How to Find City from LinkedIn Profile URL: Complete 2026 Guide

For SDRs building prospecting lists, recruiters targeting specific markets, or sales teams running geo-targeted campaigns, knowing a prospect’s exact city location makes the difference between generic outreach and personalized, high-converting messages. A prospect in San Francisco has different pain points than one in Austin, even within the same industry.

The challenge? LinkedIn profile URLs don’t directly expose city data. You see linkedin.com/in/john-smith, but not where John actually lives. This guide shows you exactly how to extract city location from LinkedIn profile URLs using modern data enrichment workflows.

Extract City Data from LinkedIn Profiles in Google Sheets

Derrick enriches LinkedIn profiles with location data, including city, directly in your spreadsheet. Find city, state, and country from any profile URL.

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What You’ll Learn and Expected Results

By the end of this tutorial, you’ll be able to:

• Extract city location from LinkedIn profile URLs in bulk (hundreds or thousands of profiles)
• Understand the two types of location data available: personal location and company city
• Set up an automated workflow that enriches profile URLs with city data in under 10 minutes
• Clean and normalize location data for accurate geo-targeting

Expected Result: Transform a list of LinkedIn profile URLs into a complete dataset with accurate city information, ready for geo-targeted outreach campaigns. Most tools achieve 85-95% city match rates on public LinkedIn profiles.

Prerequisites

Before starting, you’ll need:

• A list of LinkedIn profile URLs (in a spreadsheet or CSV file)
• Access to a data enrichment tool (we’ll use Derrick for this guide, but the principles apply to most tools)
• 10-15 minutes to set up the workflow
• Basic understanding of Google Sheets or Excel

Time estimate: 10 minutes setup + 5-10 minutes per 100 profiles enriched (automated)

Understanding LinkedIn Location Data: The Two Types You Can Extract

Before extracting city data, it’s crucial to understand LinkedIn stores location information in two distinct ways.

Personal Location: Where the Person Lives

This is the location users set on their LinkedIn profile under “Contact Info.” It appears as “Lives in [City, State/Region]” and typically reflects their current residence. When someone says “I’m based in Boston,” this is the data point you’re accessing.

Format examples:

• San Francisco Bay Area
• Austin, Texas, United States
• London, England, United Kingdom
• Greater New York City Area

According to Bright Data’s LinkedIn scraping analysis, approximately 92% of active LinkedIn profiles display a personal location, making this a highly reliable data point for prospecting.

Company Location: Where They Work

The second type is derived from their current employer’s headquarters or office location. This comes from the LinkedIn company page linked to their job experience. For instance, if someone works at Salesforce, their company location would be “San Francisco, California” even if they personally live in Austin and work remotely.

Why this matters for prospecting: A remote SDR at HubSpot might live in Denver but work for a Boston-based company. If you’re selling office furniture, the personal location matters. If you’re selling B2B SaaS that integrates with HubSpot’s tech stack, the company location is more relevant.

For most B2B prospecting use cases, you’ll want the personal location because it indicates:

• Local market conditions affecting budget and priorities
• Time zone for outreach timing
• Regional pain points and regulations (GDPR in EU, CCPA in California)
• Cultural context for messaging

Now that you understand the distinction, let’s extract this data.

Step 1: Prepare Your LinkedIn Profile URLs

First, organize your data properly to maximize enrichment success rates.

Open your spreadsheet (Google Sheets or Excel) and create a column header labeled “LinkedIn Profile URL” or “Profile URL.”

Format your URLs correctly. LinkedIn profile URLs come in several formats, and most enrichment tools handle all of them:

• Standard format: https://www.linkedin.com/in/john-smith-12345678/
• Sales Navigator format: https://www.linkedin.com/sales/lead/ACwAAABcD...
• Shortened format: linkedin.com/in/john-smith

Pro tip: Always use the full HTTPS URL format (https://www.linkedin.com/in/username) for best results. Some tools struggle with shortened URLs without the protocol.

Clean your data before enrichment:

1. Remove duplicate URLs using your spreadsheet’s “Remove duplicates” function
2. Check for broken or incomplete URLs (look for URLs that don’t contain “/in/” or “/sales/”)
3. Remove any URLs that point to company pages instead of individual profiles (these contain “/company/” instead of “/in/”)

Result at this stage: A clean column of valid LinkedIn profile URLs, ready for enrichment.

Step 2: Choose Your Extraction Method (Manual vs. Automated)

You have two main approaches to extract city data from LinkedIn profiles: manual scraping or automated enrichment. Here’s how to decide.

Method A: Manual Extraction (0-50 profiles)

If you’re working with a small list, you can manually visit each LinkedIn profile and copy the location information.

Process:

1. Click each LinkedIn profile URL
2. Look for the location under the person’s name and headline
3. Copy the city/location text manually into your spreadsheet

When to use manual extraction:

• You have fewer than 50 profiles
• You need 100% accuracy with visual verification
• Profiles are private or connection-restricted

Limitation: This method doesn’t scale. At 2 minutes per profile, 100 profiles = 3+ hours of manual work.

