Ecom Rfm Analysis

RFM Analysis

Overview

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

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

Scoring Method (Quintile-Based)

1. For each dimension, rank all customers and divide into 5 equal groups (quintiles)
2. Score 5 (best) to 1 (worst): R=5 means most recent, F=5 means most frequent, M=5 means highest spend
3. 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

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

# RFM Analysis: {Business}

## Data Summary
- Customers analyzed: {N}
- Analysis window: {start} to {end}
- Transactions: {N}

## Segment Distribution
| Segment | Count | % | Avg R (days) | Avg F | Avg M |
|---------|-------|---|-------------|-------|-------|
| Champions | {N} | {%} | {days} | {count} | ${X} |
| At Risk | {N} | {%} | ... | ... | ... |
| ... | ... | ... | ... | ... | ... |

## Key Findings
- Top 20% customers contribute {X%} of revenue
- {N} customers at risk of churning (were high-value, now dormant)
- {N} new customers need second-purchase nurturing

## Recommended Actions
| Segment | Action | Channel | Expected Impact |
|---------|--------|---------|----------------|
| Champions | {loyalty reward} | {email/app} | Increase AOV by X% |
| At Risk | {win-back offer} | {email/SMS} | Recover X% of dormant revenue |

Gotchas

• 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.

Scripts

Run python scripts/rfm_score.py --verify to execute built-in sanity tests.

References

• For Python/SQL implementation code, see references/rfm-implementation.md
• For CLV prediction extending RFM, see references/clv-prediction.md