LinkedIn Markets Prospects — Qualified Leads, Not a Name Dump

Give it job titles and cities. Get back people worth contacting, each with a 0–5 fit score, the exact profile terms behind that score, and a written reason to make contact — assembled from the profile itself, so it is specific to that person rather than a template.

Built for anyone selling into financial markets: trading tools, market data, research platforms, brokerage, fintech. Adjust one dictionary and it scores for your product instead.

Why this is different

A LinkedIn scraper returns profiles. A prospecting list needs people who plausibly want your product, each with a reason to be contacted. Those are different artefacts, and the gap between them is where cold campaigns die — an unscored list of 5,000 names produces generic outreach, and generic outreach is what gets a sending domain filtered.

This actor closes that gap. It does not replace a scraper; it decides what the scraper found is worth.

	Raw LinkedIn scraper	This actor
Output	every profile it found	only the ones that fit
Ranking	scraped order	fit score, best first
Reason to contact	—	written, from real profile fields
Past LinkedIn's 2,500-result ceiling	manual query splitting	plans the split for you
Recruiters with finance keywords	included	removed
Cost stated before spending	—	yes, up front

What you get

Every row is flat — no nested objects, so it reads correctly in the Apify table view and in a spreadsheet.

Field	Description
linkedin_url	Canonical profile URL. Every variant collapses to one string, so the same person never appears twice
full_name	First and last, with professional designations stripped — "Rahman, PharmD" becomes Rahman
headline	Their headline, verbatim
company_name / company_type	Current employer, and a coarse type (hedge fund, family office, RIA, prop trading, crypto fund)
city / country	country matters: it decides which data-protection rules apply to that row
score	0–5 fit. 0 means do not contact
matched_terms	The exact terms that produced the score — so the score can be argued with
why_them	A specific reason to make contact, built only from fields the profile carries
icp_path	Carried through so one list can be split into audience segments later
stage	Starts at not_contacted; the same nine words your CRM uses

The last row is a SUMMARY record — scan count, keep rate, score distribution, and a country tally. It is flagged so it is never mistaken for a person.

Two modes

Qualify an existing dataset — free. Already ran a LinkedIn search? Point this at the dataset id and it qualifies what you have. This actor does no scraping, so there is nothing to pay.

Search for you — paid. Give it titles and cities. It plans every search needed, tells you the estimated cost, and runs them. Turn dryRun off when you are happy with the plan.

Never past LinkedIn's ceiling

LinkedIn silently stops a search at 2,500 results. One broad query looks like it worked and quietly returns a fraction of what exists.

This actor splits by city per title automatically — 3 titles × 3 cities = 9 searches instead of one truncated one — and shows you the plan before it runs.

Cost, stated before it is spent

dryRun is on by default. The first run costs nothing and prints exactly what would run and what it would cost:

8 query(ies) × 5 page(s) = up to 1000 profiles.
Estimated cost of the search step: $4.00–$8.00 (USD).

Nothing is scraped until you turn it off deliberately.

Who this is for

• Fintech and trading-tool founders prospecting portfolio managers, traders and analysts
• Market data and research vendors building a target list by city and role
• Agencies selling into financial services who need a scored list rather than a name dump

Not for: bulk email blasts. This produces a list to work through deliberately, which is the only way cold outreach to finance professionals works at all.

What it deliberately does not do

• It does not scrape LinkedIn with your account. It drives a no-cookie search actor that runs on its own infrastructure. Your account is never logged in and never at risk.
• It does not invent an email. The search actor returns none until you run its email-search mode, so this field is simply absent rather than guessed.
• It does not invent a reason. If the profile carries no evidence, why_them is empty and the row is counted in without_reason. An empty field is honest; a fabricated sentence is not.
• It does not contact anybody. It produces a list.

Adjusting it for your product

All the judgement lives in one place — src/lib/qualify.js. MARKET_SIGNALS is a dictionary of terms and weights, NEGATIVE_SIGNALS is who to exclude, and the score thresholds are five numbers. Change those and it scores for your market instead.

The scoring rules are not universal and the defaults are calibrated for a product covering gold, silver, crude oil, EUR/USD, USD/JPY and crypto. They are visible in the source precisely so they can be argued with.

Limitations

• LinkedIn Markets Prospects reads only what its sources publish publicly. Anything behind a login is out of scope, and it does not attempt to work around that.
• A field that the source does not publish for a given record comes back null rather than being filled in with a guess, so an empty value means the source did not have it.
• Output quality is bounded by the source. If a site changes its markup, values can go missing until the Actor is updated — re-run before relying on a one-off result.
• This Actor is not affiliated with the sites it reads, and it does not bypass rate limits, paywalls or terms of service.

FAQ

What does LinkedIn Markets Prospects return?

It returns 30 documented output fields — score, matched, linkedin_url, public_identifier, first_name, last_name, full_name and headline — each with a declared type and a written description. The full schema is below.

What does LinkedIn Markets Prospects require to run?

Nothing. All 12 inputs are optional and ship with working defaults.

Does LinkedIn Markets Prospects need an API key?

No. It reads only public data and asks for no account.

How is LinkedIn Markets Prospects billed?

Per result returned, on Apify. A run that returns nothing costs nothing. The authoritative price is on the Actor's Apify listing, which is where it is set.