fnd.r-segmenting-customers
npx skills add https://github.com/bellabe/lean-os --skill fnd.r-segmenting-customers
Agent 安装分布
Skill 文档
Customer Segmenting
Generate strategic customer segment definitions for strategy/canvas/04.segments.md.
Prerequisites
Before proceeding, verify:
strategy/canvas/03.opportunity.mdexists (TAM/SAM/SOM data required)
If missing, inform user:
Canvas 03.opportunity.md required before defining segments.
Use fnd-researcher agent to establish market sizing first.
Optional context (read if exists):
strategy/canvas/01.context.mdâ KBOS frameworkstrategy/canvas/05.problem.mdâ Problem severity data
Core Principle
Segments must be observable and strategic:
| Criterion | Test |
|---|---|
| Observable | Can identify via searchable database query |
| Sizeable | Market size estimable from public data |
| Accessible | Reachable through known channels |
| Differentiable | Distinct needs from other segments |
Process
1. Load Context
Read available canvas files:
strategy/canvas/03.opportunity.md # Required: TAM/SAM/SOM
strategy/canvas/01.context.md # Optional: strategic context
strategy/canvas/05.problem.md # Optional: pain data
Extract: market size, trends, existing customer hypotheses.
2. List Segment Hypotheses
From market research, identify 3-5 potential customer groups.
For each, capture:
- Who they are (role, company type)
- Why they might buy (problem fit)
- How big the group is (rough estimate)
3. Define Observable Filters
For each segment, identify 2-4 searchable criteria.
Valid filters (can query in databases):
- Company size: “50-200 employees”
- Industry: “E-commerce, NAICS 454110”
- Technology: “Uses Shopify Plus”
- Geography: “US-based, tier-1 cities”
- Behavior: “Monthly GMV >$100K”
Invalid filters (not searchable):
- “Innovative companies”
- “Growth-minded founders”
- “Customer-centric organizations”
See references/filters.md for comprehensive examples.
4. Score Pain Intensity
Rate each segment’s pain 1-5:
| Score | Signal |
|---|---|
| 5 | Hair-on-fire, actively buying solutions |
| 4 | Significant pain, budget exists |
| 3 | Recognized problem, no urgency |
| 2 | Mild inconvenience |
| 1 | Unaware of problem |
Require evidence for each score â job postings, market reports, interview quotes.
See references/scoring.md for detailed rubric.
5. Estimate Segment Size
For each segment, calculate:
- Total matching filters (from industry data)
- Portion within SAM (addressable)
- Derivation source (cite report or calculation)
Use 03.opportunity.md TAM/SAM as ceiling.
6. Prioritize Segments
Rank by: Pain Intensity à Willingness to Pay à Accessibility
Select:
- 1 Primary (P0) â Immediate focus, highest score
- 1-2 Secondary (P1) â Expansion path
Document rationale for prioritization.
7. Write Output
Format per references/template.md.
Write to: strategy/canvas/04.segments.md
Quality Checklist
Before writing output, verify:
- Each segment has 2+ observable, searchable filters
- No psychographic traits in filters
- Segment sizes quantified with sources
- Pain scores have evidence justification
- 1-3 segments total (not 5+)
- Clear prioritization rationale
- Cross-references 05.problem.md if exists
Common Mistakes
| Mistake | Example | Fix |
|---|---|---|
| Too many segments | 5+ with blurry boundaries | Consolidate to 1-3 focused segments |
| Vague sizing | “Large market” | “~12,000 US companies matching filters” |
| Missing pain evidence | “Pain: 4” | “Pain: 4 â 340 job postings for this role” |
| Psychographic filters | “Forward-thinking retailers” | “Retailers >$1M GMV on modern platforms” |
| No prioritization logic | “Both equally important” | “Primary: highest pain (5) + proven WTP” |
Output Location
strategy/canvas/04.segments.md
Boundaries
- Does NOT validate segment existence (requires outreach)
- Does NOT guarantee segment accessibility
- Does NOT interview customers (provides framework)
- Segment sizes are estimates from available data
- Pain scores require evidence â flag when assumed
- Does NOT handle persona creation (behavior, not demographics)
- Observable filters must be searchable in databases
- Psychographic traits are NOT valid filters
Resources
- Output template: references/template.md
- Filter examples: references/filters.md
- Scoring rubric: references/scoring.md