✨Leads Finder — Effortless Data Extraction

A cost-effective alternative to ZoomInfo, Lusha & Apollo. Get verified B2B emails, LinkedIn profiles, and rich firmographics at scale.

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🚀 What this actor does

Leads Finder generates targeted B2B contact lists using advanced filters (job title, Location/City, industry, tech stack, revenue, funding, etc.) and returns verified emails, LinkedIn URLs, and detailed company data — ready for CRM or outreach. Free plan note: Users on the free Apify plan can fetch up to 100 leads/run.

Support: codecrafter70@gmail.com

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🧭 Quick start (UI)

1. Open the actor, set 📁 File name / Run label (optional).
2. Choose your filters (e.g., 👔 Job Title = "Marketing Manager", 🌍 Location = "United States", 🏭 Industry = "SaaS").
3. Set #️⃣ Number of leads to fetch (default 50,000; leave empty to fetch all that match).
4. Click Run. When finished, download results from the Dataset tab or use the Overview table.

Location vs City: Use Location for a region/country/state. To target a single city, leave Location empty and use City only. (Same logic for the Exclude fields.)

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🧩 Input schema (fields you can use)

Fields accept arrays unless noted.

General

• fetch_count (integer, default 50000) — Max leads to fetch. Leave empty to fetch all matches.
• file_name (string) — Custom run label / export name.

People targeting

• contact_job_title / contact_not_job_title — Include/Exclude titles ("realtor", "software developer", "teacher", …).
• seniority_level — Founder, Owner, C-Level, Director, VP, Head, Manager, Senior, Entry, Trainee.
• functional_level — C-Level, Finance, Product, Engineering, Design, HR, IT, Legal, Marketing, Operations, Sales, Support.

Location (Include)

• contact_location — Region/Country/State (e.g., EMEA, United States, California, US).
• contact_city — One or more cities (use this instead of Location when you want city-level targeting only).

Location (Exclude)

• contact_not_location — Region/Country/State to exclude.
• contact_not_city — One or more cities to exclude.

Email quality

• email_status — validated, not_validated, unknown (prefill: validated)

Company targeting

• company_domain — Limit to specific domains (e.g., google.com, https://apple.com).
• size — 0–1, 2–10, 11–20, 21–50, 51–100, 101–200, 201–500, 501–1000, 1001–2000, 2001–5000, 10000+
• company_industry / company_not_industry — Include/Exclude industries.
• company_keywords / company_not_keywords — Include/Exclude free-text keywords.
• min_revenue, max_revenue — Revenue bands (100K → 10B).
• funding — Seed, Angel, Series A…F, Venture, Debt, Convertible, PE, Other.

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📤 Output schema (what you get)

Results are written to the run's Dataset and rendered in the Overview table with these columns:

Person

• first_name, last_name, full_name, job_title, headline, functional_level, seniority_level
• email (verified when available)
• linkedin (profile link)
• city, state, country

Company

• company_name, company_domain, company_website (link), company_linkedin (link), company_linkedin_uid
• company_size, industry, company_description
• company_annual_revenue, company_annual_revenue_clean
• company_total_funding, company_total_funding_clean
• company_founded_year, company_phone
• company_street_address, company_city, company_state, company_country, company_postal_code, company_full_address
• company_market_cap (if public)

Context

• keywords, company_technologies

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🔎 Examples

Example 1 — US SaaS marketing leaders

• contact_job_title: ["Head of Marketing","VP Marketing","CMO"]
• functional_level: ["marketing"]
• contact_location: ["united states"]
• company_industry: ["computer software","internet","information technology & services","marketing & advertising","saas"]
• email_status: ["validated"]
• fetch_count: 5000

Example 2 — UK CTOs

• contact_job_title: ["CTO","Head of Engineering","VP Engineering"]
• contact_location: ["united kingdom"]
• email_status: ["validated","unknown"]

Example 3 — Amsterdam city-only (no broader Location)

• contact_city: ["amsterdam"]
• contact_location: (leave empty)

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✅ Best practices

• Location vs City: Choose one. Use Location for region/country/state or leave it empty and use City for city-only targeting. Same rule for Exclude.
• Start broad, then narrow. Begin with Location + title, then add industry/revenue/funding.
• Use include & exclude. Quickly remove irrelevant sectors with company_not_industry / company_not_keywords.
• Prefer validated emails. Keep email_status = ["validated"] for outreach-ready lists; add unknown to increase volume.
• Deduplicate downstream. If you merge runs, dedupe by email → linkedin → (full_name,company_domain).
• Stay compliant. Use for B2B prospecting; follow GDPR/CCPA/PECR and local rules.

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🧪 Output view (in the Apify UI)

The Overview tab shows a sortable table with links for company_website, linkedin, and company_linkedin. Export any time to CSV/JSON/XLSX from the Dataset.

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🧰 API usage

Run via API with the same input JSON as the UI.

1. POST a run with your input JSON.
2. Poll for completion.
3. Fetch dataset items (JSON/CSV).

(See Apify docs for runs, datasets endpoints.)

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💵 Pricing & limits

• From $1.5 / 1,000 leads (cheaper than typical ZoomInfo/Lusha/Apollo seat pricing).
• On the free Apify plan, the platform caps at 100 leads/run.

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🧯 Troubleshooting

• Few or zero results? Loosen filters (remove company_not_*, broaden Location, allow unknown email status). Try title synonyms ("Demand Gen" vs "Growth Marketing").
• Too many results? Add industry, revenue, funding, or switch from region to country/state/city.
• Geography mismatches? Don't mix broad regions with countries/states/cities; use either Location or City for the same target.

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✨ Changelog (high level)

• v1.1 — Simplified location filters: replaced Region/Country/State trio with Location (Region/Country/State) + City; mirrored for Exclude.
• v1.0 — Initial release with People/Company/Tech/Revenue/Funding filters, validated email preference, and LinkedIn enrichment.