Revgrowth1 Contact Discovery Pipeline
Domain -> LinkedIn Company URL -> ICP Decision-Makers -> Verified Work Emails. Uses a waterfall enrichment pattern with primary enrichment API and email fallbacks.
3-Step Pipeline
| Step | What It Does | Credits |
|---|---|---|
| 1 | Domain -> LinkedIn company URL | 1/domain |
| 2 | Waterfall ICP search -> decision-maker contacts | 1/contact found |
| 3 | Email waterfall (primary enrichment API -> LinkedIn enrichment API -> keyword search API) | 1/email (primary only) |
Default ICP Cascade (configurable)
| Tier | Titles |
|---|---|
| 1 | CEO, Founder, Owner, President, Co-Founder |
| 2 | VP Marketing, VP Sales, VP Growth, CMO, CRO |
| 3 | Marketing Director, Sales Director, Director of Growth |
Max 3 contacts per company. Custom cascade via --cascade-json.
Execution
# Standard run
python3 ./workflows/08-contact-discovery/contact_discovery.py input.csv -o contacts.csv
# Dry run (estimate credits without calling APIs)
python3 ./workflows/08-contact-discovery/contact_discovery.py input.csv --dry-run
# Limit companies, skip email step
python3 ./workflows/08-contact-discovery/contact_discovery.py input.csv -o contacts.csv --max-companies 50 --skip-email
# More contacts per company
python3 ./workflows/08-contact-discovery/contact_discovery.py input.csv -o contacts.csv --max-results 5
Input CSV
Needs a domain column (auto-detected from: domain, website, company_domain, url, website url, company website). Optional company / company name column.
Output
Creates {input}_enriched.csv with columns: company, domain, company_linkedin_url, first_name, last_name, full_name, job_title, linkedin_headline, person_linkedin_url, email, email_source, all_emails, country, person_city, icp_tier, what_matched.
Also saves raw JSON for debugging.
API Keys
Resolved: CLI flag -> env var -> ./.env.
| Key | Required | Purpose |
|---|---|---|
PRIMARY_ENRICHMENT_API_KEY |
Yes | All 3 steps |
LINKEDIN_ENRICHMENT_API_KEY |
No | Email fallback #1 |
KEYWORD_SEARCH_API_KEY + KEYWORD_SEARCH_API_SECRET |
No | Email fallback #2 |
Waterfall Enrichment Pattern
The key methodology here is waterfall enrichment: try the primary source first, then cascade through fallbacks. This maximizes coverage while minimizing cost (cheaper sources are tried first).
Primary Enrichment API (cheapest, broadest)
|-- Found? -> Done
|-- Not found? -> LinkedIn Enrichment API (higher quality for LinkedIn-sourced leads)
|-- Found? -> Done
|-- Not found? -> Keyword Search API (niche coverage)
|-- Found? -> Done
|-- Not found? -> Mark as "no email found"
Each source returns different confidence levels. Track email_source so you can analyze which sources produce the best deliverability rates downstream.