RFM Analysis RFM 客戶分群
Released已發布Perform RFM (Recency, Frequency, Monetary) customer segmentation from transaction data. Use this skill when the user needs to segment customers by purchase behavior, identify high-value buyers, design retention campaigns, or prioritize marketing spend by customer value — even if they say 'who are our best customers', 'which customers are at risk of churning', or 'how do we target our marketing'.
電商技能：RFM Analysis 分析與應用。
Overview概述
RFM segments customers based on three behavioral dimensions: Recency (when they last bought), Frequency (how often they buy), and Monetary (how much they spend). It converts raw transaction data into actionable customer segments for targeted marketing.
Framework 框架
IRON LAW: RFM Uses ACTUAL Behavior, Not Demographics
RFM is behavioral segmentation — it classifies by what customers DO,
not who they ARE. A 25-year-old and a 65-year-old in the same RFM segment
should receive the same treatment. Never mix RFM with demographic
assumptions.
The Three Dimensions
| Dimension | What It Measures | How to Calculate |
|---|---|---|
| Recency (R) | Days since last purchase | Today - Last purchase date |
| Frequency (F) | Number of purchases in period | Count of distinct transactions |
| Monetary (M) | Total spend in period | Sum of transaction values |
Scoring Method (Quintile-Based)
- For each dimension, rank all customers and divide into 5 equal groups (quintiles)
- Score 5 (best) to 1 (worst): R=5 means most recent, F=5 means most frequent, M=5 means highest spend
- Combine into 3-digit RFM score (e.g., R5-F4-M5 = recent, frequent, high-value)
Note: For Recency, LOWER days = HIGHER score (more recent is better).
Key Segments
| Segment | RFM Pattern | Description | Strategy |
|---|---|---|---|
| Champions | R5, F5, M5 | Best customers, recent, frequent, high-value | Reward, loyalty program, early access |
| Loyal | R4-5, F4-5, M3-5 | Consistent buyers | Upsell, cross-sell, referral program |
| Potential Loyalists | R4-5, F2-3, M2-3 | Recent, moderate frequency | Nurture to increase frequency |
| At Risk | R2-3, F3-5, M3-5 | Were frequent/high-value, not buying recently | Win-back campaign, special offers |
| Hibernating | R1-2, F1-2, M1-2 | Long dormant, low value | Low-cost reactivation or let go |
| New Customers | R5, F1, M1-2 | Just made first purchase | Onboarding, second-purchase incentive |
Implementation Steps
Phase 1: Data Preparation
- Required: Customer ID, Transaction Date, Transaction Amount
- Clean: Remove refunds, test orders, internal orders
- Set analysis window (typically 12-24 months)
Phase 2: Calculate RFM Scores
- Calculate R, F, M for each customer
- Assign quintile scores (1-5) for each dimension
- Combine into segments
Phase 3: Segment and Act
- Map each customer to a named segment (Champions, At Risk, etc.)
- Design targeted actions per segment
- Measure results: did targeted customers behave differently?
Output Format輸出格式
# RFM Analysis: {Business}
Gotchas注意事項
- RFM is backward-looking: It tells you what customers DID, not what they WILL do. Combine with predictive models (CLV prediction) for forward-looking insights.
- Equal quintiles may not make sense: If 80% of customers bought only once, quintile 1-4 are all "one-time buyers." Consider custom breakpoints based on business context.
- Monetary can be misleading for subscriptions: If everyone pays the same subscription fee, M dimension adds no information. Drop it and use RF only.
- B2B vs B2C frequency differs: A B2B customer buying quarterly is "frequent." A B2C customer buying quarterly may be "at risk." Calibrate to business context.
- Don't over-message At Risk customers: Bombarding dormant customers with emails can increase unsubscribes. One well-crafted win-back campaign is better than weekly emails.
References參考資料
- For Python/SQL implementation code, see
references/rfm-implementation.md - For CLV prediction extending RFM, see
references/clv-prediction.md