LinkedIn to Google Sheets, clean company data fast
Copying company details out of LinkedIn feels quick until it isn’t. One missing field turns into five tabs, a half-finished spreadsheet, and a lead list you don’t fully trust.
This LinkedIn Sheets automation hits sales ops and growth marketers first, honestly. But agency owners building account lists feel it too. You’ll pull consistent company data (identity, size, classification, funding) and log it cleanly to Google Sheets without the constant cleanup.
Below you’ll see exactly what the workflow extracts, how it standardizes fields, and where you can tweak it for your own lead qualification rules.
How This Automation Works
See how this solves the problem:
n8n Workflow Template: LinkedIn to Google Sheets, clean company data fast
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The Challenge: Cleaning LinkedIn Company Data by Hand
Building a company list from LinkedIn usually starts as “just grab the basics.” Then you realize every profile is formatted a little differently. The HQ location is buried, the website field is inconsistent, and employee counts don’t map neatly into the size buckets your team uses. Add funding details (round, amount, investors, date) and it becomes a repetitive research job that steals focus from outreach, targeting, and actual decision-making. Worse, the sheet looks complete until someone tries to segment it and half the rows don’t match.
It adds up fast. Here’s where it breaks down in real teams.
- People paste “About” text and locations in different formats, so filtering by country or state becomes a manual rework project.
- Employee counts are often missing or interpreted differently, which leads to bad size-tiering and misrouted outreach.
- Funding details get skipped because they’re time-consuming, even though they’re often the best signal for prioritization.
- When lists are assembled by multiple people, you end up debating data quality instead of acting on it.
The Fix: Airtop Pulls the Company Profile, Sheets Gets Clean Rows
This workflow takes a single input (a LinkedIn company URL) and uses Airtop to extract structured company details from that page using your LinkedIn-authenticated Airtop profile. Instead of you hunting for fields one-by-one, Airtop navigates to the profile and returns a standardized JSON payload that already contains identity data (name, tagline, HQ location, website, about), scale signals (employee count plus a size bracket), classification fields (like automation agency true/false and technical sophistication), and funding profile details (latest round, amount raised, investors, last update date). After extraction, the workflow formats the payload into a clean, consistent structure so it’s ready to log to Google Sheets and use for lead qualification.
The workflow starts when a form is submitted or another workflow triggers it with the LinkedIn URL and Airtop profile. Then Airtop retrieves the company profile data from LinkedIn and returns it in a predictable schema. Finally, the workflow formats the result payload so your sheet columns stay consistent over time.
What Changes: Before vs. After
| What This Eliminates | Impact You’ll See |
|---|---|
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Real-World Impact
Say you’re enriching a list of 40 target accounts for an outbound sprint. Manually, you might spend about 10 minutes per company grabbing the name, HQ, website, employee count, and a quick funding scan. That’s roughly 6 to 7 hours of busywork before you even write a single message. With this workflow, you submit the LinkedIn URLs (or pass them in from an upstream workflow), wait for Airtop to return the structured data, and log it to Sheets. You still review edge cases, but the heavy lifting is done.
Requirements
- n8n instance (try n8n Cloud free)
- Self-hosting option if you prefer (Hostinger works well)
- Airtop to extract company details from LinkedIn.
- Google Sheets to store and share structured lead data.
- Airtop API Key (get it from your Airtop dashboard).
Skill level: Beginner. You’ll connect Airtop and Google Sheets, then map fields once.
Need help implementing this? Talk to an automation expert (free 15-minute consultation).
The Workflow Flow
A form submission or an upstream workflow passes the inputs. You provide the LinkedIn company URL and pick the Airtop Profile that’s already authenticated with LinkedIn.
The workflow merges the incoming fields into one clean payload. This matters more than it sounds, because inputs can come from two sources (the built-in form trigger or another n8n workflow), and you want the same structure either way.
