Ecom Rfm Analysis

What it does

When your agent uses it

• The user needs to segment customers by purchase behavior
• Identify high-value buyers
• Design retention campaigns
• Prioritize marketing spend by customer value — even if they say who are our best customers

Example prompts

• “who are our best customers”
• “which customers are at risk of churning”
• “how do we target our marketing”
• “/ecom-rfm-analysis”

Requirements

• Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

1. 1For each dimension, rank all customers and divide into 5 equal groups (quintiles)
2. 2Score 5 (best) to 1 (worst): R=5 means most recent, F=5 means most frequent, M=5 means highest spend
3. 3Combine into 3-digit RFM score (e.g., R5-F4-M5 = recent, frequent, high-value)

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ecom Rfm Analysis loads about 1.3k tokens when it runs, and up to ~7.8k if it reads all its reference files. Until then it costs about 104 tokens; SKILL.md has 459 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~104

When it runs · the whole SKILL.md, loaded when a task matches

~1.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~7.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 459 words, ~1,342 tokens.

name

ecom-rfm-analysis

description

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'.

metadata.category

WP-01 電商

metadata.tags

e-commerce, rfm, segmentation, customer-analytics

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.

# 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 |

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in ecom-rfm-analysis of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_input.json
• references/clv-prediction.md
• references/rfm-implementation.md
• scripts/rfm_score.py

Open the folder on GitHubat commit 4e7f4f8