list-segmentation

# 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 | ## Extruct API Operations This skill delegates all Extruct API calls to the `extruct-api` skill. For all Extruct API operations, read and follow the instructions in `skills/extruct-api/SKILL.md`. The only Extruct operation in this skill is fetching enriched table data. Everything else is pure reasoning. ## Workflow ### Step 1: Load data Use the extruct-api skill to fetch enriched table 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: 1. **Enrichment data alignment** — do the enrichment column values match the hypothesis's "best fit" description? 2. **Signal strength** — how many enrichment columns have useful data (not N/A)? 3. **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):** ```markdown ## 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](references/tiering-framework.md) for the detailed tiering decision matrix.