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eighty-twenty-customer-value-rfm

eighty-twenty-customer-value-rfm

github.com/lvtd-llcMarketing & growth160 installsUpdated 7w ago
Repository

About eighty-twenty-customer-value-rfm

A customer-value segmentation skill for identifying which customers, buyers, leads, or accounts merit more attention. It scores recency, frequency, spending, responsiveness, and fit, then recommends treatment for each segment.

Key features

  1. 01Define the customer set and time window
  2. 02Score recency, frequency, money, and fit
  3. 03Group customers by value and potential
  4. 04Account for effort, margin, and fit
  5. 05Recommend actions by segment

Use cases

  1. 01Prioritize high-value customers for outreach
  2. 02Identify buyers with premium potential
  3. 03Plan nurture or requalification by segment
  4. 04Find low-value or costly relationships

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lvtd-llc/skills --skill eighty-twenty-customer-value-rfm

After installing, type/eighty-twenty-customer-value-rfmin Claude Code to confirm it loaded.

Details

From the project README

80/20 Customer Value RFM

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.

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