LinkedIn Employee Count / Company Growth Tracker

Track LinkedIn company employee counts, follower counts, company size, industry, and simple growth deltas from public company pages — one clean, flat, CSV-ready row per company. Built for B2B sales, recruiting, market research, and competitor / investor monitoring.

No LinkedIn login, no cookies, no session IDs. The actor reads LinkedIn's public guest company pages over HTTP, so it stays fast and cost-predictable. You pay one flat event per unique company row that passes your filters.

This is a company-level growth tracker, not a people scraper and not an AI company-monitoring report. It does not scrape individual employee profiles.

✨ Why this scraper

• Company-level signals, not profiles — current employee count, followers, size band, industry, HQ, website, plus growth deltas vs prior runs.
• Two input modes — paste LinkedIn company URLs, or just company slugs (openai).
• Automatic growth tracking — run it on a schedule and it remembers each company's last counts, computing *_delta, *_delta_percent, a growth_label, and a transparent growth_score on the next run. No paste-back, no setup.
• 28 flat fields — no nested objects; drops straight into Sheets/Excel/CRMs.
• Pay-Per-Event — one flat company-growth-result event per saved unique company. Duplicates, filtered, not-found, and blocked rows are never charged.
• Transparent growth score — rule-based (no AI), explained below.

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🚀 Quick start — sample inputs

Example 1 — track companies by URL + slug

{
    "companyUrls": ["https://www.linkedin.com/company/atlassian/", "https://www.linkedin.com/company/canva/"],
    "companySlugs": ["openai"],
    "maxResults": 100,
    "minEmployeeCount": 50,
    "includeDescription": true,
    "deduplicate": true,
    "compareWithPreviousData": true,
    "proxyConfiguration": { "useApifyProxy": true }
}

Example 2 — filter a watchlist, custom residential proxy via your own provider

{
    "companySlugs": ["stripe", "notion", "figma", "databricks"],
    "maxResults": 250,
    "minEmployeeCount": 200,
    "industryKeywords": ["software", "technology"],
    "growthFilter": "any",
    "includeDescription": false,
    "deduplicate": true,
    "proxyConfiguration": {
        "useApifyProxy": false,
        "proxyUrls": ["http://user:pass@proxy.iproyal.com:12321"]
    }
}

Provide at least one of companyUrls or companySlugs. If you provide both, they are merged and deduplicated across the whole run.

The actor blocks Apify Residential proxy; if you need residential routing, supply your own provider via proxyConfiguration.proxyUrls. See 🚦 Proxy policy below.

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📦 Output

The dataset has one view: Company growth — a 28-column flat table.

Output fields (28)

input_company, input_type, company_name, company_slug, linkedin_company_url, linkedin_company_id, employee_count, employee_count_text, company_size_band, follower_count, follower_count_text, industry, headquarters, country, website, description, previous_employee_count, employee_count_delta, employee_count_delta_percent, previous_follower_count, follower_count_delta, follower_count_delta_percent, previous_scraped_at, growth_label, growth_score, reason_tags, source_status, scraped_at.

Sample record — Company growth

{
    "input_company": "https://www.linkedin.com/company/atlassian/",
    "input_type": "url",
    "company_name": "Atlassian",
    "company_slug": "atlassian",
    "linkedin_company_url": "https://www.linkedin.com/company/atlassian/",
    "linkedin_company_id": "22688",
    "employee_count": 21120,
    "employee_count_text": "21,120 employees",
    "company_size_band": "10,001+ employees",
    "follower_count": 2487598,
    "follower_count_text": "2,487,598 followers",
    "industry": "Software Development",
    "headquarters": "Sydney, NSW",
    "country": "Australia",
    "website": "https://atlassian.com/",
    "description": "Atlassian powers the collaboration that helps teams accomplish what would otherwise be impossible alone. From space missions and motor racing to bugs in code and IT requests, no task is too large or too small with the right team, the right tools, and the right practices. Over 300,000 global companies and 80% of the Fortune 500 rely on Atlassian's software, like Jira, Confluence, Loom, and Trello, to help their teams work better together and deliver quality results on time.",
    "previous_employee_count": 21120,
    "employee_count_delta": 0,
    "employee_count_delta_percent": 0,
    "previous_follower_count": 2487577,
    "follower_count_delta": 21,
    "follower_count_delta_percent": 0,
    "previous_scraped_at": "2026-05-30T00:17:22.051Z",
    "growth_label": "stable",
    "growth_score": 65,
    "reason_tags": "employee_count_available|follower_count_available|previous_snapshot_matched|company_size_available",
    "source_status": "ok",
    "scraped_at": "2026-05-30T00:43:01.254Z"
}

