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What is RFM Analysis?

Ryan Greene

Head of Marketing

The “RFM” in RFM analysis stands for recency, frequency and monetary value. RFM analysis is a way to use data based on past customer behavior to predict how customers are likely to act in the future. An RFM model is built using three key factors: 1) how recently a customer has transacted with a brand; 2) how frequently they’ve engaged with a brand; and 3) how much money they’ve spent on a brand’s products and services.

RFM analysis was born out of direct mail marketing, in particular a 1995 article by Tom Wansbeek and Jan Roelf Bult titled “Optimal Selection for Direct Mail,” which was published in the journal Marketing Science. Their work helped confirm the Pareto Principle — the idea widely held among marketers that 80% of sales come from 20% of a brand’s customers.

RFM analysis enables marketers to increase revenue by targeting specific groups of existing customers (i.e., customer segmentation) with messages and offers that are more likely to be relevant based on data about a particular set of behaviors — and thus generate increased response rates, loyalty and, ultimately, customer lifetime value (CLV).

Each of these RFM metrics has been shown to be effective in predicting future customer behavior and increasing revenue. Customers who have made a purchase in the recent past are more likely to do so in the near future. Those who interact with your brand more frequently are more likely to do so again soon. And those who have spent the most are more likely to be big spenders going forward.

RFM analysis enables you to target customers with messages that best match their relationship with your brand. For example, you are likely to have more success suggesting big-ticket items to customers who spend frequently and in large amounts. On the other hand, you are more likely to grow the value of your relationships with customers who make purchases frequently, but only in small amounts, by rewarding them for their loyalty or offering referral promotions.

How it Works

Market research has traditionally concentrated on demographic and psychographic data, which marketers use to conduct customer segmentation. That data is then used to predict the behavior across much larger populations that share the same set of traits. However, these methods depend on data from a small sample of consumers.

With the advent of systems like customer data platforms (CDPs) that help gather, unify and synthesize customer behaviors, marketers have much more granular data about the habits of individual customers to inform segmentation. Rather than segmenting customers only using demographic and psychographic data, marketers can create segments based on the real-world behavior of individuals, including purchase history across any channel (online or offline), browsing history, prior campaign responses and more. Unsurprisingly, this type of segmentation is called behavioral segmentation.

And even a basic CRM system can perform rudimentary tracking of the three easily quantifiable characteristics that contribute to RFM analysis:

  • Recency: This refers to the amount of time since a customer’s last interaction with a brand, which can include their last purchase, a visit to a website, use of a mobile app, a “like” on social media and more. Recency is a key metric because customers who have interacted with your brand more recently are more likely to respond to new marketing efforts.
  • Frequency: This refers to the number of times a customer has made a purchase or otherwise interacted with your brand during a particular period of time. Frequency is a key metric because it shows how deeply a customer is engaged with your brand. Greater frequency indicates a higher degree of customer loyalty.
  • Monetary value: This refers to the total amount a customer has spent purchasing products and services from your brand over a particular period of time. Recency is a key metric because customers who have spent more in the past are more likely to spend more in the future.

RFM Analysis for Customer Segmentation

Computing RFM for real-world application typically requires special analytical expertise or advanced math skills. And, like any model, RFM models can vary in complexity from simple to sophisticated. RFM segmentation begins by ranking customers in each of the three categories—recency, frequency and monetary value. Typically, this is done on a scale of 1 to 10. A 10 indicates the top 10% in each category (i.e., the most recent to transact, the most frequent to transact and those who purchased the most), a 9 the next 10% and so forth. With scoring such as this, you can create effective customer segments, including:

  • Your best customers: These are the customers who earn top scores in every category. They’re loyal, willing to spend generously and likely to make another purchase soon. Such customers are primed to respond well to loyalty programs. They’re more likely to be interested in new products you launch. And because they’re committed to your brand and its products, it probably makes less business sense to offer them discount pricing. Instead, increase CLV by suggesting big-ticket items and recommending products based on past purchases.
  • Your big spenders: This customer segment is based on only one of the three metrics — customers with top scores for monetary value. Typically, marketers target this segment with luxury offers, higher subscription tiers and value-add cross-/upsells that increase average order value. Again, it probably makes sense not to shrink margins by offering discounts.
  • Your loyal customers: This is another customer segment that takes into consideration only one of the three metrics — customers with top scores for frequency. Despite making purchases often, they aren’t necessarily your biggest spenders, so consider rewarding them with free shipping or similar offers. Advocacy programs and reviews can also be effective ways to engage these customers.
  • Your faithful customers: Customers who score high for frequency but low in monetary value tend to respond best to product recommendations based on past purchases, as well as incentives tied to spending thresholds (e.g., a free gift for transactions above the brand’s average order value).
  • Your at-risk customers: Customers who have been in your top tier in the past (best, big spenders and/or loyal) but who now score low for recency and frequency present a special opportunity. Marketers should consider targeting them with messages aimed at retention, such as discount pricing, exclusive offers and new product launches. With the help of your CDP, you can even create specific customer journeys aimed at re-engaging and retaining at-risk customers.

