LinkedIn Company About Scraper With Firmographic Enrichment

LinkedIn Company Scraper — Firmographics, Employees and HQ Data

LinkedIn Company About Scraper With Firmographic Enrichment turns a list of LinkedIn company URLs or handles into a complete firmographic dataset — industry, specialties, founded year, organization type, structured headquarters, numeric employee count, follower count, and a stable company identifier — all from the public "About" page, no login required. It's built for B2B sales teams qualifying accounts, market researchers segmenting companies by size and industry, and data teams enriching a CRM with reliable firmographic data.

What is LinkedIn Company About Scraper With Firmographic Enrichment?

LinkedIn Company About Scraper With Firmographic Enrichment is an Apify Actor that fetches a LinkedIn company page's public "About" section and extracts every firmographic field it contains — no LinkedIn login required. Key capabilities:

• Scrape one or many company pages per run, by full URL or bare handle
• Get structured headquarters (city, state, country), numeric employee count and range, and a company-size bucket
• Get founded year, computed company age, organization type, industry category, and specialties
• Filter results by employee-count range, industry keywords (include or exclude), and organization type
• Get a dataCompletenessScore showing what fraction of firmographic fields were actually populated for each company
• Automatic proxy fallback (including a real-browser fetch path to reduce LinkedIn's HTTP 999 block response) on repeated blocks

What data can I extract with LinkedIn Company About Scraper With Firmographic Enrichment?

Field	Example Value	Use Case
company_name / companyUrn	"Microsoft" / "urn:li:organization:1035"	Identify the company with a stable ID across renames
domain / linkedin_url	"microsoft.com" / LinkedIn company URL	Cross-reference the company's own website
category / specialties	"Software Development" / "Cloud Computing, AI, ..."	Segment companies by industry and focus areas
organizationType	"Public Company"	Filter by legal/organizational structure
foundedYear / companyAgeYears	1975 / 51	Segment by company maturity
followerCount	24000000	Gauge LinkedIn audience size
hqCity / hqState / hqCountry	"Redmond" / "Washington" / "United States"	Structured location for geographic segmentation
employeeCount / employeeCountRange / employeeSizeBucket	221000 / {"start": 200000, "end": 500000} / "10,001+"	Numeric and bucketed company-size data
description / slogan	"Microsoft is a technology company..." / "Empower every person..."	Full company description and tagline
dataCompletenessScore	0.93	See exactly how complete this company's firmographic record is
jobsSearchUrl	LinkedIn jobs search link	Direct link to the company's open roles

Numeric employee data and the completeness score

employeeCount and employeeCountRange are parsed into real numeric values from LinkedIn's own displayed employee range text, not left as an unusable string — employeeSizeBucket gives a standard size category for quick segmentation. dataCompletenessScore is a genuine computed metric: the fraction of firmographic fields actually populated for that company (0.0-1.0), rounded to two decimals — not every company's LinkedIn page publishes every field (a small or newer company may have no specialties or follower data listed), so this score tells you exactly how complete a given record is before you rely on it downstream.

Firmographic filtering after the fetch

minEmployeeCount/maxEmployeeCount, industryKeywords/excludeIndustryKeywords, and organizationTypeFilter are all post-fetch filters applied to the real extracted data — every field they filter on comes from the company's own About page, not an external database.

Why not build this yourself?

LinkedIn's official API (Marketing Developer Platform) requires partner-program approval and doesn't expose arbitrary third-party company firmographic lookups to general developers. Building an equivalent tool yourself means handling LinkedIn's aggressive anti-bot HTTP 999 response (which this Actor works around with a real-browser fetch fallback), parsing the About page's employee-range text into real numeric values, and building the completeness-scoring and firmographic-filtering logic from scratch. All of that is already implemented in this Actor's source.

How to use data extracted from LinkedIn?

B2B sales and account qualification

Filter a target account list by minEmployeeCount/maxEmployeeCount and industryKeywords to confirm which accounts genuinely fit your ideal customer profile before a sales team spends time on outreach.

