80/20 Customer Value RFM

SkillDev tools

Segment 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

Free. Sign in, add 80/20 Customer Value RFM and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

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.

Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

80/20 Customer Value RFMStart free

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