Segment and Tier

Take an enriched table + hypothesis set and produce a tiered, segmented list. This decides WHO gets which message and in what order.

Inputs

Input	Source	Required
Enriched table	Extruct table ID (after `list-enrichment`)	yes
Hypothesis set	`claude-code-gtm/context/{vertical-slug}/hypothesis_set.md` or context file	yes
Context file	`claude-code-gtm/context/{company}_context.md`	recommended

Environment

Variable	Service
`EXTRUCT_API_TOKEN`	Extruct API

Before making API calls, check that EXTRUCT_API_TOKEN is set by running test -n "$EXTRUCT_API_TOKEN" && echo "set" || echo "missing". If missing, ask the user to provide their Extruct API token and set it via export EXTRUCT_API_TOKEN=<value>. Do not proceed until confirmed.

Base URL: https://api.extruct.ai/v1

Official API Reference

https://www.extruct.ai/docs

Workflow

Step 1: Load data

Fetch enriched table data via GET /tables/{table_id}/data. Parse all rows and their enrichment column values.

Read the hypothesis set file. Parse each hypothesis into:

Number and short name
Description with data points
Best-fit company type

Step 2: Match companies to hypotheses

For each company row, evaluate which hypothesis fits best. Consider:

Enrichment data alignment — do the enrichment column values match the hypothesis's "best fit" description?
Signal strength — how many enrichment columns have useful data (not N/A)?
Specificity — does the company's profile match the hypothesis narrowly or broadly?

Assign each company ONE primary hypothesis. If multiple fit, pick the strongest signal.

Decision framework:

For each company:
  1. Read all enrichment values
  2. For each hypothesis:
     - Does the company's vertical/industry match the "best fit"?
     - Do enrichment values confirm the hypothesis pain point?
     - Is there a specific data point that makes this hypothesis resonate?
  3. Pick the hypothesis with the strongest evidence
  4. If no hypothesis fits well, mark as "Unmatched"

Step 3: Assign tiers

Three tiers based on fit strength and data richness:

Tier	Criteria	Action
Tier 1	Strong hypothesis fit + data-rich (3+ enrichment fields populated) + clear hook signal	Personalized email via `email-response-simulation` review
Tier 2	Medium hypothesis fit OR data-rich but no clear hook	Standard templated email via `email-generation`
Tier 3	Weak fit OR missing data (2+ fields N/A) OR unmatched hypothesis	Hold for re-enrichment or different campaign

Tier 1 signals (any of these):

CEO/leadership made a public statement related to the hypothesis
Recent news directly relevant to the pain point
Hiring for roles that signal the hypothesis pain
High hypothesis fit score from enrichment (grade 4-5)

Tier 3 signals (any of these):

Most enrichment fields returned N/A
No hypothesis match above threshold
Company profile too generic to confidently segment

Step 4: Generate output

Output a segmented list in two formats:

Markdown table (for review):

## Segmented List: [Campaign Name]

### Tier 1 — [N] companies (personalized outreach)

| Company | Domain | Hypothesis | Tier Rationale | Hook Signal |
|---------|--------|-----------|----------------|-------------|
| [name] | [domain] | #[N] [name] | [why this tier] | [specific hook] |

### Tier 2 — [N] companies (templated outreach)

| Company | Domain | Hypothesis | Tier Rationale |
|---------|--------|-----------|----------------|
| [name] | [domain] | #[N] [name] | [why this tier] |

### Tier 3 — [N] companies (hold/re-enrich)

| Company | Domain | Issue |
|---------|--------|-------|
| [name] | [domain] | [what's missing] |

CSV (for email-generation):

Save to claude-code-gtm/csv/input/{campaign-slug}/segmented_list.csv with columns:

company_name, domain, tier, hypothesis_number, hypothesis_name, tier_rationale, hook_signal

Step 5: Review with user

Present summary stats:

Total companies: N
Tier 1: N (X%)
Tier 2: N (X%)
Tier 3: N (X%)
Unmatched: N

Ask:

"Does the tier distribution look right? (Typical: 10-20% Tier 1, 50-60% Tier 2, 20-30% Tier 3)"
"Any companies that should move tiers?"
"Ready to proceed to email-generation?"

Reference

See references/tiering-framework.md for the detailed tiering decision matrix.

list-segmentation