# LinkedIn Markets Prospects — Qualified Finance Leads (`alaudinburki/linkedin-markets-prospects`) Actor

Turn a LinkedIn search into qualified prospects — each with a fit score and a written reason to contact them — instead of a pile of raw profiles.

- **URL**: https://apify.com/alaudinburki/linkedin-markets-prospects.md
- **Developed by:** [alaudin burki](https://apify.com/alaudinburki) (community)
- **Categories:** Lead generation, Business
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $4.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 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.

# Actor input Schema

## `mode` (type: `string`):

Qualify a dataset you already have (free), or plan and run the searches yourself (paid).

## `dryRun` (type: `boolean`):

ON by default. Shows exactly which searches would run and the estimated cost, then stops. Turn this OFF when you are happy with the plan — that is the only thing standing between you and a paid scrape.

## `datasetId` (type: `string`):

Only used in 'Qualify an existing dataset' mode. Paste the dataset id from a LinkedIn search actor's run.

## `jobTitles` (type: `array`):

One search runs per title for every city below. Titles decide who, so be specific — 'Portfolio Manager' finds buyers, 'Investment' finds everybody.

## `cities` (type: `array`):

Splitting by city is how you get past LinkedIn's hard limit of 2,500 results per search — it silently stops there, so one broad query quietly returns a fraction of what exists.

## `industryIds` (type: `array`):

Filter to an industry. 43 is Financial Services, which is what most users want here. Leave as-is unless you know the code you need.

## `pagesPerQuery` (type: `integer`):

One page is up to 25 profiles. Cost scales directly with this number, so it is the main dial on what a run spends.

## `minScore` (type: `integer`):

0 keeps everybody including recruiters; 3 keeps only people with real market relevance; 5 keeps only near-perfect fits. Rows below this are dropped, not hidden.

## `maxItems` (type: `integer`):

Hard limit on prospects written. Enforced exactly — the best-scoring rows are kept when it bites, never whoever happened to be scraped first.

## `icpPath` (type: `string`):

Carried onto every row so a list can be split later. A = traders and analysts, B = small funds and emerging managers, C = institutional.

## `readLimit` (type: `integer`):

Safety valve on how much raw data is pulled before qualifying. Rarely needs changing.

## `timeoutSecs` (type: `integer`):

How long a single search may run before it is abandoned. Abandoned searches do not fail the whole run.

## Actor input object example

```json
{
  "mode": "search",
  "dryRun": true,
  "jobTitles": [
    "Portfolio Manager",
    "Head of Trading",
    "Managing Partner"
  ],
  "cities": [
    "London",
    "Zurich",
    "Dubai"
  ],
  "industryIds": [
    "43"
  ],
  "pagesPerQuery": 5,
  "minScore": 1,
  "maxItems": 250,
  "icpPath": "B",
  "readLimit": 100000,
  "timeoutSecs": 3600
}
```

# Actor output Schema

## `prospects` (type: `string`):

Best-fit people first. Each carries a 0-5 score, the terms behind it, and a written reason to make contact.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "mode": "search",
    "dryRun": true,
    "datasetId": "",
    "jobTitles": [
        "Portfolio Manager",
        "Head of Trading",
        "Managing Partner"
    ],
    "cities": [
        "London",
        "Zurich",
        "Dubai"
    ],
    "industryIds": [
        "43"
    ],
    "pagesPerQuery": 5,
    "minScore": 1,
    "maxItems": 250,
    "icpPath": "B",
    "readLimit": 100000,
    "timeoutSecs": 3600
};

// Run the Actor and wait for it to finish
const run = await client.actor("alaudinburki/linkedin-markets-prospects").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "mode": "search",
    "dryRun": True,
    "datasetId": "",
    "jobTitles": [
        "Portfolio Manager",
        "Head of Trading",
        "Managing Partner",
    ],
    "cities": [
        "London",
        "Zurich",
        "Dubai",
    ],
    "industryIds": ["43"],
    "pagesPerQuery": 5,
    "minScore": 1,
    "maxItems": 250,
    "icpPath": "B",
    "readLimit": 100000,
    "timeoutSecs": 3600,
}

# Run the Actor and wait for it to finish
run = client.actor("alaudinburki/linkedin-markets-prospects").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "mode": "search",
  "dryRun": true,
  "datasetId": "",
  "jobTitles": [
    "Portfolio Manager",
    "Head of Trading",
    "Managing Partner"
  ],
  "cities": [
    "London",
    "Zurich",
    "Dubai"
  ],
  "industryIds": [
    "43"
  ],
  "pagesPerQuery": 5,
  "minScore": 1,
  "maxItems": 250,
  "icpPath": "B",
  "readLimit": 100000,
  "timeoutSecs": 3600
}' |
apify call alaudinburki/linkedin-markets-prospects --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,alaudinburki/linkedin-markets-prospects"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/tOS6mh3qKuNHBdWTi/builds/bdojLbiIVH4dTEFHF/openapi.json