Airtop retrieves the company profile and extracts the details. It navigates to the LinkedIn company page and returns structured data like headquarters, employee count, size bracket, and funding fields.
The result is formatted into a standardized output. That final payload is what you map into Google Sheets columns (or pass into your CRM enrichment workflow next).
You can easily modify the output schema to match your sheet columns based on your needs. See the full implementation guide below for customization options.
Step-by-Step Implementation Guide
Step 1: Configure the Form Submission Trigger
Set up the incoming data sources that feed LinkedIn URLs and Airtop profiles into the workflow.
- Add the Form Submission Trigger node and set Form Title to
Company information. - In Form Description, use the expression
=This Airtop Studio automation simplifies LinkedIn data extraction by automatically providing structured, reliable, and easily actionable data—saving significant effort, reducing errors, and enabling fast analysis and decision-making.. - Configure Form Fields with two required inputs: Company's LinkedIn URL (placeholder
https://www.linkedin.com/company/airtop-ai/) and Airtop Profile (connected to Linkedin). - Add the Upstream Workflow Trigger node and define Workflow Inputs as
company_linkedinandairtop_profileso external workflows can pass data in. - Connect both Form Submission Trigger and Upstream Workflow Trigger to Merge Input Fields to unify the data path.
https://www.linkedin.com/company/your-company/) or the extraction may fail.Step 2: Connect Airtop
Link Airtop so the workflow can retrieve and analyze LinkedIn company data.
- Open the Retrieve Company Profile node.
- Credential Required: Connect your airtopApi credentials.
Step 3: Set Up Merge Input Fields
Normalize inputs so data from either trigger is stored under the same keys.
- Open the Merge Input Fields node and add two assignments.
- Set company_linkedin to the expression
={{ $json.company_linkedin || $json["Company's LinkedIn URL"] }}. - Set airtop_profile to the expression
={{ $json.airtop_profile || $json["Airtop Profile (connected to Linkedin)"] }}.
Step 4: Set Up Retrieve Company Profile
Configure the Airtop extraction request and analysis prompt for structured JSON output.
- In Retrieve Company Profile, set Resource to
extractionand Operation toquery. - Set URL to the expression
={{ $json.company_linkedin }}. - Set Profile Name to the expression
={{ $json.airtop_profile }}. - Set Session Mode to
new. - Paste the full prompt into Prompt:
=# LinkedIn Company Analysis Prompt Extract and analyze the following information from the provided LinkedIn company page. Present the results in a structured JSON format. ## Required Data Points ### 1. Company Identity - Full company name (including suffixes like Inc., LLC, etc.) - Brand tagline/headline (directly under company name) - Global headquarters location - Company description (full "About" section text) - Primary website URL (excluding social media links) ### 2. Company Scale - Current employee count (from LinkedIn "X employees" metric) - Employee range bracket: [0-9], [10-150], [150+] ### 3. Business Classification Evaluate the following characteristics based on company description, recent posts, and featured content: #### Automation Agency Status - Boolean (true/false) classification - Criteria for "true": * Company explicitly offers automation services to clients * Core business model involves developing/implementing automations * Primary revenue from automation consulting/development #### AI Implementation Level Classify as [Low/Medium/High] based on: - Low: No evidence of AI/automation/scraping usage - Medium: Uses AI/automation tools or mentions them as supplementary capabilities - High: Core business involves AI development, automation creation, or data harvesting services ### 4. Technical Sophistication Evaluate overall technical capabilities as [Basic/Intermediate/Advanced/Expert] based on: - Technology stack mentioned - Technical job postings - Products/services complexity - Engineering team size - Technical achievements highlighted ### 5. Investment Profile If available, document: - Most recent funding round - Total funding amount - Key investors - Last funding date Mark as "Not publicly disclosed" if information unavailable ## Output Format Return data in the following JSON structure, with all fields required, make sure the response contains only the json. { "company_profile": { "name": string, "tagline": string, "location": { "city": string, "state": string, "country": string }, "overview": string, "website": string }, "scale": { "employee_count": number, "size_bracket": string }, "classification": { "is_automation_agency": boolean, "ai_focus_level": string, "technical_tier": string }, "funding": { "latest_round": string, "total_raised": string, "investors": [string], "last_updated": string } }. - In Additional Fields → Output Schema, set the schema to the provided JSON schema value to enforce structure.