Market research and competitive segmentation

Compare employeeSizeBucket/category/foundedYear across a set of companies in the same industry to understand market structure — how many established versus newer players, and how company size correlates with industry sub-segment.

CRM and data enrichment pipelines

Run a list of existing CRM company records through this Actor to backfill missing firmographic fields, using dataCompletenessScore to flag records that still need manual review.

AI agents and automated pipelines

An agent can call this Actor to enrich a company name or URL into structured firmographic data before feeding it into a lead-scoring or account-research pipeline.

🔼 Input sample

Parameter	Required	Type	Description	Example Value
companyUrls	No	array	LinkedIn company URLs or bare handles	["https://www.linkedin.com/company/microsoft", "google"]
proxySettings	No	object	Proxy config; auto-falls back to residential on blocks	{"useApifyProxy": true}
minEmployeeCount / maxEmployeeCount	No	integer (min 0)	Skip companies outside this employee-count range; 0 = no limit	50, 5000
industryKeywords	No	array	Keep only companies whose industry text matches one of these	["software", "technology"]
excludeIndustryKeywords	No	array	Skip companies whose industry text matches any of these	["staffing"]
organizationTypeFilter	No	string (enum)	Keep only this organization type; any (default) keeps all	"publicCompany"
retryAttempts	No	integer (min 0)	Retries before giving up on a blocked/failed fetch	3 (default)
requestDelaySecs	No	integer (min 0)	Base delay between company page fetches	2 (default)

{
  "companyUrls": ["https://www.linkedin.com/company/microsoft", "google"],
  "minEmployeeCount": 1000,
  "industryKeywords": ["software", "technology"],
  "organizationTypeFilter": "publicCompany"
}

Common pitfall: the firmographic filters (minEmployeeCount, industryKeywords, etc.) are all post-fetch — the Actor still fetches and charges for every company page in companyUrls, then drops non-matching rows afterward, so filtering doesn't reduce request volume.

🔽 Output sample

Output is typed, normalized JSON — one row per company (exportable as JSON, CSV, or Excel).

{
  "company_name": "Microsoft",
  "companyUrn": "urn:li:organization:1035",
  "linkedin_url": "https://www.linkedin.com/company/microsoft",
  "domain": "microsoft.com",
  "category": "Software Development",
  "specialties": "Cloud Computing, AI, Productivity Software",
  "organizationType": "Public Company",
  "foundedYear": 1975,
  "companyAgeYears": 51,
  "slogan": "Empower every person and organization on the planet to achieve more.",
  "followerCount": 24000000,
  "logoUrl": "https://media.licdn.com/...",
  "croppedCoverUrl": "https://media.licdn.com/...",
  "hqCity": "Redmond",
  "hqState": "Washington",
  "hqCountry": "United States",
  "location": "Redmond, Washington, United States",
  "number_employees": "10,001+ employees",
  "employeeCount": 221000,
  "employeeCountRange": {"start": 200000, "end": 500000},
  "employeeSizeBucket": "10,001+",
  "associated_members": "1200000",
  "description": "Microsoft is a technology company...",
  "dataCompletenessScore": 0.93,
  "hasWebsite": true,
  "hasFollowerData": true,
  "hasSpecialties": true,
  "jobsSearchUrl": "https://www.linkedin.com/jobs/search?f_C=1035",
  "success": true,
  "scraped_at": "2026-07-26T14:02:11Z"
}

All 30 fields shown above are the Actor's real dataset columns — this is the full output shape, not a subset.

How do you filter and target specific companies?

industryKeywords/excludeIndustryKeywords is the category axis — include or exclude by industry text match. organizationTypeFilter narrows scope to a specific legal structure (public, private, nonprofit, etc.). minEmployeeCount/maxEmployeeCount is the genuine quality/size threshold, filtering on real numeric employee data extracted from each page. There's no volume cap here beyond the length of companyUrls itself, since this is a direct-lookup tool — you supply exactly the companies you want, and retryAttempts/requestDelaySecs tune reliability rather than scope.

