​

Playbook

​

Dropleads Playbook

​

1) Start with low-cost discovery

• Use dropleads_get_lead_count to size the audience before any paid call.
• Use dropleads_search_people to inspect masked contacts and validate ICP filters (free).
• Use dropleads_search_people after you already have target account domains, by passing filters.companyDomains. It is a contact search primitive, not a dependable way to discover target accounts.
• Do not build joins or account-discovery flows that depend on every returned lead having companyDomain. Treat returned companyDomain as optional; if you need account domains as the source of truth, use a company-native search/enrichment tool first.
• Tighten filters until sample rows clearly match role, industry, and geo expectations.
• Key filter fields: filters.jobTitles, filters.seniority (C-Level/VP/Director/Manager/Senior/Entry/Intern), filters.industries, filters.departments, filters.companyDomains, filters.employeeRanges, filters.personalCountries.include (for geo), pagination.page, pagination.limit.

​

Filter best practices

{
  "filters": {
    "companyDomains": ["microsoft.com"],
    "jobTitles": ["CTO", "VP Engineering"],
    "seniority": ["C-Level", "VP", "Director"],
    "personalCountries": { "include": ["United States"] }
  },
  "pagination": { "page": 1, "limit": 25 }
}

​

Geo filters are best-effort, not verified

• City-level is the loosest match and leaks. personalCities can return contacts whose stated city loosely matches even when their real location differs, and non-US contacts can appear under a US-city filter (e.g. a Bulgarian contact surfacing under personalCities: San Francisco + personalCountries: United States). Country/state are more reliable.
• Person vs. company location are different fields. personal* filters the contact’s own location; organization* filters the company HQ. Don’t conflate them — filtering a remote employee by company HQ city (or vice versa) drops or leaks legitimate matches.
• Verify geo when precision matters. Combine personalCountries/personalStates with personalCities, then post-filter the returned leads on their country/state/city (and exclude obvious mismatches) before trusting the result or spending on enrichment. Do not assume the filter alone guarantees the geo.

​

2) Escalate paid calls only for shortlisted targets

• Run dropleads_email_finder for contacts that passed the discovery pass.
• Run dropleads_mobile_finder only when phone is required for the workflow.
• Keep pilots small first, then scale after quality checks pass.

​

3) Gate outbound with verifier status

• Treat invalid, catch_all, and unknown as non-send by default.
• Treat valid as the only status that passes automatic send gates.
• Respect credits_charged in responses for post-execution billing accuracy.

​

4) Practical sequencing

1. Count segment (dropleads_get_lead_count).
2. Sample segment (dropleads_search_people).
3. Pre-score titles with run_javascript if looking for a specific profile (e.g. founders, GTM engineers).
4. Scrape LinkedIn profiles with apify_run_actor_sync for work history/signals (preferred over call_ai — structured data, faster, cheaper).
5. Extract signals with run_javascript from Apify output (e.g. founder detection, hiring signals).
6. Enrich emails via waterfall (dropleads_email_finder first, then other providers).
7. Verify candidate emails (dropleads_email_verifier or leadmagic_email_validation).
8. Expand only after pilot quality is confirmed.

​

5) Account discovery boundary