Investor prospecting lists from PitchBook and LinkedIn data
Fundraising research usually means either paying for a PitchBook or Crunchbase seat the founder only needs for a few months, or manually building a spreadsheet from investor websites and LinkedIn one profile at a time. Neither is a great use of the limited time founders have during an active raise.
Investor prospecting with public data means joining public investor profile data with public LinkedIn partner profiles into one targeted, sector- and stage-matched list, instead of paying for a full research-platform seat or building the list one profile at a time by hand.
Quick answer
Use PitchBook Investors Scraper to collect public investor profiles — firm name, focus areas, and stated investment criteria — without a PitchBook subscription. Use LinkedIn Profile Scraper to enrich individual partner or associate profiles with role, background, and current firm. Combine both into a prospecting list segmented by sector and stage fit.
The investor prospecting stack
The stack uses two CrawlerBros Actors:
- PitchBook Investors Scraper for public investor profiles without a subscription.
- LinkedIn Profile Scraper for public LinkedIn profile information without login.
The workflow:
\
Sector and stage criteria
-> PitchBook Investors Scraper (firm-level investor profiles)
-> LinkedIn Profile Scraper (partner-level enrichment)
-> segmented investor prospecting list
\\
Key facts
- Firm-level data answers "who invests in this"; partner-level data answers "who do I email." Both are needed for outreach that isn't generic.
- Start from known profile URLs, not broad category browsing. Both Actors work best against specific, already-identified profile pages.
- Focus fields go stale. A firm's stated sector/stage focus can change; re-verify before outreach, not just before building the list.
- Fit beats volume. A well-matched list of 20-50 firms consistently outperforms an unfiltered list of hundreds.
Actor configuration that matters
For PitchBook Investors Scraper, start from firm or investor profile URLs discovered through sector research rather than trying to enumerate an entire investor category in one run — public profile pages are the reliable input; broad category browsing is not.
\json
{
"profileUrls": [
"https://pitchbook.com/profiles/investor/example-1",
"https://pitchbook.com/profiles/investor/example-2"
],
"includeInvestmentCriteria": true
}
\\
For LinkedIn Profile Scraper, feed it the specific partner or associate profile URLs found through the PitchBook data or firm websites, since public profile scraping works best against known URLs rather than broad search.
What the output looks like
The PitchBook row needs firm name, focus sectors, stage focus, and stated check size when available. The LinkedIn row needs name, title, current firm, and background summary. Joining the two gives a prospecting row that answers both "does this firm invest in what I'm building" and "who specifically should I reach out to."
Use case 1: targeted seed and Series A outreach
A founder raising a specific round size in a specific sector can filter PitchBook profiles for matching stage and sector focus, then use LinkedIn enrichment to identify the right partner to reach rather than sending a cold email to a generic firm inbox.
Use case 2: warm-intro path mapping
Once a target investor list exists, cross-referencing partner LinkedIn backgrounds against a founder's own network (previous companies, schools, mutual connections) helps prioritize which firms are reachable through a warm introduction versus cold outreach.
Use case 3: competitive fundraising intelligence
Tracking which investors a competitor has raised from, where that's publicly known, helps a founder understand which firms are already active in the category and how to differentiate outreach messaging for firms likely to be comparing the two companies.
Production notes
Start from known profile URLs, not broad scraping. Both Actors work best against specific, already-identified profile pages rather than attempting to enumerate an entire category of investors in one pass.
Respect what's public. Only collect information visible on public profile pages. Avoid combining this with any data source that would require login credentials or private access.
Re-verify stage and sector focus before outreach. Investor focus areas change; a profile scraped months ago may not reflect a firm's current thesis.
Prioritize fit over volume. A list of 20 well-matched investors outperforms a list of 200 firms scraped without sector or stage filtering.
Firm-level data alone produces low response rates. A firm-level list without partner-level context means outreach goes to generic inboxes. Adding LinkedIn partner enrichment makes outreach targeted enough to get meaningfully better response rates.
Cost comparison
| Approach | Cost | Data freshness | Partner-level detail |
|---|---|---|---|
| PitchBook/Crunchbase subscription | Expensive, ongoing | High | Yes |
| Manual research | Free, very slow | Depends on effort | Inconsistent |
| Apify PitchBook + LinkedIn pipeline | Pay per run | On demand | Yes |
Check the current Pricing tab on each Actor page before running at scale.
FAQ
Can Apify replace a PitchBook subscription?
It can replace the workflow of researching public investor profiles for a specific list; it doesn't replicate PitchBook's full paid database and analytics.
Is scraping public LinkedIn profiles allowed?
LinkedIn Profile Scraper collects publicly visible profile information without login; always review current platform terms before large-scale collection.
How big should a first investor list be?
Most founders get better results from a well-matched list of 20-50 firms than an unfiltered list of hundreds.
What's the fastest way to try this?
Pick 10 investor firms whose public focus matches your sector and stage, then run PitchBook Investors Scraper and LinkedIn Profile Scraper against them.
Try it yourself
Pick 10 investor firms whose public focus matches your sector and stage. Run PitchBook Investors Scraper against their profile pages, then use LinkedIn Profile Scraper to find the right partner at each. A short, well-matched list beats a long, generic one.
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