Real output from a back-to-back run, so the deltas here are small and the label is stable. Over daily/weekly schedules the *_delta fields and growth_label (growing / declining) reflect real headcount and follower movement.

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🎯 Growth score

Transparent rule-based score (0–100) computed from extracted fields and run-over-run deltas — no AI, no external enrichment. Starts at 50, then:

Signal	Points
Employee count delta ≥ 10%	+25
Employee count delta ≥ 3% and < 10%	+15
Follower count delta ≥ 10%	+10
Follower count delta ≥ 3% and < 10%	+5
Employee count ≥ 50	+5
Employee count ≥ 200	+5
Company size band present	+5
Employee count delta ≤ -10%	−25
Employee count delta < -3% and > -10%	−15
Follower count delta ≤ -10%	−10
Follower count delta < -3% and > -10%	−5
Employee count missing	−10

Score is clamped to 0–100.

Growth label: growing (employee delta ≥ 3% or follower delta ≥ 10%) · declining (employee delta ≤ -3% or follower delta ≤ -10%) · stable (a previous count exists but no threshold met) · unknown (no previous count available).

reason_tags is a pipe-separated list explaining the row — e.g. employee_count_available, follower_count_available, previous_snapshot_matched, employee_growth_positive, follower_growth_positive, company_size_available, missing_previous_snapshot, missing_employee_count.

---

📈 Tracking growth over time

Growth deltas come for free across runs — no paste-back needed. With compareWithPreviousData on (default), the actor remembers each company's last-known employee/follower counts in a persistent store and diffs against them automatically on the next run:

1. First run — no prior data yet, so deltas are null and growth_label is unknown. Counts are saved to the store.
2. Next run(s) — each company is matched to its last-known counts (by canonical URL, then slug) and *_delta, *_delta_percent, growth_label, growth_score, and previous_scraped_at are computed. The store is then updated with the fresh counts.

Because the store keeps the last-known value per company, a company's baseline can come from an earlier run, not just the immediate previous one — so changing company lists and gaps are handled gracefully (a company with no prior data simply comes back unknown).

• historyStoreName — set this to isolate separate tracking sets (e.g. one per watchlist). Leave empty for a sensible per-task default. The store is account-scoped and behaves the same locally and on Apify.
• compareWithPreviousData: false — disables tracking entirely (no store reads/writes; all rows come back unknown).

Just schedule the actor (daily/weekly/monthly) and growth is tracked for you.

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💰 Pricing

Pay-Per-Event. One flat event per saved row (final per-event price is configured on the Apify console):

Event	Charged when
company-growth-result	Once per unique company row that passed all filters and was successfully written to the dataset.

So your bill is simply results_saved × price_per_event. The actor honors the user-configured per-run spending cap (Apify eventChargeLimitReached): it caps how many companies it fetches up-front to what the limit can pay for, and stops cleanly the moment the cap is reached during charging.

Not charged:

• Duplicates (deduplicated by company ID, canonical URL, slug, and name+website/HQ keys).
• Rows filtered out by employee / follower / industry / growth filters.
• Not-found, blocked, or failed company pages.

🚦 Proxy policy

Use Apify Datacenter proxy or no proxy for normal runs — both work for LinkedIn's public guest company pages at this actor's conservative concurrency.

Apify Residential proxy is not supported. The actor will fail at startup if proxyConfiguration.apifyProxyGroups includes RESIDENTIAL. Reason: in pay-per-event actors, residential bandwidth (~/GB) is billed to the developer, not the run user, so a single bandwidth-heavy run could exceed the per-result event revenue.

