LinkedIn Ad Library Scraper

Scrape and analyze paid advertising campaigns from the LinkedIn Ad Library — extract headlines, body copy, images, videos, CTAs, targeting details, impressions, and advertiser information across multiple countries.

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Watch it in action

Stop copying ads by hand. This short demo shows how to configure the Actor, run it against a live Ad Library search, and export clean data — no code required.

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Overview

The LinkedIn Ad Library is a publicly available database where advertisers disclose the creative and performance details of their paid ads. However, it provides no bulk export, no search API, and no way to filter by impression range or targeting criteria.

This Actor automates the entire workflow: it constructs a search query for LinkedIn's Ad Library, paginates through all matching results, opens each ad's detail page, and extracts 20+ structured fields — including creative type, headline, body text, image/video URLs, call-to-action buttons, advertiser info, impressions (and per-country breakdown), targeting parameters, and more.

Whether you're a marketer benchmarking competitor campaigns, a researcher tracking political or brand messaging, or a product team tracking ad creative trends, this Actor turns an otherwise manual process into a single configurable run that exports clean, queryable JSON.

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Use Cases

• Competitive ad intelligence — Enter a competitor's company name or a keyword like "project management software" and retrieve every active paid ad they've disclosed, along with the creative they're running and estimated reach.
• Creative and copy analysis — Pull headlines, body copy, images, and CTAs at scale to study messaging strategies, visual styles, or CTA patterns across an industry or over a date range.
• Political and brand messaging research — Filter by ad payer (e.g., a PAC or organization) and date range to track how messaging evolves across election cycles or product launches.
• Market and audience insights — Inspect targeting parameters (industry, job function, education level, location, language) to understand how advertisers segment their LinkedIn audiences.
• Performance estimation — Filter ads by impression range (minImpressions / maxImpressions) to surface high-reach campaigns or narrow in on niche, low-spend placements.

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Why use this Actor?

Capability	This Actor	Manual collection
Bulk export	✅ JSON, CSV, Excel, XML	Copy-paste only
Structured data	✅ 20+ fields per ad	Only what's visible on screen
Pagination	✅ Automatic, up to 10,000 ads per run	Manual clicking
Filtering	✅ Country, date range, impression range	Not supported by LinkedIn
Scheduling	✅ Run on a schedule via Apify	Not possible
BI tools	✅ Google Sheets, Looker Studio, Tableau, Power BI, Redshift	Manual re-export each time
AI agents	✅ Runnable via MCP	Not applicable

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Integrations

The dataset output is flat, well-typed JSON, so it plugs into the tools you already report in.

• Google Sheets — Append new ads to a spreadsheet automatically on a schedule.
• Looker Studio — Build ad-creative dashboards from a synced Sheet or a connector.
• Tableau — Load the dataset through Tableau's REST/JSON or Web Data Connector path.
• Power BI — Import CSV/JSON, or refresh from an OData feed.
• Redshift — Load via ETL tools or a connector service.
• MCP / AI agents — Expose this Actor as a tool for Claude, Cursor, or any MCP client.

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Input

Configure the scraper through the following fields:

Input	Type	Description
keyword	string	Search term used to find ads on the Ad Library (e.g. "python course").
payer	string	Name of the entity paying for the ads (e.g. a company or organization).
company	string	LinkedIn company page name to filter ads by.
countries	array of ISO codes	Limit results to specific countries (e.g. ["DE", "FR", "US"]). Defaults to ["DE"].
startDate	date (YYYY-MM-DD)	Start of the reporting period. Required together with endDate.
endDate	date (YYYY-MM-DD)	End of the reporting period. Required together with startDate.
minImpressions	integer	Minimum estimated impressions to include (0 – 1,000,000,000).
maxImpressions	integer	Maximum estimated impressions to include (0 – 1,000,000,000).
maxLimit	integer (required)	Maximum number of ad detail pages to scrape (1 – 10,000; default: 5).
proxy	object	Optional proxy configuration to avoid rate limits or geo-blocks during scraping.

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Output

Each run produces a structured JSON record per ad. A representative example:

