Recency, Frequency, Monetary (RFM) Modeling with Data Distiller for Real-Time Personalization

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In today’s highly competitive market, understanding customer behavior and acting on insights in real time is critical for success. Businesses are constantly looking for ways to drive personalized experiences that increase customer engagement and loyalty. One method that has stood the test of time in this endeavor is RFM modeling—an analysis technique that segments customers based on three key behaviors: Recency, Frequency, and Monetary value.

The Data Distiller team built a customer RFM model within the Adobe Experience Platform environment. Here’s a look at what we achieved and how this approach can transform how businesses engage with their customers.

What is RFM Modeling?

RFM (Recency, Frequency, and Monetary) analysis is a data-driven approach to segment customers into distinct groups based on:

• Recency (R): How recently a customer has made a purchase.
• Frequency (F): How often a customer makes purchases.
• Monetary (M): How much revenue a customer generates through purchases.

These three metrics provide a comprehensive view of customer behavior, allowing businesses to classify customers into segments like high-value, at-risk, or new customers. Each RFM score offers actionable insights for targeted marketing and engagement strategies.

Implementing RFM with Data Distiller

Here’s a simplified view of the SQL code to build an RFM model:

SELECT
    userid,
    days_since_last_purchase,
    orders,
    total_revenue,
    5-NTILE(4) OVER (ORDER BY days_since_last_purchase DESC)  AS recency,
    NTILE(4) OVER (ORDER BY orders DESC)                      AS frequency,
    NTILE(4) OVER (ORDER BY total_revenue DESC)               AS monetization
FROM
  RFM_Values;

In this structure, we capture key behavioral data such as:

• User ID: The primary identity for the customer, based on their email.
• Days Since Last Purchase: The recency metric.
• Orders: The frequency metric.
• Total Revenue: The monetary value.
• RFM Model: A classification text that summarizes the customer’s RFM score.

By running this SQL query in Data Distiller, you can automatically segment customers into RFM categories. The real power of this model comes from its ability to:

• Highlight recent buyers who are more likely to engage.
• Identify frequent shoppers and loyal customers.
• Focus on high-revenue contributors to maximize ROI.

Applying RFM Attributes for Audience Creation

Once the RFM scores are calculated, the next step is to enrich the Real-Time Customer Profile. Data Distiller can continuously update these profiles with fresh RFM attributes, thereby automating the segmentation process. This enables marketing teams to take timely action without diving into technical details.

For example:

• Recency: Engage customers who haven’t purchased recently with targeted retention campaigns.
• Frequency: Reward loyal customers who purchase frequently through loyalty programs.
• Monetary: Offer premium services or discounts to high-revenue customers.

Dashboards built on top of this model allow stakeholders to easily visualize and interact with these segments, driving better decision-making and campaign targeting.

Automating RFM Insights for Actionable Campaigns

One of the most valuable aspects of building this model in Adobe Experience Platform is the ability to automate the entire process—from RFM score calculation to actionable insights on dynamic dashboards. Through Data Distiller’s Scheduling feature, fact tables are updated as soon as fresh data becomes available, ensuring that the RFM segments stay current.

For the marketer, this means they can focus on what they do best—creating targeted campaigns—without needing to worry about managing the data pipeline. The platform handles the heavy lifting of data processing and segmentation, giving marketers the ability to act in real time.

RFM and Real-Time Personalization

RFM (Recency, Frequency, Monetary) segments can be dynamically integrated into real-time personalization strategies by leveraging customer behaviors to tailor interactions instantly. As customer data is updated in real time, businesses can adjust their personalization efforts based on the latest RFM scores. For example, a customer who recently made a high-value purchase might see personalized product recommendations or loyalty rewards immediately upon their next visit, while a less engaged customer could receive a targeted offer or incentive to re-engage. This real-time adaptation ensures that customers receive highly relevant and timely content, enhancing their overall experience and increasing the likelihood of conversions.

Once these attributes or base segments are created in Real-Time Customer Profile, they become available for personalization at the Edge (e.g., Adobe Target, Offer Decisioning) and for Streaming Activation through platforms like Adobe Journey Optimizer and Streaming Destinations.

Dear Marketer: You Should Not Worry About SQL

As a marketer, you’re not expected to be writing or understanding SQL all day. The whole purpose of RFM (Recency, Frequency, Monetary) analysis is to have these attributes prepared so you can use them for audience analysis, activation, and personalization.

Typically, data engineers, architects, or your marketing ops team will handle the technical work, while you’ll focus on consuming and applying the results. That’s even more reason to be kind to your data teams!

But if you're curious about SQL, don’t worry—it’s not as hard as it seems. SQL operates on similar principles to working with Excel. The main limitation of Excel is that it struggles with large, complex datasets and can’t handle high volumes of events. That’s why tools like Data Distiller exist, designed to process trillions of records in one go.

Keep in mind that all the RFM attributes created in Data Distiller are automatically added to the Real-Time Customer Profile. Once they’re in there, they become available for audience creation and activation across social media and paid media channels. They’re also ready to use as audiences in Adobe Journey Optimizer. And here’s the real advantage: these attributes are available for edge personalization through Adobe Target or even Offer Decisioning.

Also, RFM attributes are calculated for each individual customer. You can also add this data as a lookup table in Customer Journey Analytics, allowing you to analyze every journey within the context of RFM attributes.

Lastly, the same RFM attributes can be used to enrich the B2B Real-Time Customer Profile, which enables account segmentation and personalization of buying groups in Adobe Journey Optimizer's B2B edition. Essentially, this means that the entire Adobe DX (Digital Experience) portfolio can be activated using these attributes. Whether it's for precise account-based marketing, personalized experiences, or optimizing journeys for B2B audiences, these RFM attributes play a crucial role in driving effective personalization and engagement across Adobe’s ecosystem.

So, the big question you should be asking your data team isn’t how to build the RFM attributes, but rather how to gain access to them. Specifically, you should ask what data they are calculated on, how frequently they are updated, and how fresh the data is. Understanding these factors will help ensure that your audience analysis, segmentation, and personalization strategies are based on up-to-date and relevant insights.

Read the Tutorial in Data Distiller Guide

The link is here