If you genuinely need residential routing, supply your own residential provider via the proxy editor's Custom proxy URLs field — that traffic goes through your provider, not Apify, and is unaffected:

http://user:pass@proxy.iproyal.com:12321
http://user:pass@proxy.brightdata.com:22225
http://user:pass@proxy.oxylabs.io:7777

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📊 Run summary

After each run, a RUN_SUMMARY entry is written to the key-value store:

{
    "inputs_total": 120,
    "unique_inputs": 115,
    "successful_inputs": 101,
    "failed_inputs": 8,
    "blocked_requests": 6,
    "raw_results_found": 104,
    "results_saved": 92,
    "duplicates_removed": 5,
    "filtered_out": 7,
    "previous_snapshots_matched": 65,
    "history_entries_loaded": 110,
    "history_entries_saved": 92,
    "growing_companies": 24,
    "stable_companies": 36,
    "declining_companies": 5,
    "unknown_growth_companies": 27,
    "charged_events": 92,
    "charge_failures": 0,
    "retry_count": 18,
    "runtime_seconds": 410,
    "scraped_at": "2026-05-25T05:00:00.000Z"
}

charged_events equals the number of successfully saved unique rows.

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⚙️ Filters

Filter	Effect
minEmployeeCount / maxEmployeeCount	Keep companies within the employee-count bounds. Missing counts are dropped when a bound is set.
minFollowerCount	Keep companies with at least this many followers. Missing counts dropped when > 0.
industryKeywords	Case-insensitive substring match on industry. Missing industry dropped when keywords given.
growthFilter	any / growing / stable / declining / unknown (meaningful once prior data exists, i.e. from the 2nd run on).
deduplicate	One row per canonical company (recommended ON).

Filters are applied after extraction and scoring, and before any dataset push or event charge.

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🚧 Limitations (V1)

• Public guest data only: no login, cookies, or member-only content. LinkedIn gates some company pages for guests; when a page is gated the company is reported as blocked/partial in the summary and not charged. Use Apify Proxy and keep runs modest for best coverage.
• Best-effort fields: employee_count, follower_count, company_size_band, industry, headquarters, and website populate only when LinkedIn shows them on the public page. Missing values are null and the row is marked partial.
• Growth deltas need a prior run — the first run for a given company has no stored baseline, so growth_label is unknown and delta fields are null; deltas appear from the second run onward (with compareWithPreviousData on).
• No people scraping: no employee lists, new-hire detection, department/location breakdowns, job-page analytics, contact/email enrichment, website crawling, or AI narratives.
• maxResults caps saved unique rows across the whole run.

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❓ FAQ

Do I need a LinkedIn account or cookies? No. The actor only reads LinkedIn's public guest company pages.

Why are some fields null / why is a row partial? LinkedIn does not always expose every field publicly, and some pages are gated for guests. The actor returns whatever is visible and marks incomplete rows partial.

How do I get growth deltas? Just run the actor more than once — with compareWithPreviousData on (default) it remembers each company's last counts and computes deltas automatically on the next run, no paste-back needed. See 📈 Tracking growth over time.

Can I pass just slugs? Yes — put them in companySlugs (e.g. openai). They become https://www.linkedin.com/company/<slug>/.

Can I export to CSV? Yes — every field is flat (no nested objects). Use Apify's CSV / Excel export, or call the dataset API with format=csv.

Will I get blocked? The actor uses conservative concurrency, realistic headers, session rotation, and retry/backoff. Default Apify Proxy is sufficient for typical small/medium runs. For large runs, split the list and/or supply your own proxy provider.

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🛠️ Technical notes

• Stack: Node.js 22 · Apify SDK 3 · Crawlee CheerioCrawler · Cheerio + native fetch. No browser.
• Surface: LinkedIn public guest company page (/company/<slug>/); parsed via JSON-LD, og: meta, and visible top-card / about text.
• Concurrency: min=1, max=5 (conservative; tune after real runs).
• Memory: 1 GB min · 2 GB default · 4 GB max.
• Proxy: Apify Proxy enabled by default; custom configs accepted; Apify Residential rejected at startup.