Mastering RFM + Engagement Scores for Smarter Email Targeting

In a digital landscape where inboxes are more crowded than ever, sending better emails—not more emails—is what truly drives ROI. Marketers today have access to more behavioral data than ever before. However, without strategic segmentation, that data remains underutilized. By combining RFM scoring (Recency, Frequency, and Monetary) with Email Engagement Scores, brands can pinpoint exactly who is most likely to open, click, and convert. This hybrid model has become a best-in-class approach for optimizing email marketing performance in 2025.

“If you don’t know which of your subscribers are most likely to open or buy, you’re just guessing.” — Mr. Phalla Plang, Digital Marketing Specialist

Understanding RFM Scoring

RFM analysis is a classic marketing model that evaluates customer value based on three factors:

• Recency — How recently a customer made a purchase
• Frequency — How often they purchase
• Monetary — How much money they spend

Each customer is assigned a score for these three dimensions—usually from 1 to 5—and the scores are then combined to create an overall value profile (Rejoiner, 2022). For example, a customer with a 5-5-5 rating is among the most valuable and engaged.

RFM segmentation helps marketers understand which customers are loyal, at-risk, or inactive, enabling targeted campaigns that drive higher lifetime value.

Adding the Engagement Layer

While RFM reveals purchase behavior, it doesn’t measure communication behavior—how subscribers interact with your emails. That’s where Email Engagement Scoring comes in.

Email engagement is typically measured by:

• Open rate (who views the message)
• Click-through rate (CTR) (who interacts with links)
• Reply rate (who engages in dialogue)
• Conversion rate (who completes desired actions)

An engagement score assigns weight to each action. For instance, you may award:

• Open = 1 point
• Click = 3 points
• Reply or conversion = 5 points

By combining engagement data with RFM metrics, marketers can distinguish high-value but disengaged customersfrom highly engaged but low-spending subscribers (MarTech, 2012).

Why Combining RFM and Engagement Works

When used together, RFM and engagement scores create a multidimensional view of your audience.

According to HubSpot (2024), brands that integrate behavioral engagement metrics with RFM models achieve:

• 20–25% higher click-through rates
• Up to 15% lower churn rates
• Improved deliverability, since disengaged users are segmented and reactivated separately

This hybrid model allows you to personalize not only what you send but also to whom and when.

How to Build an RFM + Engagement Model

Step 1: Collect the Right Data

You’ll need two primary data sets:

1. Transactional data — Purchase history, order frequency, total spend
2. Email interaction data — Opens, clicks, replies, unsubscribes, conversions

Ensure the time windows align—e.g., using the last 6 or 12 months—to produce fair comparisons (Peel Insights, 2025).

Step 2: Calculate RFM Scores

1. Rank each metric (R, F, M) from 1–5 using quintiles or deciles.
2. Combine the three to form a score like 5-3-4.
3. Sum or weight the components depending on business priorities.

Many marketers use free templates or tools like Rejoiner’s RFM Calculator or Braze’s RFM Segmentation to automate this step.

Step 3: Calculate Engagement Scores

1. Define the engagement actions you value (e.g., open, click, conversion).
2. Assign a weighted value to each.
3. Sum the totals for each subscriber over a defined window (e.g., 90 days).
4. Normalize scores on a 1–5 scale for consistency.

Step 4: Combine and Segment

Create a two-axis grid with RFM on one side and Engagement on the other. Then classify each subscriber:

Segment	Description	Action
Champions	High RFM + High Engagement	Reward loyalty with exclusives and early access
At Risk	High RFM + Low Engagement	Run win-back campaigns and feedback surveys
Potential Buyers	Low RFM + High Engagement	Offer discounts and product recommendations
Dormant	Low RFM + Low Engagement	Sunset or re-engage with special incentives

This approach ensures your highest-value and most active subscribers receive the most relevant and timely content.

Tools to Simplify the Process

• Braze — Automates segmentation using RFM metrics and engagement data.
• HubSpot Marketing Hub — Offers lead scoring workflows integrating both behavioral and purchase data.
• Klaviyo — Enables advanced customer segmentation using engagement and RFM scoring in real time.
• Peel Analytics — Provides visual dashboards for tracking RFM segments over time.

These tools allow marketers to integrate CRM, email analytics, and predictive modeling seamlessly—reducing manual work and increasing precision.

Best Practices for RFM + Engagement Targeting

1. Refresh data regularly. Recalculate scores monthly or quarterly to keep them relevant.
2. Use weighting logic. If engagement predicts conversion more accurately in your niche, weight it higher than monetary.
3. Protect deliverability. Exclude low-engagement users from frequent sends to avoid spam filters.
4. Run A/B tests by segment. Test offers and tones for each RFM tier to maximize ROI.
5. Integrate predictive analytics. Use tools like Simon.ai or Bloomreach to forecast future engagement based on past behavior.

Common Mistakes to Avoid

• Using outdated data: Recency is crucial; stale data leads to poor segmentation.
• Over-emailing dormant users: Hurts sender reputation and engagement metrics.
• Ignoring time windows: Ensure purchase and engagement windows match to avoid skewed results.
• Not tracking segment migration: Customers move between segments—track and adjust.

The Future: AI-Powered Predictive Scoring

By 2025, marketers are moving beyond static RFM segmentation to AI-driven predictive scoring models. According to Braze (2025), these models forecast who will purchase or unsubscribe next, using hundreds of behavioral signals.

Platforms like Bloomreach and Simon.ai now allow marketers to build predictive RFM models that update dynamically based on customer interactions (Bloomreach, 2025; Simon.ai, 2024). This ensures real-time personalization—a must for modern retention and lifecycle marketing.

Conclusion

Combining RFM with Email Engagement Scores transforms traditional segmentation into a smarter, data-driven system. It ensures that your campaigns reach not only the most valuable customers but also the most responsive.

By understanding both what customers do (RFM) and how they behave (engagement), brands can:

• Increase open and click rates
• Improve deliverability
• Boost customer lifetime value
• Reduce churn

The RFM + Engagement framework is no longer optional—it’s the future of intelligent email targeting.

References

Bloomreach. (2025). RFM customer segmentation. Retrieved from https://documentation.bloomreach.com/engagement/docs/rfm-segmentation

Braze. (2025). Understanding RFM segmentation for smarter customer targeting. Retrieved from https://www.braze.com/resources/articles/rfm-segmentation

HubSpot. (2024). RFM analysis: A data-driven approach to customer segmentation. Retrieved from https://blog.hubspot.com/service/rfm-analysis

MarTech. (2012). Adding email engagement to RFM scoring. Retrieved from https://martech.org/adding-email-engagement-to-rfm-scoring/

Peel Insights. (2025). RFM analysis for email ROI optimization. Retrieved from https://www.peelinsights.com/post/rfm-email-roi

Rejoiner. (2022). RFM analysis: The ultimate guide to ranking customer value. Retrieved from https://www.rejoiner.com/resources/rfm-analysis

Simon.ai. (2024). Implementing RFM analysis and segmentation in customer marketing. Retrieved from https://www.simon.ai/blog-posts/implementing-rfm-analysis-and-segmentation-customer-marketing