Step 5: Configure Format Result Payload
Normalize the final response so downstream steps can consume a clean JSON payload.
- Open Format Result Payload and set Mode to
raw. - Set JSON Output to
={{ $json.data.modelResponse }}.
Step 6: Test and Activate Your Workflow
Run a manual test to verify the extraction pipeline and output format before going live.
- Click Execute Workflow and submit the form via Form Submission Trigger or run Upstream Workflow Trigger with
company_linkedinandairtop_profile. - Confirm Merge Input Fields outputs unified keys and Retrieve Company Profile returns a structured JSON response.
- Check Format Result Payload for a clean JSON object in the output.
- When satisfied, toggle the workflow to Active for production use.
Watch Out For
- Airtop credentials can expire or lose access if your LinkedIn-authenticated profile gets logged out. If things break, check the Airtop Profile connection in Airtop first, then refresh the API key in n8n.
- If you later extend this workflow with batching (Split in Batches) or waiting for page loads, processing times vary. Bump up the wait duration if downstream steps fail because extraction returned an empty response.
- Classification fields like “Is this an automation agency?” depend on consistent rules. If the default logic feels too generic, adjust it early or you’ll be second-guessing the labels in your sheet every week.
Common Questions
Usually about 30 minutes if Airtop and Google Sheets are already connected.
Yes. No coding is required, but someone needs to map fields carefully once. After that, it runs like a template.
Yes. n8n has a free self-hosted option and a free trial on n8n Cloud. Cloud plans start at $20/month for higher volume. You’ll also need to factor in Airtop API usage costs based on how many company pages you extract.
Two options: n8n Cloud (managed, easiest setup) or self-hosting on a VPS. For self-hosting, Hostinger VPS is affordable and handles n8n well. Self-hosting gives you unlimited executions but requires basic server management.
You can adjust the Merge Input Fields and Format Result Payload steps to match your sheet columns and naming conventions. Common tweaks include changing the employee size brackets, adding your own “ICP fit” field, and splitting headquarters into separate city/state/country columns for cleaner filtering.
Usually it’s an expired Airtop API key or a LinkedIn-authenticated Airtop Profile that got logged out. Regenerate the key in Airtop, update it in n8n, and confirm the selected profile can still access LinkedIn. If you’re extracting lots of companies in a short window, you may also be hitting rate limits or page load variability, which means Airtop returns partial fields.
On n8n Cloud, capacity depends on your plan’s monthly executions, and each company lookup typically counts as one run. If you self-host, there’s no fixed execution cap, but your server resources become the limiter. In practice, most teams enrich in batches (like 20–100 companies at a time) so they can review results and avoid hammering LinkedIn all at once.
Often, yes, because the Airtop extraction and payload formatting is easier to control in n8n. You can merge inputs from different sources, standardize output once, and reuse the same workflow as a sub-workflow inside a larger lead gen system. Zapier and Make can work, but multi-step data shaping tends to get messy and expensive as the logic grows. If all you need is “URL in, row out,” you might not care. If you want scoring, branching rules, or CRM handoff next, n8n is usually the calmer choice. Talk to an automation expert if you want a quick recommendation.
Once this is running, your sheet becomes a reliable source of truth instead of a collage of pasted text. The workflow handles the repeatable extraction, so you can spend your time qualifying and reaching out.
Need Help Setting This Up?
Our automation experts can build and customize this workflow for your specific needs. Free 15-minute consultation—no commitment required.