{ "companyUrls": ["microsoft", "google", "amazon"], "minEmployeeCount": 10000, "organizationTypeFilter": "publicCompany" }

{ "companyUrls": ["https://www.linkedin.com/company/openai"], "industryKeywords": ["artificial intelligence"], "excludeIndustryKeywords": ["staffing"] }

{ "companyUrls": ["small-startup-handle"], "minEmployeeCount": 1, "maxEmployeeCount": 50 }

▶️ Want to try other LinkedIn scrapers?

Scraper Name	What it extracts
LinkedIn Company Employees Scraper With Growth Tracker	Employee lists with headcount growth tracking
LinkedIn Company Scraper: Search By Name, Keyword or URL	Company discovery/search by name or keyword
LinkedIn Profile Scraper	Individual profile experience and education data
LinkedIn Post Scraper & Comments Reactions	Company/individual post content with engagement data
LinkedIn B2B Email Scraper	Corporate email and phone leads sourced from LinkedIn
Instagram Profile Scraper & Account Privacy Checker	Comparable account-verification data for Instagram

How to extract LinkedIn data programmatically

Every run is a standard Apify Actor call — one API request with your token, JSON input, JSON results back.

Python example

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_API_TOKEN>")

run_input = {
    "companyUrls": ["https://www.linkedin.com/company/microsoft", "google"],
    "minEmployeeCount": 1000,
}

run = client.actor("<YOUR_USERNAME>/linkedin-company-about-scraper-with-firmographic-enrichment").call(run_input=run_input)

for company in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(company["company_name"], company["employeeCount"], company["category"])

Export to spreadsheets or CRM

Export the dataset directly as CSV or Excel, then map company_name/domain to your CRM's account fields and employeeCountRange/category to firmographic segmentation columns.

Is it legal to scrape LinkedIn company data?

Company firmographic data (industry, employee count, headquarters, founded year) is business data, not personal data tied to an individual — this is closer to public business-directory information than personal contact data. Public LinkedIn company "About" pages are visible to any logged-out visitor. Consult legal counsel for commercial applications involving bulk automated querying of LinkedIn's services.

❓ FAQ

How accurate is the numeric employee count?

It's parsed directly from LinkedIn's own displayed employee-range text into real numeric values — employeeCount and employeeCountRange reflect exactly what LinkedIn's About page shows, not a third-party estimate. Accuracy depends on how current LinkedIn's own displayed range is for that company.

What does dataCompletenessScore actually measure?

The fraction of firmographic fields that had a real, non-empty value for that specific company (0.0-1.0) — not every company's LinkedIn page lists every field (specialties, follower count, slogan, etc. are frequently missing for smaller companies), so this flags which records are more or less complete.

Does filtering reduce how many pages get fetched?

No — minEmployeeCount, industryKeywords, and organizationTypeFilter are all applied after the fetch. Every URL in companyUrls is still fetched and charged; the filters only control which rows survive into the final output.

How many companies can I scrape per run?

There's no fixed cap in the input schema — add as many company URLs or handles as needed to companyUrls.

Do I need a LinkedIn account to use this?

No — the Actor reads LinkedIn's public "About" page, which requires no login.

Does LinkedIn Company About Scraper With Firmographic Enrichment work with Claude, ChatGPT, and AI agent frameworks?

It has no dedicated MCP server registration in this Actor's current setup, but it is callable as a standard Apify API endpoint by any agent framework that can make an authenticated HTTP request and parse JSON.

How does this compare to other LinkedIn company scrapers?

No independently verifiable competitor listing could be confirmed at the time of writing, so no specific comparison is made here. This Actor's distinguishing feature is the numeric employee-count parsing and completeness-scoring layer, computed at no extra request cost beyond the base page fetch.

What happens when a company URL doesn't resolve?

The row is still pushed with success: false rather than being silently dropped, so you can see exactly which companies in your input list failed to resolve.

Conclusion

LinkedIn Company About Scraper With Firmographic Enrichment turns a company URL list into a structured, quality-scored firmographic dataset — real numeric employee data, structured headquarters, and a transparent completeness score, computed at no extra request cost. It's built for B2B sales teams, market researchers, and CRM enrichment pipelines that need reliable company data. Start a run with your company URLs to get firmographic data back in one pass.