export linkedin post analytics to csv

Pulling Linkedin post analytics data by hand does not scale. You either copy-paste it one item at a time, or you fight the API — rate limits, auth, and pagination — and still end up with half the fields missing. For a social media manager, the data only matters if it is complete, fresh, and in a sheet you can act on. That is the gap LinkedIn Post Performance Scraper closes.

LinkedIn Post Performance Scraper runs 7,204 times a month on Apify.

What you get

likes, comments, shares, the full reaction breakdown, author details, post text and media attachments
No cookies, no login
media attachments. No cookies

Instead of building a scraper, you point LinkedIn Post Performance Scraper (data-slayer/linkedin-post-analytics-scraper) at your input and run it. It handles the requests, retries, and parsing, and returns one clean row per item in the format you already use.

How it works — step by step

1. Open the actor Go to data-slayer/linkedin-post-analytics-scraper and click Try for free.

2. Paste your input

{
  "linkedin_urls": []
}

3. Run it Click Start. A typical run finishes in under a minute and returns one row per item.

4. Get your data Download as JSON, CSV, or Excel, or push straight to Google Sheets / Airtable via the built-in integrations.

What the output looks like

Field	Example	Use it for
`url`	`https://…/item/ABC123`	link back to the source
`caption`	`"…"`	content analysis
`engagement`	`…`	filter / sort / export
`collectedAt`	`2026-09-30T12:00:00Z`	time-series / scheduling

Why scraping Linkedin post analytics without getting blocked is hard in 2026

Linkedin is one of the most aggressively defended sites on the web, and 2026 is the hardest year yet to pull Linkedin data at scale. If you have tried to scrape Linkedin yourself, you have probably already hit one of these walls:

Anti-bot detection. Linkedin fingerprints the TLS handshake, the HTTP headers, and the request timing of every client. A plain requests call is flagged before it ever reaches a public profile, and you get blocked with a login wall or an empty response.
Rate limits. The public endpoints throttle by IP and by session. Hit the rate limit and the API returns errors for minutes; ignore it and the account or IP is temporarily banned.
Login walls and cookies. Many surfaces (stories, some reels, follower lists) are only served to a logged-in session, so a naive scraper needs a real login, a cookie jar, and a way to refresh it — which is exactly what gets accounts disabled.
Pagination and shifting JSON. Linkedin changes its private JSON shape without notice, so a scraper you wrote last quarter silently returns half the fields today.

That is the difference between a script that works once on your laptop and a Linkedin scraper that runs every day without maintenance. For a post analytics job you do not want to babysit proxies, cookies, and retries — you want the rows.

Three ways to get Linkedin data — and which one to use

There are three honest ways to get Linkedin post analytics data in 2026. Each has a real cost.

Approach	How it works	The catch
Build your own scraper	Write a Python scraper with `requests`, rotate residential proxies, manage a login session, parse the private JSON	Weeks of work, constant breakage, and you own the block/ban risk. Fine for a one-off, painful at scale.
Official Linkedin API	Use the platform’s own API	Heavily restricted, requires app review, returns a fraction of the public fields, and is not built for bulk extraction.
A ready-made scraping API / actor	Point a maintained actor at your input and download clean rows	You pay per result, but you skip the proxy, login, and parsing work entirely.

For most post analytics work the third option wins on total cost. A scraping API gives you the same public Linkedin data a hand-built Python scraper would, without the account-handling system around it. Apify hosts these actors and exposes them through a web scraping API, so you can run one by hand, on a schedule, or from code with the Apify API.

How to scrape Linkedin post analytics without getting blocked

If you do build your own Linkedin scraper, these are the controls that actually keep it alive. Every one of them is already handled for you inside a maintained actor.

Use residential proxies. Datacenter IP ranges are blocked on sight. Rotate residential proxies per request so no single IP crosses the rate limit.
Respect the rate limit. Throttle to a concurrency the target tolerates, and back off exponentially when you see a 429. A conservative rate limit beats a fast ban.
Send real headers and a warm session. Match a browser’s headers, keep a cookie jar, and reuse a session instead of opening a cold connection every call.
Retry with jitter. Transient failures are normal; retry with exponential backoff and random jitter, and treat an empty body as a failure, not a result.
Page carefully. Follow cursors to the end, dedupe by id, and stop cleanly when the feed ends — do not hammer the same page.
Cache what you already have. Re-fetch only new items. Most “blocks” are self-inflicted by re-scraping the same public profile hundreds of times.

Do all six and you can scrape Linkedin without getting blocked for a while. Do none of them and you will get blocked on day one. That maintenance burden is the real reason teams move to a hosted Linkedin scraper instead of owning the plumbing.

What you can do with Linkedin post analytics data

Once the rows land in a sheet, the data does the work. Four patterns we see most:

Competitor benchmarking. Track the engagement metrics of a competitor’s public posts over time and see what format wins in your niche.
Creator and lead discovery. Pull the public profiles behind a hashtag or keyword and build a shortlist of creators to work with.
Content research. Export the top posts for a topic, cluster their captions and hashtags, and use the winners as a brief for your own content.
Reporting and monitoring. Schedule the actor daily, push to Google Sheets, and let a dashboard refresh itself instead of paying an analyst to copy-paste numbers.

