Lookalike Company Finder — Similar Companies to Your Customers

Your best next customers look like your best current customers. Lookalike Company Finder takes a few example domains — three customers, a segment, a competitor's logos page — builds a profile of what they have in common, and returns similar companies scored 0–100 with the reasons: shared keywords, industry, size band, tech overlap, geography, and how many competitor lists name them together. Every row carries the domain, description, HQ, employee band and LinkedIn page, read from the company's own website and public LinkedIn page. No login, no API key, no data vendor.

• Sales ops & ABM — turn "accounts like Acme" into a scored target list for the territory.
• SDRs & founders — expand a hand-picked list of 5 ideal customers to 50 more of the same shape.
• Agencies — find every local business that looks like the three you already serve in a city.
• Investors & researchers — map the competitive set around any company from its domain.

What does Lookalike Company Finder do?

It is a similar-companies finder driven by example domains, not by filters you have to guess:

1. Profiles the seeds. Reads each seed's website (title, description, keywords, headline text, structured data, tech stack), its public LinkedIn company page (industry, company size, headquarters, specialties) and its Product Hunt product (topics, team size).
2. Finds candidates. Web searches for X competitors, X alternatives, companies like X (Google, with Bing / DuckDuckGo fallbacks), the "10 best X alternatives" articles those searches surface (the companies they link and name), the Product Hunt topics the seeds sit in, and keyword searches built from the profile (sales crm companies, drain cleaning in Denver).
3. Reads every candidate the same way and scores it against the profile. The top maxResults are delivered, best first, each with matchedOn — the evidence in plain words — and a scoreBreakdown.

Why use this lookalike company finder?

• Explained scores. matchedOn: ["keywords: sales crm, pipeline, deals", "industry: Software Development", "tech: Segment, Intercom", "named in 3 competitor lists"] — you can see why a company is on the list before you spend a credit on it.
• Real fields or null. Industry, size and HQ come from the company's public LinkedIn page or its own structured data; nothing is inferred from the name. Signals without evidence are left out of the score, not guessed.
• Works beyond SaaS. Local services, agencies, manufacturers: the profile uses the seeds' own words and locations, so "three Denver plumbers" finds Denver plumbers.
• Pay per delivered row. Seeds, excluded domains, duplicates, unreachable sites and everything under your minimum score cost nothing.
• CRM-ready. Dataset + LOOKALIKES.csv; run it from the API, a schedule, Zapier / Make / n8n, or an AI agent through MCP.

What data does it extract?

Field	Description
company, domain, website	Company name (LinkedIn → site name → domain), registrable domain (the row key), home page URL
similarityScore	0–100 against the seed profile
matchedOn	Plain-words evidence: keywords, industry, tech, size, geography, competitor lists / topics naming it
scoreBreakdown	Per-signal scores 0–1 (keywords, industry, tech, size, geography, cooccurrence), null where there was no evidence, plus coverage
industry, industrySource	LinkedIn industry label (linkedin) or keyword-classified label (keywords)
employeeBand, employeesOnLinkedIn	1-10 … 10001+ from LinkedIn / structured data; the LinkedIn head-count when shown
hq, country	Headquarters (LinkedIn or JSON-LD address) and ISO country
techOverlap, techStack	Business technologies shared with the seeds; every technology fingerprinted on the site
description, tagline, keywords, specialties	What the company says it does; the terms it shares with the profile; LinkedIn specialties
linkedin, twitter, productHuntUrl, foundedYear	Public profiles and founding year when published
seedsMatched, foundVia, listicleMentions, sources	Which seeds it was linked to, how it was found (serp, listicle, producthunt, keyword), how many competitor lists name it, every source URL / query
scrapedAt	Provenance

How to find lookalike companies

1. Open the Actor and click Try for free.
2. Under Example customer domains paste 3–20 domains of companies you want more of.
3. Optionally set Locations (United States, Berlin, Germany), Company sizes (51-200, mid-market), Only companies in the seeds' industry, and Exclude domains (existing customers).
4. Click Start. A 20-row run takes 3–6 minutes (it reads ~80 pages); results arrive sorted by score.
5. Export CSV / Excel / JSON or open LOOKALIKES.csv. The OUTPUT record holds the seed profile (industries, key phrases, shared tech, sizes, countries) so you can check what the run understood your seeds to be.