Why RFM is Effective for Small and Medium-Sized Businesses

For startups and smaller retailers with limited marketing resources, RFM analysis can be a particularly effective tool because of its:

  • Simplicity: RFM analysis does not, on its own, require complex tools or sophisticated analytical capabilities. The principles are easy to understand and the results are easy to interpret and act on.
  • Affordability: In many cases, it’s possible for marketing professionals without advanced statistical or analytical training to perform RFM customer segmentation with only a standard spreadsheet.
  • Effectiveness in direct marketing: RFM analysis, which grew out of database marketing and direct mail marketing, has been shown to be effective with relatively inexpensive digital direct marketing strategies that smaller brands can afford, such as email.

Scaling RFM to the Enterprise

That said, as a business scales, you will also need technology that scales with the complexity and volume of interactions across all your channels, regions and more. With advanced RFM, you can create more authentic experiences at scale — using a range of customer traits as inputs to your model and going beyond scores and segments to achieve 1:1 personalization.

The most advanced enterprise-class CDPs serve as an engine for creating these types of RFM-driven experiences. They empower business users to orchestrate campaigns and journeys quickly and seamlessly leveraging the full breadth and depth of all your customer data — across any and all channels.

The Limits of RFM Analysis: What to Avoid

While RFM segmentation is powerful, it does have limits. When performed manually, it’s prone to human error. RFM analysis is also based on just a few behavioral traits, lacking the power of the advanced predictive analytics now available.

Some businesses may use RFM analysis as an excuse to bombard high-ranking customers with messages and thus reduce response rates on campaigns that could otherwise be highly effective. On the other hand, it can cause marketers to neglect customers with low rankings even though many of them may be worth cultivating. For example, your RFM model may fail to account for the impact of past promotions or seasonality on RFM analysis. Likewise, a customer may have very little activity with your brand one month, yet be ready to engage in purchasing behavior the following month due to a birthday or anniversary.

How Relevant the RFM Model is Today

RFM analysis remains a perennial favorite of marketers. It’s simple and intuitive, yet data-driven. It has the power to provide actionable insights down to the individual customer level — all without any input from data scientists or complex tools. That isn’t to say you can’t do sophisticated things with RFM analysis. For example, you can use RFM techniques to identify your best customers and turn them into a seed audience within an advertising platform that uses lookalike modeling to automatically identify prospects who share similar key traits.

Nevertheless, thanks to CDPs, marketers are now able to combine RFM data with other behavioral and demographic traits — everything from geolocation to recent products purchased — to create even more effective segmentation. Better yet, they can quickly and easily apply lookalike models and other sophisticated analytics to predict what messages are most likely to resonate and how and when those messages are most likely to prompt action.

With or without these more sophisticated modern approaches, marketers can use RFM analysis to:

  • Increase the effectiveness of email marketing campaigns: Build an automated drip campaign with messages tailored to each segment.
  • Increase loyalty and user engagement: Follow up on recent customers or new customers with timely promotions and educational content likely to increase their engagement with your brand.
  • Decrease churn: Send personalized messages, offer repeat purchases at a discount or provide surveys that help you understand and address potential concerns.
  • Reduce marketing costs and increase ROI: Reduce costs by focusing quickly and easily on smaller segments that are more likely to produce revenue and use insights from RFM analysis to optimize campaigns going forward.

Learn more
RFM is one of many powerful KPIs that can be used to deploy and measure the success of your enterprise’s marketing programs and customer experiences. Explore additional KPIs by reading 12 KPIs Every Marketer Should Define and Know. And if you’re ready to dig deeper into how you can deploy authentic customer experiences at enterprise scale, contact us today to schedule a conversation with one of ActionIQ’s marketing technology experts.

Written By

Head of Marketing

Ryan Greene is an expert on the intersection of customer data and digital strategies who has led big data marketing initiatives within the retail and financial services industries. Ryan joined ActionIQ because he believes business stakeholders are most effective when they have user-friendly solutions to extract intelligence and value from all their data.



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