV, and the Top 10% of its customers are responsible for 50% of its LTV. 

These are remarkable numbers!

They serve as a bold, size 1000 font sign that says, “SELL TO ME!” In particular, score 1 customers should be given the highest priority (your most compelling offers, plenty of loyalty points, early access to products, upsells and cross-sells, etc.), but score 2 customers have the highest potential to become RFM Score 1 customers. 

Therefore, it is vital to nurture them in order to level them up into RFM Score 1. 

By increasing the number of high-value customers with high RFM Scores, your brand will grow faster, be more profitable, and have a more efficient and effective marketing spend. At Daasity, we’ve seen this to be universally true: the brands that focus efforts on maximizing value from their HVCs grow to be larger, more profitable brands.  

Targeting the Top 21% to 40% of Your Customers (RFM Score 3 and 4)

Your Top 21-40% of Customers are the customers who aren’t your best customers, but they could be. If you can boost their RFM, you can expect them to generate the lion share of your brand's profits (i.e., join the illustrious ranks of RFM Score 1 and 2). These customers love your brand, and if nurtured properly, will continue to make frequent purchases from your brand for years to come. 

Avoiding the Bottom 20%

Improving profitability and optimizing your marketing budget with RFM analysis isn’t only about building value among your high score customers but saying goodbye to your lowest RFM Score customers. 

As we often say, not every customer you have will love you equally, and that’s okay. 

The “bottom 20%” (RFM Score 9 and 10) of this brand’s customers only make up 4% of its total LTV.

For your customers with these low RFM Scores, you may be best served lowering/discontinuing your marketing spend dedicated to them and refocusing it on your HVCs to generate even greater value from them.

Automating and Customizing RFM Analysis with Daasity

Daasity is a data and analytics platform purpose-built for consumer brands selling via eCommerce, Amazon, retail, and/or wholesale. We centralize all your data, and enable to build your perfect data environment. 

You can leverage some of our ~20 prebuilt (and fully customizable) dashboards, or you can build your own in your preferred BI tool. 

Daasity provides RFM analysis on an LTV basis out of the box, and we enable brands to automatically push RFM and other data (updated daily) to marketing platforms, such as Klaviyo, Sailthru, and Attentive. This means you can create dedicated campaigns and offers for particular RFM segments.

Customizing RFM Analysis in Daasity based on your brand’s needs

Depending on your brand’s customer base, your industry, and the types of products you sell, you may want to adjust certain elements of your RFM. With Daasity, you can, so that your analytics solution is as unique as your brand.

We provide guidelines about how best to leverage each dimension, but if you’re ready to jump in and change how you have your RFM calculated, you can:

part of Daasity UI showing customization of recency, frequency, monetary values
Screenshot from the Daasity App for setting up RFM Scoring. You can choose one of the suggestions (e.g., 0-30 for Recency or 50-100 for Monetary) or input your own.

Final RFM Analysis thoughts

If you’re looking to find your best and worst customers, develop better targeting in your marketing program, optimize spending, and much more, RFM analysis is the way to go. 

RFM is rooted in the most important dimensions of customer behavior, and it can help power your brand to new data-driven heights.

If you’d like to learn even more about RFM analysis and/or have RFM (and the rest of your data) automated for your brand, we’d love to chat with you

Data is our passion, and putting your data to work for you is our business.

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