Analyzing Won Deals CSV

• Sales/Marketing tech: 7 companies (30%)
• Fintech/Risk: 5 companies (22%)
• B2B Marketplaces: 4 companies (17%)
• Property/Real Estate tech: 3 companies (13%)
• Other: 4 companies (18%)

• 20-100 employees: 13 companies (57%)
• 100-300 employees: 8 companies (35%)
• 300+: 2 companies (8%)

• Enrichment/scoring workflows: 14 companies (61%)
• Search/discovery features: 7 companies (30%)
• Risk/compliance: 5 companies (22%)
• Matching/recommendation: 4 companies (17%)

• Larger companies (100+) → higher ACV ($4.8K avg)
• Enrichment use cases → higher usage volume

1. What’s your core value proposition?
2. Who sends these emails (name/role)?
3. Preferred tone for outreach?

claude-code-gtm/context/extruct_context.md

claude-code-gtm/context/win-case-analysis/hypothesis_set.md

# Seed 1: LeadEnrich (sales intelligence)
curl -X GET "https://api.extruct.ai/v1/companies/leadenrich.com/similar?limit=100"

# Seed 2: MarketCompass (market research)
curl -X GET "https://api.extruct.ai/v1/companies/marketcompass.com/similar?limit=100"

# Seed 3: PartnerMatch (marketplace)
curl -X GET "https://api.extruct.ai/v1/companies/partnermatch.io/similar?limit=100"

# Seed 4: RiskView (fintech risk)
curl -X GET "https://api.extruct.ai/v1/companies/riskview.io/similar?limit=100"

# Seed 5: SupplierIQ (procurement)
curl -X GET "https://api.extruct.ai/v1/companies/supplieriq.io/similar?limit=100"

# Query 1: B2B scoring/evaluation
curl -X GET "https://api.extruct.ai/v1/companies/search" \
  -d '{
    "q": "B2B software that scores, evaluates, or ranks companies for vendor assessment, lead qualification, or risk screening",
    "filters": {"include": {"employee_count": ["11-50", "51-200", "201-500"]}},
    "limit": 150
  }'

# Query 2: Sales/marketing enrichment
curl -X GET "https://api.extruct.ai/v1/companies/search" \
  -d '{
    "q": "sales intelligence and lead enrichment platforms that provide company data for B2B outreach",
    "limit": 150
  }'

# Query 3: Marketplace/matching platforms
curl -X GET "https://api.extruct.ai/v1/companies/search" \
  -d '{
    "q": "B2B marketplace or matching platforms connecting buyers and sellers or partners",
    "limit": 100
  }'

curl -X POST "https://api.extruct.ai/v1/tables/tbl_win_case_lookalike/columns" \
  -d '{...5 column configs...}'

curl -X POST "https://api.extruct.ai/v1/tables/tbl_win_case_lookalike/run"

## Win Case Lookalike Campaign - Segmented

### By Hypothesis:
- #1 B2B Scoring: 247 companies (36.2%)
- #2 Enrichment at Scale: 189 companies (27.7%)
- #3 Risk & Compliance: 98 companies (14.4%)
- #4 Discovery & Matching: 86 companies (12.6%)
- Multi-hypothesis: 42 companies (6.2%)
- Unmatched: 20 companies (2.9%)

### Tier 1 — 68 companies (10.0%)

Strong hypothesis match + API-forward (grade 4-5) + recent news/hook

| Company | Domain | Hypothesis | Hook Signal |
|---------|--------|------------|-------------|
| VendorScore | vendorscore.io | #1 B2B Scoring | Raised Series A ($8M) 2 months ago for "vendor intelligence" |
| ProspectIQ | prospectiq.com | #2 Enrichment | Blog: "Why we're moving from Clearbit to..." |
| FraudGuard | fraudguard.ai | #3 Risk & Compliance | Launched "Business Verification API" last quarter |

### Tier 2 — 436 companies (63.9%)

Medium fit, standard templated outreach

### Tier 3 — 178 companies (26.1%)

Low data quality or weak hypothesis fit, hold

claude-code-gtm/csv/input/win-case-lookalike/segmented_list.csv

• Hypothesis-specific P1 openers
• Tailored example queries
• Relevant social proof from your win cases

claude-code-gtm/prompts/win-case-lookalike/
├── h1_b2b_scoring_template.md
├── h2_enrichment_scale_template.md
├── h3_risk_compliance_template.md
└── h4_discovery_matching_template.md

# Email Prompt: Hypothesis #1 - B2B Scoring Workflows

## P1 Opener
If {{b2b_scoring_feature}} = "Yes - core feature":
  "I saw [Company] offers [specific feature] for scoring/evaluating companies. 
  We've worked with similar platforms on the data layer for these workflows."

If {{recent_funding_news}} mentions scoring/evaluation:
  "I saw [Company] recently [funding/product news]. We've helped similar 
  platforms add company intelligence to their scoring features."

## P2 Example Query
"Find B2B software vendors in supply chain with 50-200 employees, 
founded after 2018, with recent funding"

## P3 Social Proof
Choose based on {{use_case_category}}:
- If Vendor evaluation → "A procurement platform uses this to auto-score supplier stability."
- If Lead scoring → "A sales intelligence tool uses this to qualify enterprise leads."
- If Partner matching → "A marketplace platform uses this to verify partner companies."

## P4 Proof Point
"PS. The API is free for search (unlimited). Enrichment starts at $59/mo for 1K credits."

...

for each company in segmented_list:
  if tier == 1 or tier == 2:
    template = load_template_for_hypothesis(company.hypothesis_number)
    email = generate(template, company.enrichment_data)
    save_to_csv(email)

Subject: Company intelligence for vendor scoring

Hi [First Name],

I saw VendorScore raised $8M for vendor intelligence features. We've worked 
with procurement platforms on the company data layer for similar workflows.

Extruct is an API for company search and intelligence. Example query for 
vendor scoring:

"Find logistics software vendors in North America with 50-200 employees, 
founded after 2018, with recent funding rounds"

A procurement automation platform uses this to auto-score supplier stability 
before manual due diligence.

Worth exploring?

Danny
Extruct

PS. API is free for search (unlimited). Enrichment starts at $59/mo for 1K credits.

claude-code-gtm/csv/output/win-case-lookalike/
├── emails_h1.csv (247 emails for Hypothesis #1)
├── emails_h2.csv (189 emails for Hypothesis #2)
├── emails_h3.csv (98 emails for Hypothesis #3)
└── emails_h4.csv (86 emails for Hypothesis #4)

# Campaign 1: Hypothesis #1 - B2B Scoring
curl -X POST "https://api.instantly.ai/api/v1/campaign/create" \
  -d '{
    "name": "Win Case Lookalike - B2B Scoring",
    "from_email": "[email protected]"
  }'

# Upload leads for Campaign 1
for each lead in emails_h1.csv:
  curl -X POST "https://api.instantly.ai/api/v1/lead/add" ...

# Repeat for Campaigns 2-4

• Campaign 1 (H1 - B2B Scoring): 186 contacts
• Campaign 2 (H2 - Enrichment): 143 contacts
• Campaign 3 (H3 - Risk): 76 contacts
• Campaign 4 (H4 - Discovery): 99 contacts