ecom-rfm-analysis

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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'.

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Sourceasgard-ai-platform/skills

Added on2026-04-15

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npx skill4agent add asgard-ai-platform/skills ecom-rfm-analysis

SKILL.md Content

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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

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

markdown

# 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

Script	Description	Usage
`scripts/rfm_score.py`	Score customers on R/F/M and assign segment labels	`python scripts/rfm_score.py --help`

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
```