fnd-r-segmenting-customers

name	fnd.r-segmenting-customers
description	Generates strategic customer segment definitions with observable filters, segment sizing, and pain intensity scores. Use when defining target customers, building Canvas section 04, translating market research to segments, or when user mentions "segments", "ICP", "target market", or "who to sell to".
allowed-tools	Read Write WebSearch
license	Complete terms in LICENSE.txt

Customer Segmenting

Generate strategic customer segment definitions for strategy/canvas/04.segments.md.

Prerequisites

Before proceeding, verify:

• strategy/canvas/03.opportunity.md exists (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.md — KBOS framework
• strategy/canvas/05.problem.md — 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 — 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 × Willingness to Pay × Accessibility

Select:

• 1 Primary (P0) — Immediate focus, highest score
• 1-2 Secondary (P1) — 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 — 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 — 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