What makes RFM Segmenter notable?

991 downloads. Trust evidence available

How should RFM Segmenter be evaluated before use?

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What kind of evidence is visible on this page?

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RFM Segmenter

Segment your customer base using Recency, Frequency, and Monetary value scoring to identify Champions, Loyal Customers, At-Risk buyers, and Churned segments... Skill: RFM Segmenter Owner: leooooooow Summary: Segment your customer base using Recency, Frequency, and Monetary value scoring to identify Champions, Loyal Customers, At-Risk buyers, and Churned segments... Tags: latest:1.1.0 Version history: v1.1.0 | 2026-06-06T14:39:17.896Z | user Summary: Adds detailed reference materials and actionable frameworks for effective RFM segmentation and campaign planning. - Added four

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Rank

Safety

Downloads

991

Updated

Oct 11, 2026

Version

1.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 991 downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 991 downloadsadoption · observed Oct 11, 2026
Latest release: 1.1.0release · observed Jun 6, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.1.0release
Summary: Adds detailed reference materials and actionable frameworks for effective RFM segmentation and campaign planning. - Added four new support documents: checklist.md, output-template.md, scoring-methodology.md, and segment-action-playbook.md. - Removed the skill-card.md file. - Enhanced documentation for implementing and interpreting RFM segmentation. - Provides clearer guidance on scoring, campaign strategy, and output structure.
Observed Jun 6, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17974h9acjg4h7h5djv1hg51d83hcca:rfm-segmenter

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-leooooooow-rfm-segmenter/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

44,252 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: RFM Segmenter
description: Segment your customer base using Recency, Frequency, and Monetary value scoring to identify Champions, Loyal Customers, At-Risk buyers, and Churned segments — then generate targeted retention and reactivation campaigns for each group.
---

# RFM Segmenter

Most ecommerce stores treat all customers the same: one email blast, one discount, one message. RFM segmentation breaks your customer base into behaviorally distinct groups so you can send the right message to the right person at the right time. This skill takes raw order data, scores each customer on Recency (how recently they bought), Frequency (how often), and Monetary value (how much they've spent), assigns them to named segments, and recommends specific marketing actions for each group.

## Quick Reference

| Decision | Strong | Acceptable | Weak |
|----------|--------|------------|------|
| Data window | Rolling 12–24 months | Last 12 months fixed | All-time (biases toward old customers) |
| Scoring method | Quintile-based (1–5 per dimension) | Quartile-based (1–4) | Binary high/low |
| Segment naming | Named behavioral segments (Champions, etc.) | Numbered clusters | R/F/M score only |
| Monetary metric | Net revenue (post-refund) | Gross revenue | Order count only |
| Recency baseline | Category purchase cycle-adjusted | Fixed 30/60/90/180 days | No adjustment |
| Action specificity | Segment-specific campaigns and offers | Generic retention vs. win-back | Mass email to all |

## Solves

1. **Indiscriminate discounting** — Giving a 20% coupon to Champions who would have bought anyway destroys margin. RFM lets you reserve discounts for At-Risk and Lapsed segments where they're actually needed.
2. **Churn blindness** — Without recency scoring, you don't know a customer is slipping away until they're gone. RFM flags customers before they churn.
3. **Misallocated retention spend** — Marketing budgets spent on customers who already churn (Hibernating segment) have near-zero ROI. Redirect that spend to Promising and Needs Attention segments.
4. **Over-emailing engaged customers** — Sending 5 emails a week to Champions can flip them from loyal to annoyed. RFM helps you calibrate frequency by segment.
5. **Reactivation guesswork** — Win-back campaigns work best with personalized angles. RFM tells you which churned customers were once high-value and worth the spend.
6. **LTV forecasting** — Champions and Loyal Customers are your LTV anchors. Knowing their size helps you set realistic growth targets.
7. **Loyalty program design** — RFM reveals which behaviors (frequency vs. spend) actually predict long-term customer value in your category.

## Workflow

### Step 1 — Export and Clean Order Data
Pull a customer-level order export with: Customer ID (or email), Order Date, and Order Value (net of refunds). Set your analysis window (recommended: rolling 24 months for stable categories, 12 months for fashion/seasonal). Remove test orders, internal orders, and wholes

_meta.json

{
  "ownerId": "kn70fv0ehp50emedet9tx3fekd82pw3b",
  "slug": "rfm-segmenter",
  "version": "1.1.0",
  "publishedAt": 1780756757896
}

checklist.md

# RFM Segmenter — Quality Checklist

Use before deploying any RFM-driven campaign. All sections must be checked.

