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