Stg Segmenting Customers

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.

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:

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

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.