LinkedIn Lookalike Companies — Similar Companies Finder avatar

LinkedIn Lookalike Companies — Similar Companies Finder

Pricing

from $0.80 / 1,000 lookalike companies

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LinkedIn Lookalike Companies — Similar Companies Finder

LinkedIn Lookalike Companies — Similar Companies Finder

Find companies similar to the ones you give. Reads the public LinkedIn company page and returns the lookalikes LinkedIn lists for it, ranked by how many pages list each one (2 levels deep for a wider net), with new-since-last-run flags. Optional headcount, followers and website. No login.

Pricing

from $0.80 / 1,000 lookalike companies

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Developer

Northbell

Northbell

Maintained by Community

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1

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2 days ago

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Give it LinkedIn companies — linkedin.com/company/… URLs or just the handle — and get back the lookalike companies LinkedIn lists for them: name, industry, location, URL, and a closeness score (timesListed: how many of the pages it read list that company). Add depth: 2 to read the pages of the lookalikes too, and the companies that many neighbours point at rise to the top. $1 per 1,000 lookalike companies ($4 per 1,000 with company details switched on), no login, no cookies.

Run it again — or put it on a schedule — and every row also tells you what changed since the last run: isNewSinceLastRun (LinkedIn did not list this company last time), previousRank and rankChange (positive = moved up). On the first run these are null.

What you get

One row per lookalike company (type: "lookalike"), closest first.

FieldWhat it is
name, slug, url, kindthe company; kind is company, or showcase for a product or brand page
industry, locationas shown on the similar-page card (location is missing on some cards)
timesListed, listedBycloseness: how many of the pages read list this company, and the handles of those pages
rankposition in the list (1 = closest), by timesListed, then depth, then the order LinkedIn shows them (null on the companies you gave)
seed, depththe first company you gave that led to it, and how many steps away it is (1 = on a page you gave, 2 = on a lookalike's page, 0 = a company you gave)
isNewSinceLastRun, previousRank, rankChangethe change since your previous run (null on the first run)
enrichedtrue when the company's own page was read for details (only possible with Add company details on), otherwise false
employees, followers, website, sizeBand, founded, headquartersonly with Add company details on: read from the company's own page. employees is the headcount LinkedIn counts; sizeBand is what the company states about itself
enrichErroronly when details were asked for and that company's page could not be read
observedAtwhen the row was made

Companies that could not be read come back as type: "error" rows (seed, errorKind, message) and are free.

Example, measured on 7 Oct 2026: given Stripe and Adyen, Revolut and Wise are on both pages (timesListed: 2) and come first; the other 15 are on one page each.

Use it for

  • Account lists — start from your best customers, get the companies that look like them, and feed the handles into your CRM or enrichment
  • Competitor and market maps — see who LinkedIn puts next to a company, and who sits next to everyone (depth 2)
  • Watching a space — schedule it and see which companies newly appear in a competitor's neighbourhood

Input

{ "companies": ["stripe", "https://www.linkedin.com/company/adyen"], "depth": 1, "maxCompanies": 100, "enrich": false, "includeSeeds": false }
  • companies — LinkedIn company URLs or handles, one per line
  • depth — 1 (default) reads only your companies' pages; 2 also reads the pages of the lookalikes, roughly 10 times more pages
  • maxCompanies — the most lookalikes to return (default 100, up to 1,000); with depth 2 the Actor stops reading more pages once it has found that many
  • enrich — read each lookalike's own page for employees, followers, website, size band, founding year and headquarters
  • includeSeeds — also return a row for each company you gave (depth 0)

The change since the last run is remembered per set of companies and depth in a named key-value store in your Apify account (linkedin-lookalike-history), so the second run of the same input already shows it. Schedule it (Apify → Schedules) to keep watching.

Pricing

$1 per 1,000 lookalike companies, plus $0.01 per run. With Add company details on, each row whose company page was read costs $4 per 1,000 instead — one row is charged one price, never both. Error rows are free. A row whose own page could not be read comes back without the details and is charged the $1 price. The $0.01 run-start fee is the one charge every run pays, even one that returns nothing.

100 lookalikes cost $0.11; 100 with company details cost $0.41.

Notes

  • Public data only: what anyone sees on a company page without signing in. Companies, not people — no names of employees, no emails, no phone numbers.
  • The similar companies are LinkedIn's own list (usually 10 per page). The Actor adds the counting across pages, the ranking and the change tracking. Some company pages show no list; those come back as a free error row.
  • timesListed counts only the pages that were read. With depth 2, a small maxCompanies stops the reading early, so the counts are then based on fewer pages.
  • Pages are read at about 20 a minute (a polite, shared budget). Depth 1 with 100 companies given is 100 pages, about 5 minutes; depth 2 for one company and 100 lookalikes is roughly 10 to 20 pages; company details add one page per row. With company details on, rows are written closest first while the pages are read, so a stopped run still has the best ones.
  • If LinkedIn answers "too many requests", the Actor waits 5 seconds and then 10 seconds and tries again. If four pages in a row still fail, it stops reading more pages: companies you gave that were not read come back as free error rows, and lookalikes whose details were not read come back without the details (charged the $1 price) — run it again a little later.
  • location is empty on some cards, founded on companies that do not state it, employees on pages without a count (for example some showcase pages); headquarters is usually there. Empty fields are null, never guessed.
  • The change since the last run compares with the last earlier run that read the same pages: the same companies, the same depth, and at depth 2 the same maxCompanies (it decides how many pages are read). It counts what LinkedIn listed, not only the top maxCompanies. If a page that was read last time could not be read this time (LinkedIn refused it, or the company is gone), nothing fair can be compared and that run shows null; the next normal run compares with the last normal one, so one bad day does not turn the whole list "new". A company that is simply not on LinkedIn does not switch the comparison off.
  • School pages (linkedin.com/school/…) are accepted as input, but LinkedIn may refuse to show them without a sign-in (HTTP 999); such a school comes back as a free error row.
  • Websites and plain company names are not matched here: use Domain to LinkedIn Company URL to turn a list of websites into handles first.

For headcount and how fast a company is growing, see LinkedIn Company Scraper — Headcount, Employee Growth & Details — its rows already carry the 10 similar companies for each company you give; this Actor is for ranking and widening them across many companies.

For AI agents

{"companies": ["stripe"], "maxCompanies": 20} returns up to 20 lookalike companies as rows with name, slug, industry, location and timesListed; add "enrich": true for employees and website, "depth": 2 for a wider net. Rows have type: lookalike or error. Feed slug values back into companies to walk the graph one step further.

**Something broke or looks wrong?** Open an issue on this Actor's Issues tab and I'll look at it. **Found it useful?** A short review on the Store page helps other people find it.