How Predictive Analytics Supports RFM Modeling | Pecan AI

How Predictive Analytics Supports RFM Modeling

Discover how predictive analytics enhances RFM modeling to identify high-value customers and drive personalized offers.

In a nutshell:

  • RFM modeling categorizes customers based on recency, frequency, and monetary value of purchases.
  • RFM analysis helps identify high-value customers for targeted promotions.
  • Predictive analytics outperforms RFM by considering more variables and predicting future outcomes.
  • RFM segments include recent purchasers, high-frequency purchasers, high-value purchasers, and VIP customers.
  • Brands can benefit from using predictive analytics to drive sales and personalize offers for customers.

RFM (recency, frequency, monetary modeling) is a marketing analytics method used to categorize or group customers. Analysts define groups by the recency, frequency and monetary value of their recent purchases. With RFM analysis (also known as RFM modeling), marketers identify high-value customers for ad campaigns or other tailored promotions.

Traditionally, RFM is categorized as a descriptive analytics approach, meaning it analyzes past events. In contrast, predictive and prescriptive analytics are forward-looking.

How does RFM analysis work?

RFM analysis typically assigns customers a score based on their interactions with your brand. Scores are based on three components: recency, frequency, and monetary.

  • Recency: How recently your customers purchased from you
  • Frequency: How often or at what frequency your customers are purchasing
  • Monetary: How much your customers are spending with you

Then, you can weight or average these scores to prioritize them from highest to lowest, thereby identifying your high-value or VIP customers.

Importantly, the significance of RFM modeling is rooted in the 80/20 rule, which states that 80% of your revenue comes from 20% of your customers. Identifying and segmenting customers based on their purchasing behavior allows brands and marketers to drive sales. For instance, they can provide personalized offers, extend exclusive deals, or offer other promotional elements.

Customer segmentation with RFM

Customer segmentation with RFM analytics is fairly straightforward once your goal or objective is confirmed. For example, a brand could segment the customer database to highlight VIP customers with purchase recency within the last 60 days. Another example is segmenting VIP customers with X number of purchases in the last 90 days.

A few of the most used RFM segments include:

  • Most recent purchasers: Customers who recently purchased.
  • Highest-frequency purchasers: Customers who are most often purchasing from your business.
  • Highest-value purchasers: Customers who make the largest value purchases on average (think “whales”).
  • Most loyal or VIP Customers: highest score across all RFM categories.

Why predictive analytics outsmarts RFM analytics

With the introduction of predictive analytics and AI-powered modeling, RFM analysis has become an antiquated approach. With predictive analytics, brands can now consider a greater array of variables, demographics, behaviors, etc., to identify high-value customers. In the past, the use of only three considerations has been one of the leading limitations of RFM analytics.

Furthermore, RFM analysis does not predict future events or outcomes. It only looks to your customers’ past, instead of providing foresight about what customers are likely to do in the future, which is a core benefit of predictive analytics models.

In short, predictive analytics considers more components of your customers’ behavior and profiles, while also offering a look into their future. Unlock this powerful technology’s potential for your business with a predictive analytics platform accessible to all business teams.

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