Tips for better lookalikes

• Homogeneous seeds. Five sales-CRM vendors give a sharp profile; a CRM, a bakery and a law firm give mush. Run one segment at a time.
• Add the odd one out to excludeDomains and re-run — the profile is rebuilt every run.
• Raise minSimilarityScore to 50 when you only want the obvious peers; lower it to 15 for a wide net you will curate by hand.
• Local segments: give the seeds' cities in Locations (or let the run pick them up from the seeds' HQ) so keyword searches stay local.

How much does it cost?

Pay-per-event, no subscription: $0.03 per lookalike company delivered plus a small run-start fee. 50 lookalikes ≈ $1.50. Rows filtered out by your settings, duplicates, seeds and low scores are free. Apify's free plan covers a few test runs a month.

Input

Only Example customer domains is required. Example:

{
    "seedDomains": ["pipedrive.com", "close.com", "folk.app"],
    "maxResults": 20,
    "locations": ["United States"],
    "companySizes": ["11-50", "51-200", "201-500"],
    "excludeDomains": ["salesforce.com"]
}

Output

One dataset item per company:

{
    "company": "Freshsales",
    "domain": "freshworks.com",
    "website": "https://www.freshworks.com/",
    "similarityScore": 74,
    "matchedOn": ["keywords: sales crm, pipeline, deals", "industry: Software Development", "tech: Segment", "size: 5001-10000 employees", "geography: US", "named in 4 competitor lists; search result for a seed; linked to 2 seeds"],
    "scoreBreakdown": { "keywords": 0.81, "industry": 1, "tech": 0.4, "size": 0.25, "geography": 1, "cooccurrence": 0.9, "coverage": 1 },
    "industry": "Software Development",
    "industrySource": "linkedin",
    "employeeBand": "5001-10000",
    "hq": "San Mateo, California",
    "country": "US",
    "techOverlap": ["Segment"],
    "description": "Freshsales is an AI-powered sales CRM …",
    "linkedin": "https://www.linkedin.com/company/freshworks-inc",
    "seedsMatched": ["close.com", "pipedrive.com"],
    "foundVia": ["listicle", "serp"],
    "listicleMentions": 4,
    "sources": ["bing:Pipedrive alternatives", "listicle:zapier.com/blog/pipedrive-alternatives", "https://www.freshworks.com/", "https://www.linkedin.com/company/freshworks-inc"],
    "scrapedAt": "2026-09-26T14:02:11.000Z"
}

The key-value store also holds LOOKALIKES.csv and OUTPUT (run summary: seed profile, candidates found / read / dropped per filter, search and transport statistics, notices).

FAQ

Where do the similar companies come from?

From the public web, found the way an analyst would: competitor / alternative searches, the articles that list alternatives, Product Hunt topics and keyword searches. There is no vendor database behind it — which is why every row also carries the source it was found through.

Why is employeeBand or hq sometimes null?

They are read from the company's public LinkedIn page (linked from its website) or its structured data. A company without a linked LinkedIn page or with a LinkedIn auth wall keeps those fields null; the score is then computed on the remaining signals and scoreBreakdown.coverage shows it.

Does it need my LinkedIn or Google account?

No. LinkedIn company pages are read as a logged-out visitor sees them; web search uses Apify's Google SERP proxy with Bing / DuckDuckGo fallbacks.

Can I feed it company names instead of domains?

Not yet — domains are unambiguous. Use the company's main website domain.

Is this legal?

The Actor reads what companies publish about themselves on their own websites, their public LinkedIn company page and Product Hunt. No personal data is collected. Check your local rules before contacting the companies.

Support

Open an issue in the Issues tab with your run ID and the seeds that misbehaved. The API tab shows how to call this Actor from code or from any MCP-capable AI agent.