All of it runs on public data — no login, no personal data, and no private accounts.

Is scraping Linkedin post analytics legal?

Scraping public Linkedin data is generally lawful in most jurisdictions, but the rules are not uniform and they change. A few principles keep you on the right side of it:

Public data only. If a field is visible to a logged-out visitor, it is fair game in most readings; private profiles, DMs, and anything behind a login are not.
Mind privacy law. GDPR, CCPA, and similar regimes still govern how you store and process personal data even when you collected it lawfully. Do not build profiles of individuals from public data without a lawful basis.
Respect terms and robots. Follow the platform’s terms and its robots.txt, and never use scraped data to harass, spam, or re-identify people.
Keep it proportionate. A steady, modest rate limit is both more ethical and more durable than a burst that degrades the service for everyone.

This article is not legal advice. When the use case is commercial and the data is personal, get a lawyer’s read before you scale.

Best Linkedin scraper: how to choose one in 2026

Search for the best Linkedin scraper and you get a wall of tools. The best Linkedin scraper for your job comes down to four questions:

Does it run logged out? If a tool needs your Linkedin login, it is putting your account at risk. A good Linkedin scraper reads public data without a session.
Does it handle the blocking for you? Residential proxies, retries, and pacing should be the tool’s problem, not yours.
Does it return the fields you need? A tool that returns ten fields when you need fifty is a false economy.
Can you schedule it? The value compounds when the data refreshes itself.

Across the Linkedin scrapers in 2026, the ones that last are maintained, logged-out, and API-first. That is the design of the actor on this page.

What you can extract from Linkedin URLs, posts, reels and hashtags

The unit of work is a Linkedin URL or handle. From those you can extract Linkedin data across every public surface:

Posts and reels — captions, media, view counts, and engagement metrics.
Comments — the public comment thread, with authors and timestamps.
Hashtags — the public posts behind a hashtag, for trend and creator research.
Profiles — the public profile fields a logged-out visitor can see.

You hand the actor the Linkedin URLs you care about and it returns one row per item. Because it is a single Linkedin scraper API, the same call works for posts, reels, comments, and hashtags — you do not stitch together four tools.

Linkedin scraping API vs Bright Data vs a custom build

If you have looked at Bright Data or another scraping API, the trade-off is the same everywhere: a web scraping API sells you the unblocking layer, and you still own the parsing. A hosted Linkedin scraper API goes one step further — it returns the parsed post analytics rows, not just the HTML.

	Custom Python build	Bright Data / raw proxy	Hosted actor
Unblocking	you build it	included	included
Parsing to fields	you build it	you build it	included
Maintenance	ongoing	low	vendor
Time to first row	days	hours	minutes

Social media scraping is a maintenance problem, not a one-off script. The cheapest line item is almost never the one that costs you a week of engineering every quarter.

Linkedin post analytics terms, explained

A quick reference for the terms this guide uses:

page analytics — the public profile or page you point the actor at. A run returns page analytics for every row.
export your linkedin — the file or dataset you download when the run finishes. A run returns export your linkedin for every row.
linkedin analytics — the likes, comments, views, and shares on an item. A run returns linkedin analytics for every row.
spreadsheet — part of the Linkedin post analytics data you get back. A run returns spreadsheet for every row.
demographics — part of the Linkedin post analytics data you get back. A run returns demographics for every row.
content strategy — part of the Linkedin post analytics data you get back. A run returns content strategy for every row.
timeframe — part of the Linkedin post analytics data you get back. A run returns timeframe for every row.
marketer — part of the Linkedin post analytics data you get back. A run returns marketer for every row.

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FAQ

Can I scrape Linkedin without logging in? Yes — for public Linkedin data you do not need a login or cookies. The actor runs logged out on your side, which is exactly what keeps your own account safe.

Does Linkedin block scraping? Linkedin blocks naive scrapers aggressively: datacenter IPs, cold sessions, and fast bursts all get flagged. A maintained Linkedin scraper rotates residential proxies, paces itself under the rate limit, and retries cleanly, so it does not get blocked.

Do I need coding skills to scrape Linkedin data? No. You paste your input into the actor’s form and click Start — no Python, no proxy setup, no cookie handling. Developers can still drive the same actor through the Apify API.

How much does it cost to scrape Linkedin post analytics? You pay Apify compute plus a small per-result price; check the actor’s Pricing tab for the exact rate. The free tier covers small runs, so you can test before you commit.

Can I export Linkedin post analytics to CSV or Excel? Yes — download as JSON, CSV, or Excel, or connect Google Sheets / Airtable directly from the actor page: https://apify.com/data-slayer/linkedin-post-analytics-scraper

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Who this is for

If you are a social media manager, this replaces the manual Linkedin post analytics pull. Run it on a schedule, push the output to Sheets or Airtable, and your report refreshes itself.

Try it now

Ready to run it yourself? Open LinkedIn Post Performance Scraper on Apify →

No login, no code. Free tier included.