---

## 1. Data Quality (8 items)
- [ ] Order data exported from ecommerce backend (Shopify/WooCommerce/BigCommerce), not marketing platform
- [ ] Refunds and returns subtracted — using net revenue, not gross
- [ ] Test orders excluded (internal team purchases, dummy orders)
- [ ] Wholesale / B2B accounts excluded or flagged separately
- [ ] Analysis window clearly defined (start date and end date)
- [ ] Duplicate customer records merged (same customer, multiple emails/accounts)
- [ ] Cancelled orders excluded from frequency and monetary totals
- [ ] Order dates verified — no future dates, no clearly erroneous dates (e.g., year 1900)

## 2. Scoring Accuracy (7 items)
- [ ] Scoring method (quintile/quartile/tertile) matches customer base size
- [ ] Recency scored correctly: lower days = higher score
- [ ] Frequency scored correctly: more orders = higher score
- [ ] Monetary scored correctly: higher net spend = higher score
- [ ] Tied values handled (not leaving gaps in quintile buckets)
- [ ] Single-purchase customers (F1) correctly identified and not forced into multiple frequency buckets
- [ ] Segment boundaries documented and reproducible

## 3. Segment Assignment (5 items)
- [ ] All customers assigned to exactly one segment (no gaps or duplicates)
- [ ] Segment mapping rules documented and reviewed
- [ ] Champions segment represents recognizable top customers (spot-check 10 names)
- [ ] Lost/Hibernating segments represent recognizable lapsed customers (spot-check)
- [ ] Segment sizes pass the reasonableness check (Champions 5–15%, Lost <20%)

## 4. Campaign Readiness (8 items)
- [ ] Each segment has a defined campaign strategy (message angle, offer, channel)
- [ ] At Risk and Can't Lose Them campaigns are prioritized (highest ROI recovery)
- [ ] Champions campaign uses no discount (or minimal incentive only)
- [ ] Email audiences built in ESP (Klaviyo, Mailchimp) for each active segment
- [ ] Suppression list applied (Lost segment excluded from active sends)
- [ ] SMS list built for At Risk and Can't Lose Them segments (if applicable)
- [ ] Retargeting audiences uploaded to Meta/Google Ads for key segments
- [ ] A/B test designed for at least one segment campaign (message or offer variant)

## 5. Tracking & Reporting (6 items)
- [ ] Campaign UTM parameters set up per segment (utm_content=rfm-champions, etc.)
- [ ] Baseline conversion metrics documented before campaigns launch
- [ ] Revenue attribution plan defined (how will you measure each segment's campaign contribution?)
- [ ] Next RFM refresh date scheduled (30/60/90 days)
- [ ] Segment migration tracking set up (can you see who moved between segments?)
- [ ] KPIs defined per segment (e.g., At Risk: reactivation rate; New Customers: 90-day repeat rate)

## 6. Final Review (4 items)
- [ ] RFM output reviewed by someone who knows the customer base (does it "feel" right?)
- [ ] To

output-template.md

# RFM Segmenter — Output Template

## Analysis Summary
**Store:** [Store Name]
**Analysis Date:** [Date]
**Data Window:** [Start] – [End] ([N] months)
**Total Customers Analyzed:** [N]
**Scoring Method:** [Quintile / Quartile]
**Monetary Metric:** [Gross / Net Revenue]

---

## RFM Score Distribution

| Segment | Customer Count | % of Base | Avg R Score | Avg F Score | Avg M Score | Avg LTV | Avg Orders | Avg Days Since Purchase |
|---------|---------------|-----------|------------|------------|------------|---------|-----------|------------------------|
| Champions | [N] | [%] | [N] | [N] | [N] | $[N] | [N] | [N] |
| Loyal Customers | [N] | [%] | | | | $[N] | [N] | [N] |
| Potential Loyalists | [N] | [%] | | | | $[N] | [N] | [N] |
| New Customers | [N] | [%] | | | | $[N] | [N] | [N] |
| Promising | [N] | [%] | | | | $[N] | [N] | [N] |
| Needs Attention | [N] | [%] | | | | $[N] | [N] | [N] |
| About to Sleep | [N] | [%] | | | | $[N] | [N] | [N] |
| At Risk | [N] | [%] | | | | $[N] | [N] | [N] |
| Can't Lose Them | [N] | [%] | | | | $[N] | [N] | [N] |
| Hibernating | [N] | [%] | | | | $[N] | [N] | [N] |
| Lost | [N] | [%] | | | | $[N] | [N] | [N] |
| **Total** | **[N]** | **100%** | | | | **$[N]** | | |

---

## Revenue Concentration

| Segment | Revenue % of Total | Revenue at Risk (At Risk + Can't Lose) |
|---------|-------------------|---------------------------------------|
| Champions + Loyal | [%] | — |
| At Risk | [%] | $[N] |
| Can't Lose Them | [%] | $[N] |
| **Total Revenue at Risk** | | **$[N]** |

