Customer Segmentation Using RFM Model
Chaya Chaipitakporn
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Posted Apr 18, 2025
Segmented users using RFM model for targeted marketing strategies.
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👥 Customer Segmentation (RFM Model) – Showcase Project
Segmenting users based on behavior to enable targeted retention, reactivation, and loyalty marketing strategies.
🧩 Project Summary
This project demonstrates how to segment users using the RFM (Recency, Frequency, Monetary) framework, a classic method for identifying different customer lifecycle stages. The dataset is based on a public e-commerce sample from Google Analytics 4 and was used purely for showcase purposes.
⚠️ Disclaimer:
This segmentation is performed on public sample data and does not reflect actual business value. In a real-world scenario:
The time range, thresholds, and segmentation strategy would align with specific business goals
The output would support strategic planning, marketing automation, and ROI analysis
The scoring system would be tuned to the company’s specific customer journey and CLV model
📌 Business Problem
The client wanted to better understand their user base and improve retention, but had no clear visibility into who their high-value customers were, who was churning, or which users showed potential. A behavioral segmentation model was needed to unlock campaign opportunities and guide resource allocation.
⚙️ My Approach
Queried GA4 raw ecommerce data in BigQuery (
purchase events only)Built RFM scores for each user:
Recency: Days since last purchase
Frequency: Total number of purchases
Monetary: Total spend
Ranked each user from 1 to 5 for R, F, and M, then classified them into behavioral segments
Aggregated users by group to visualize lifecycle distribution
Provided a framework for strategic CLV planning and ROI modeling based on segment movement
📊 Segment Distribution (Based on GA4 Sample)
🧠 Segment Strategy Map
Each group reflects a specific customer state based on recency and engagement value (frequency + spend):
🔵 Champions – Best customers, high RFM. Maintain with rewards & early access.
🟢 Loyal / Potential Loyalists – Core revenue drivers. Reward and nurture.
🟠 Needs Attention / Hibernating – Were once valuable, now dormant. Reactivation is critical.
🟣 Recent / Promising / Price Sensitive – New or growing users. Nurture toward higher value.
⚫ Lost – Very low engagement. Minimal return expected.
🔍 Strategic Planning Use Case
In real consulting settings, this RFM model is used not just for insight, but for business impact modeling:
✅ CLV by Segment:
Each segment’s average Customer Lifetime Value (CLV) can be estimated. For example, Champions may generate 10x the revenue of Promising users.
✅ ROI Modeling by Movement:
We simulate: "What if we can move 20% of Potential Loyalists into Loyal Customers?"
→ The ROI from that shift might outweigh moving Lost users up one level.
✅ Action Prioritization:
We found that improving Frequency scores from 3 to 4 (mid → high) has more return than moving users from 1 to 2 (low → mid).
→ This allows targeted spending on lifecycle campaigns where they matter most.
✅ Effort vs Value Map:
Segments are ranked by ease of movement vs value gained. For example:
Promising → Potential Loyalist = High success rate
At Risk → Loyal = Medium effort, high ROI
Hibernating → Active = High cost, low return
This forms the basis for campaign sequencing, incentive planning, and budgeting.
✅ Strategic Recommendations
Create segment-specific automations:
Champions → surprise rewards, referral programs
Loyal / Potential Loyalist → upsell, cross-sell, personalized perks
Hibernating / Can’t Lose Them → reactivation with urgency-based incentives
Monitor recency & frequency shifts monthly and re-score to track segment migration
Use CLV estimates per segment to guide marketing investment
Test behavior-based thresholds (not just time-based) for lifecycle targeting
🛠️ Tools & Techniques
BigQuery • GA4 • SQL (Window Functions) • RFM Scoring • Customer Lifecycle Strategy • CLV Modeling • Segment ROI Simulation