80/20 Customer Value RFM
SkillDev toolsSegment customers and lists by 80/20 value, responsiveness, recency, frequency, and money. Use when identifying best customers, prioritizing follow-up, finding high-value buyers, pruning low-value accounts, or deciding what customer data to measure.
Use 80/20 Customer Value RFM in Claude, ChatGPT or Ahel Desktop
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Then ask your AI: use the 80/20 Customer Value RFM skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by hashgraph-online/awesome-codex-plugins in plugins/LVTD-LLC/skills/skills/eighty-twenty-customer-value-rfm/SKILL.md and read by Ahel’s review.
Use this skill to find the customers, buyers, leads, or accounts that deserve disproportionate attention. It is based on 80/20 Sales and Marketing by Perry Marshall.
Source Traceability
Primary source: 80/20 Sales and Marketing, chapters 12, 19, 22, and 23.
- Chapter 12 covers premium buyer behavior and hidden customer value.
- Chapter 19 covers removing problem customers and weak performers.
- Chapter 22 covers choosing the few measurements that matter.
- Chapter 23 covers recency, frequency, money, and customer-value measurement.
Workflow
1. Define The Customer Set
Capture:
- Customer or lead population.
- Time window.
- Revenue, profit, or strategic value metric.
- Available behavioral data.
- Follow-up capacity.
If the data is weak, create a practical scoring plan instead of pretending the segmentation is precise.
2. Score RFM And Responsiveness
Score each customer or segment:
- Recency: how recently they acted or bought.
- Frequency: how often they act or buy.
- Money: how much they spend or can spend.
- Responsiveness: how quickly and strongly they respond.
- Fit: whether they match the desired customer profile.
3. Segment The List
Use these groups:
- Best buyers: high recent value, repeat behavior, strong fit.
- Expansion candidates: good fit with evidence of premium potential.
- Nurture: fit is plausible but timing or proof is weaker.
- Low-value tail: low fit, low value, high effort, or high support burden.
- Problem customers: negative margin, toxic behavior, or strategic drag.
4. Decide Treatment By Segment
Recommend different action by segment:
- Personal outreach.
- Premium offer.
- Referral request.
- Automated nurture.
- Requalification.
- Sunset, prune, or fire.
Output Format
# Customer Value Segmentation
## Data Available
- Population:
- Time window:
- Fields available:
- Data gaps:
## RFM Segments
| Segment | Recency | Frequency | Money | Responsiveness | Action |
|---------|---------|-----------|-------|----------------|--------|
## 80/20 Findings
- Top customers:
- Hidden expansion group:
- Low-value tail:
- Problem customers:
## Recommended Actions
1. [Action]
2. [Action]
3. [Action]
Quality Bar
- Do not treat all customers as equally valuable.
- Do not use revenue without considering effort, margin, or fit.
- Do not recommend firing customers without a clear reason.
- Separate premium potential from current spend.
- State what data would improve the segmentation.
Signals
- GitHub stars
- 1k
- Forks
- 316
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
eighty-twenty-customer-value-rfm- Source
- github.com/hashgraph-online/awesome-codex-plugins
github.com/hashgraph-online/awesome-codex-plugins