LinkedIn Firmographic Data Scraper

Construct comprehensive firmographic datasets from LinkedIn company pages. Retrieves 900+ structured data points per company — industry classification, headcount, funding history, investor details, geographic footprint, and organizational network — suitable for academic research, market analysis, and enterprise CRM enrichment.

Firmographic Coverage

Company Identity & Classification

• Legal company name, LinkedIn URL, vanity slug
• Company type: Public, Privately Held, Nonprofit, Educational, Self-Employed, Government Agency, Partnership, Sole Proprietorship
• Industry category (LinkedIn standard taxonomy)
• Specialties (free-text list)
• Founded year

Scale & Growth Indicators

• Current employee count (LinkedIn-reported headcount)
• Employee count range (standardized bucket: 1–10, 11–50, 51–200, 201–500, etc.)
• LinkedIn follower count
• Open job listings count (hiring signal)

Geography

• Headquarters city, state/region, country
• Additional office locations (where listed)

Financial Intelligence

• Funding rounds — all available: round type, announced date, amount raised, currency, lead investor names, co-investors
• Total funding amount (sum of disclosed rounds)
• Last funding date and round classification

Organizational Network

• Affiliated pages — subsidiaries, sister companies, regional pages
• Similar companies — LinkedIn's peer graph (competitive landscape signal)
• Featured content links

Input Schema

{
  "company_urls": [
    "https://www.linkedin.com/company/databricks/",
    "https://www.linkedin.com/company/snowflake/"
  ],
  "include_funding": true
}

Condensed Output Record

{
  "company_name": "Databricks",
  "linkedin_url": "https://www.linkedin.com/company/databricks/",
  "company_type": "Privately Held",
  "industry": "Software Development",
  "employee_count": 6200,
  "employee_count_range": "5001-10000",
  "founded_year": 2013,
  "headquarters_city": "San Francisco",
  "headquarters_country": "United States",
  "website": "https://databricks.com",
  "follower_count": 480000,
  "open_jobs": 312,
  "total_funding_usd": 3500000000,
  "last_funding_type": "Series I",
  "last_funding_date": "2023-09-14",
  "funding_rounds": [...],
  "specialties": ["Apache Spark", "Data Lakehouse", "MLflow"],
  "similar_companies": [...],
  "affiliated_pages": [...]
}

Applications

Market mapping: Build a structured dataset of all companies in a target vertical — filterable by size, location, and funding stage.

Investor research: Extract funding round history and investor rosters for deal flow analysis or portfolio benchmarking.

CRM enrichment: Pipe company LinkedIn URLs through this actor to populate firmographic fields in Salesforce, HubSpot, or your data warehouse.

Academic research: Construct longitudinal company datasets for organizational studies, industry analysis, or entrepreneurship research.

Pricing

$15 per 1,000 company records. Billed on successful results only.

FAQ

How complete is the funding data? Funding data is sourced from what companies have disclosed on their LinkedIn company page. Companies that have not updated their LinkedIn funding section will have incomplete or empty funding records.

Is headcount data verified? Employee counts are self-reported by companies on LinkedIn and may differ from actual headcount. The employee_count_range field provides a standardized bucket less susceptible to gaming.

Can I retrieve 10,000 company records in a single run? Yes, with appropriate Apify memory allocation. For very large batches, consider splitting across multiple runs to stay within runtime limits.

Does the data export include all nested objects? Yes. Funding rounds, affiliated pages, and similar companies export as nested JSON. When exported as CSV, these fields are stringified JSON arrays.