{
  "adId": "1534932424",
  "adUrl": "https://www.linkedin.com/ad-library/detail/1534932424",
  "advertiserLogo": "https://media.licdn.com/dms/image/v2/C4D0BAQE_-3BSObKKhw/company-logo_100_100/company-logo_100_100/0/1630458623850/hogeschool_van_arnhem_en_nijmegen_logo?e=1793232000&v=beta&t=7h4JfSmEjCr-ucIXCmU-VVobsvuePPW-61gWInec92E",
  "advertiserName": "HAN University of Applied Sciences",
  "advertiserUrl": "https://www.linkedin.com/company/12219",
  "availabilityStart": "Sep 1, 2026",
  "availabilityEnd": "Sep 15, 2026",
  "body": "🐍 𝗣𝘆𝘁𝗵𝗼𝗻 𝗹𝗲𝗿𝗲𝗻? 𝗕𝗲𝗴𝗶𝗻 𝗯𝗶𝗷 𝗱𝗲 𝗛𝗔𝗡!\n\nPython is toegankelijk, krachtig en breed inzetbaar – van data-analyse tot automatisering. In deze cursus leg je een stevige basis en leer je programmeren met praktijkgerichte opdrachten.\n\nPerfect als je:\n🚀 Sneller wilt werken met data of bestanden\n🧠 Je probleemoplossend vermogen wil vergroten\n💪 Een waardevolle skill wil toevoegen aan je werk\n\n\nStartdatum: 27 Oktober 2026 of 8 april 2026\nLestijden: 8 lesdagen van 09:30-15:00\nLocatie: HAN University of Applied Sciences, Academie Toegepaste Biowetenschappen en Chemie in Nijmegen",
  "ctasUrl": [
    "https://www.han.nl/opleidingen/cursus/programmeren-in-python"
  ],
  "ctas": [
    "Inschrijven"
  ],
  "creativeType": "SPONSORED_STATUS_UPDATE",
  "format": "Single Image Ad",
  "impressions": "< 1k",
  "impressionsPerCountry": [
    {
      "country": "Netherlands",
      "impressions": "100%"
    }
  ],
  "paidBy": "Stichting Hogeschool van Arnhem en Nijmegen",
  "headline": "Meld je vandaag nog aan voor de cursus!",
  "imageUrl": [
    "https://media.licdn.com/dms/image/v2/D4E10AQEeBPbfaCXtog/image_627_1200/B4DZjGW4x.H0AY-/0/1755674545433/BijdeHANlerenprogrammerenmetPythonjpg?e=2147483647&v=beta&t=BRB6ha1EklGp5wXzpoXoI05zdZmhPhq_DrGDX6NnRPs"
  ],
  "videoUrl": null,
  "targetingLanguage": "Nederlands",
  "targetingLocation": "Nederland",
  "targetingParameter": [
    {
      "targeting_parameter": "Audience",
      "targeted": false,
      "excluded": false
    },
    {
      "targeting_parameter": "Demographic",
      "targeted": false,
      "excluded": false
    },
    {
      "targeting_parameter": "Company",
      "targeted": false,
      "excluded": false
    },
    {
      "targeting_parameter": "Education",
      "targeted": true,
      "excluded": false
    },
    {
      "targeting_parameter": "Job",
      "targeted": true,
      "excluded": false
    },
    {
      "targeting_parameter": "Member Interests and Traits",
      "targeted": false,
      "excluded": false
    }
  ]
}

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Frequently Asked Questions

How do I export LinkedIn Ads to Google Sheets?

Run the Actor, then use Apify's Google Sheets integration (or any automation tool) to append the dataset to a spreadsheet. For one-off reports, simply export the dataset as CSV or Excel from the run page and import it into Sheets. Scheduling the integration gives you a self-updating ad tracker.

Can I connect LinkedIn Ads to Looker Studio?

Yes. The simplest route is to sync the dataset to a Google Sheet first, then add that Sheet as a Looker Studio data source. That gives you refreshable dashboards of headlines, copy, CTAs, and ad images. Third-party ETL connectors can also push Apify datasets directly into Looker Studio.

How do I get LinkedIn Ads data into Tableau or Power BI?

Both tools can read the dataset as CSV/JSON or via a REST-style endpoint using your Apify API token. Export the dataset, use a Google Drive integration, or point the tool at the dataset URL, and refresh on a schedule.

Is there a LinkedIn Ads connector for Redshift?

There's no single-click native connector, but the dataset loads into Redshift through standard ETL tools or connector services (for example Coupler.io, Zapier, or CData Sync). Provide your Apify API token and dataset ID on the source side, and your Redshift credentials on the destination side, then pick a sync schedule.

Can I integrate Linkedin Ad Library Scraper data with other services?

Yes. Scraped Linkedin Ad Library data can be integrated with almost any cloud service or web app. Apify supports Zapier, n8n, Slack, Make, Airbyte, Gumloop, CrewAI, IFTTT, Lindy, GitHub, Google Sheets, Google Drive, and more.

You can also use webhooks to trigger actions whenever a run finishes, like sending notifications or syncing datasets.

Can I use Linkedin Ad Library Scraper with the Apify API?

Yes. The Apify API provides programmatic access to run Actors, fetch datasets, monitor performance, and manage tasks. For Node.js, use the apify-client NPM package. For Python, use the apify-client PyPI package. Full API docs are available in the Apify API reference.

Can I run this Actor through MCP?

Yes. The LinkedIn Ad Library Scraper can be used as an MCP tool, so AI clients like Claude Desktop or Cursor can run it with natural language. You can connect it through the official Apify MCP Server (for multiple Actors) or the Actor-to-MCP Bridge (for this single Actor).

Connect directly using this Actor’s MCP endpoint:

Learn more: Apify MCP docs

Can I schedule recurring LinkedIn ad monitoring?

Yes. Create a Schedule in Apify Console, attach this Actor, and set the interval (daily, weekly, or a cron expression). Combined with a Sheets or BI integration, this turns the Actor into an always-current competitor ad monitoring pipeline.

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