stg-segmenting-customers

name	stg-segmenting-customers
description	Generates strategic customer segment definitions with observable filters, pain scoring, and compression-model elimination. Use when constructing segment hypothesis during BUILD phase 2.
serves	strategist
domain	customer-segmentation
affects	segment-hypothesis
depends-on	none
produces	segment hypothesis in register format

Customer Segmentation

Generate segment hypothesis using compression model: enumerate candidates, score, eliminate, carry alternatives. All segments defined by observable filters with tier-labeled evidence.

Procedure

Step 1: Load Context [S]

Read: market sizing notes (TAM/SAM/SOM), governor problem space description, research signals from BUILD phase 1.

Produce: segment generation parameters (industry focus, market scope, problem type).

Gate: context_loaded: bool -- market data and problem space available.

• Pass: Step 2.
• Fail: Report which inputs are missing. If market sizing is unavailable, proceed with governor input and research signals, noting reduced confidence.

Step 2: Enumerate Candidate Segments (Minimum 3) [K-grounded]

Grounded in: market data, problem space, research signals.

From market research, identify 3-5 potential customer groups. For each:

• Who they are (role, company type, industry)
• Why they might have the problem (structural reason)
• Rough size from TAM/SAM data

WebSearch for segment-specific signals if needed (job postings, forum activity, tool adoption patterns).

Produce: candidate segment list with initial evidence.

Gate: candidates_enumerated: bool -- at least 3 candidates identified, each with structural rationale for why they might have the problem.

• Pass: Step 3.
• Fail: If fewer than 3 candidates, broaden scope (adjacent industries, different company sizes, different roles). If still <3, document why the market may be narrower than expected.

Step 3: Define Observable Filters for Each Candidate [R]

For each segment, identify 2-4 searchable criteria.

Valid Filters (Observable)	Invalid Filters (Reject)
Company size (employees, revenue)	"Innovative companies"
Industry (NAICS code, vertical)	"Growth-minded founders"
Technology used (specific tools, platforms)	"Tech-savvy teams"
Geography (region, country)	"Forward-thinking"
Behavioral signals (job postings, tool usage)	Any psychographic or subjective filter
Funding stage (from Crunchbase)	"Quality-focused"

For invalid filters, replace with observable proxies:

• "Tech-savvy" -> "uses [specific tools]"
• "Growth-oriented" -> "raised funding in last 12 months" or "headcount grew >20% YoY"

Produce: filtered candidate list. Candidates with only invalid filters are weakened but not eliminated -- flag for governor input.

Gate: filters_defined: bool -- each candidate has at least 2 observable, searchable filters.

• Pass: Step 4.
• Fail: Candidates without observable filters cannot be targeted. Flag as "segment hypothesis requires governor input to operationalize."

Step 4: Score Pain Intensity for Each Candidate [R]

For each segment, assess pain signal strength from public data:

Score	Level	Evidence Required
5	Hair-on-fire, actively buying solutions	G2 reviews, competitor traction, job postings for manual workaround roles
4	Recognized pain, some buying	Forum complaints, budget line items, partial solutions adopted
3	Latent pain, workarounds common	DIY tools, spreadsheet solutions, occasional complaints
2	Mild inconvenience	Mentioned but not prioritized
1	Unaware or accepting	No public signal

Every score requires cited evidence. No score without signal. If no signal found, score = 1 with note: "no public signal found -- T3 assumption."

Tier labels: scores based on direct observation of behavior (reviews, job postings) = T1. Scores inferred from indirect signals = T2. Scores assumed without signal = T3.

Produce: scored candidate list, each score with evidence and tier label.

Gate: candidates_scored: bool -- every candidate has a pain score with cited evidence and tier label.

• Pass: Step 5.
• Fail: Candidates without evidence get score 1 (unaware). Do not assign higher scores without signals.

Step 5: Eliminate Weak Candidates (Compression) [R]

Eliminate candidates that fail on 2+ of:

• Pain score < 3
• Observable filters are insufficient (cannot find them)
• Market size too small (segment x estimated ARPU < operating costs)
• No public signal of the problem in this segment

Record elimination rationale with evidence for each eliminated candidate.

Produce: surviving segments + elimination log.

Gate: compression_complete: bool -- elimination rationale documented for every eliminated candidate.

• Pass: Step 6.
• Fail: If all candidates eliminated, broaden search. If only 1 survives, note the closest eliminated candidate as "weak alternative."

Step 6: Select Primary and Carry Alternatives [K-grounded]

Grounded in: scored candidates from Steps 4-5.

Primary = highest pain score with adequate market size.

If two segments tie, carry both and flag as governor decision.

At least 1 alternative must survive. If all but one eliminated, note the closest eliminated candidate as "weak alternative."

Produce: segment hypothesis with possibility space (primary, alternatives, eliminated).

Gate: selection_complete: bool -- primary identified, at least 1 alternative carried.

• Pass: Step 7.
• Fail: If no alternative can be carried, document: "Single viable segment identified. Compression model produced no alternatives. This is a concentration risk."

Step 7: Write Hypothesis [S]

Write complete segment hypothesis in register format:

• Claim: One paragraph -- who has this problem acutely, observable characteristics. Use behavioral language, not demographic generalization.
• Evidence: Tier-labeled evidence items from scoring.
• Possibility Space: Candidates considered, eliminated (with rationale), alternatives carried.
• Assumptions: What must be true about this segment (each with tier label, load-bearing flag, blast radius).
• Kill Condition: What would prove this is the wrong segment. Must be specific and observable (not "segment doesn't work"). Example: "Interviews with 5 [segment] companies reveal <2/5 experience this problem weekly."

Produce: complete segment hypothesis in register format.

Gate: hypothesis_written: bool -- all fields populated, kill condition is specific and observable.

• Pass: Done.
• Fail: Fix the missing or vague field.

Quality Criteria

• Minimum 3 candidate segments considered (possibility space recorded)
• At least 1 alternative carried (not just the winner)
• Every segment defined by observable, searchable filters (no psychographics)
• Pain scores backed by cited evidence with tier labels
• Elimination rationale documented for every eliminated candidate
• Kill condition is specific and observable (not "segment doesn't work")
• Claim uses behavioral language, not demographic generalization

Failure Modes

Mode	Signal	Recovery
Single-player assumption	Primary segment matches governor's own profile exactly	Flag to governor: "You may be designing for yourself. Is this segment defined by your characteristics or by market evidence?" Carry an alternative that explicitly differs from governor profile
Psychographic filters masquerading as observable	Filters include "tech-savvy", "growth-oriented", "innovative"	Replace with observable proxies: "tech-savvy" -> "uses [specific tools]"; "growth-oriented" -> "raised funding in last 12 months" or "headcount grew >20% YoY"
Pain score without evidence	Score assigned but evidence field is empty or says "likely" / "probably"	WebSearch for specific signals. If no signals found, score = 1 (unaware) with note: "no public signal found -- T3 assumption"
Anchoring on first segment	First segment listed gets highest score; alternatives feel perfunctory	Re-score with the second segment as the assumed primary. Does the scoring change? If not, anchoring is not the issue. If yes, the scores are framing-dependent -- carry both

Boundaries

In scope: Segment enumeration, observable filter definition, pain scoring with evidence, compression-model elimination, possibility space recording, hypothesis writing in register format.

Out of scope: Market sizing (stg-sizing-markets), problem scoring (stg-scoring-problems), competitive analysis (stg-analyzing-competition), customer validation interviews.