---

## Segment Migration (vs. Previous Quarter)

| Segment | Previous Count | Current Count | Change | Migration Rate |
|---------|---------------|--------------|--------|----------------|
| Champions | [N] | [N] | +/−[N] | [%] |
| At Risk | [N] | [N] | +/−[N] | [%] |
| Lost | [N] | [N] | +/−[N] | [%] |

---

## Campaign Action Matrix

| Segment | Priority | Message Angle | Offer | Channel | Cadence | Expected Response Rate |
|---------|----------|--------------|-------|---------|---------|----------------------|
| Champions | Maintain | Exclusivity / Early access | No discount (or free shipping) | Email + SMS | Monthly | 35–55% open rate |
| Loyal Customers | Upsell | Loyalty rewards / New arrivals | 5–10% VIP discount | Email | Bi-weekly | 30–45% |
| Potential Loyalists | Convert | Second purchase nudge | 10–15% one-time offer | Email | Weekly for 3 weeks | 15–25% |
| New Customers | Onboard | Welcome series / Brand story | 10% next order | Email | Days 1, 4, 7, 14 | 20–35% |
| Needs Attention | Retain | "We noticed you haven't visited" | 10–15% limited time | Email | 2x per month | 10–20% |
| At Risk | Win-back | "We miss you" sequence | 15–20% + free shipping | Email + SMS | 3-step sequence | 8–18% |
| Can't Lose Them | Reactivate | VIP personal outreach | 20–25% + bonus gift | Email + phone | Immediate | 15–30% |
| Hibernating | Low-cost reactivation | Mass "Still interested?" | 10–15% | Email only | Quarterly | 2–5% |
| Lost | Suppress

scoring-methodology.md

# RFM Scoring Methodology Guide

## Quintile vs. Quartile vs. Tertile — Which to Use

| Customer Base Size | Recommended Scoring | Segments Per Dimension |
|-------------------|--------------------|-----------------------|
| 50–200 customers | Tertile (1–3) | 3 |
| 200–500 customers | Quartile (1–4) | 4 |
| 500+ customers | Quintile (1–5) | 5 |
| 5,000+ customers | Quintile (1–5) or custom decile | 5–10 |

**Why it matters:** With 150 customers and quintile scoring, each bucket has ~30 people. That's workable. With 80 customers, each bucket has 16 — too small to run meaningful segment campaigns. Use tertile scoring for small lists.

---

## Step-by-Step Quintile Calculation

### 1. Recency Scoring (R)

Sort all customers ascending by days since last order (fewest days = best recency):

```
Total customers: 1,000
Quintile boundaries (20% each = 200 customers per bucket):

R5 (best): Days 0–18     → 200 customers
R4:         Days 19–35   → 200 customers
R3:         Days 36–58   → 200 customers
R2:         Days 59–105  → 200 customers
R1 (worst): Days 106+    → 200 customers
```

*Note: The exact day boundaries depend on your specific customer distribution. Calculate them from your actual data — don't use fixed cutoffs like "0–30 days = R5" because your purchase cycle may differ.*

### 2. Frequency Scoring (F)

Sort all customers descending by number of orders:

```
F5 (best): 8+ orders     → 200 customers (top 20%)
F4:         5–7 orders   → 200 customers
F3:         3–4 orders   → 200 customers
F2:         2 orders     → 200 customers
F1 (worst): 1 order      → 200 customers
```

*Edge case: If a large percentage of customers have exactly 1 order (common for new stores), expand F1 to include all single-purchase customers and score remaining customers across F2–F5.*

### 3. Monetary Scoring (M)

Sort all customers descending by net revenue:

```
M5 (best): $800+         → 200 customers (top 20%)
M4:         $400–799     → 200 customers
M3:         $200–399     → 200 customers
M2:         $100–199     → 200 customers
M1 (worst): <$100        → 200 customers
```

---

## Handling Edge Cases

### Tied Values
When many customers share the same value (e.g., 400 customers have exactly 1 order), you have two options:
1. **Assign all tied values to the same score bucket** — Acceptable if the tied group is <30% of your total.
2. **Random assignment within the tied group** — Distribute ties across adjacent buckets randomly to maintain even distribution.

### Customers With a Single Order
In most ecommerce stores, 40–60% of customers have exactly 1 order. Don't over-segment this group:
- All single-purchase customers get F1 by default
- Use R and M scores to differentiate within this group
- A customer with R5, F1, M5 is a "High-Value New Customer," distinct from R5, F1, M1 (just a new customer with small first order)

### New Store With <12 Months of Data
- Don't force a 12-month window. Use all available data.
- Your "Lost" segment will be empty or tiny — t

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