Method B: Automated Enrichment (50+ profiles)

For any list larger than 50 profiles, automated enrichment is dramatically more efficient. Enrichment tools access LinkedIn’s public data programmatically and extract location information in bulk.

Benefits:

• Process 100 profiles in 5-10 minutes (vs. 3+ hours manually)
• Structured, consistent data output (no copy-paste errors)
• Additional data points extracted simultaneously (job title, company, experience)

When to use automated enrichment:

• You have 50+ profiles to process
• You need repeatable workflows for ongoing prospecting
• You want additional profile data beyond just city

For the rest of this guide, we’ll focus on automated enrichment using Derrick, which operates natively in Google Sheets.

Result at this stage: Decision made on extraction method based on your list size and needs.

Step 3: Set Up LinkedIn Profile Enrichment in Derrick

This is where you’ll connect your LinkedIn profile URLs to Derrick’s enrichment engine. If you’re using a different tool, the logic remains similar—you’re mapping profile URLs to an enrichment API.

Install Derrick in Google Sheets:

1. Open your Google Sheet with the LinkedIn profile URLs
2. Go to Extensions > Add-ons > Get add-ons
3. Search for “Derrick” in the Google Workspace Marketplace
4. Click “Install” and grant the necessary permissions

Launch Derrick:

1. Once installed, click Extensions > Derrick > Launch Derrick
2. The Derrick sidebar appears on the right side of your sheet

Select the enrichment feature:

In the Derrick sidebar, you’ll see multiple features. For extracting city from profile URLs, select “LinkedIn Profile Scraper” (also called “Enrich Lead Data” in some versions).

This feature scrapes public LinkedIn profile data, including location information, job title, company, experience, skills, and more—all from the profile URL.

Configure your column mapping:

1. In the dropdown menu, select the column header that contains your LinkedIn profile URLs
2. Derrick automatically detects columns with “LinkedIn,” “Profile,” or “URL” in the header
3. If your column isn’t auto-detected, manually select it from the dropdown

Result at this stage: Derrick is configured and ready to enrich your LinkedIn profile URLs with location data.

Step 4: Run the Enrichment and Extract City Data

Now execute the enrichment process to populate your spreadsheet with city information.

Click the “Enrich” button in the Derrick sidebar. The enrichment process begins automatically.

What happens behind the scenes:

1. Derrick takes each LinkedIn profile URL from your selected column
2. Accesses the public LinkedIn profile page (no Sales Navigator required)
3. Extracts the location field visible under the person’s name
4. Parses the location string into structured components: city, state/region, country
5. Writes the enriched data back to your Google Sheet in new columns

Monitor the progress:

Derrick displays a progress indicator showing how many profiles have been processed. For 100 profiles, expect 5-10 minutes depending on server load.

Review the enriched columns:

Once complete, Derrick creates several new columns in your sheet, including:

• Location (full location string, e.g., “Austin, Texas, United States”)
• City (parsed city only, e.g., “Austin”)
• State/Region (e.g., “Texas”)
• Country (e.g., “United States”)
• Job Title, Company Name, Company LinkedIn URL, and 40+ other attributes

The City column is what you need for geo-targeted prospecting.

Result at this stage: Your Google Sheet now contains a “City” column populated with location data extracted from each LinkedIn profile URL.

Step 5: Extract Company City (Optional Secondary Enrichment)

If you also want to know where the person’s company is located (not just where they live), you can run a second enrichment step on the company LinkedIn URLs.

Why you might need this:

Marie, a sales ops manager at a SaaS company, lives in Denver but works for a San Francisco-based startup. If you’re targeting companies in the Bay Area tech scene, you care about San Francisco (company city), not Denver (personal city).

The two-step process:

After completing Step 4, you now have both:

1. Personal city (from the profile enrichment)
2. Company LinkedIn URL (also extracted in Step 4)

Run company enrichment:

1. In Derrick, select the “LinkedIn Company Scraper” feature (also called “Enrich Company Data”)
2. In the dropdown, select the column containing Company LinkedIn URLs (created in Step 4)
3. Click “Enrich”

What you get:

Derrick enriches the company URLs with company-level data, including:

• Company City (headquarters location)
• Company Address
• Company Country
• Company Industry, Employee Count, Specialties, and more

Now you have both personal city and company city in your spreadsheet, allowing you to filter and segment based on either.

Use case example:

Tom, an SDR selling office management software, filters for prospects who:

• Live in Texas (personal city) → relevant local tax incentives
• Work at companies with 50-200 employees (company size)
• In the tech industry (company industry)

This level of segmentation dramatically improves outreach relevance.

Result at this stage: Optional company location data added, giving you both personal and corporate city information for each profile.

Understanding Location Data Formats and Edge Cases

LinkedIn’s location data isn’t always perfectly structured. Here’s how to handle common formatting variations you’ll encounter.

Broad vs. Specific Locations

LinkedIn allows users to choose their location granularity. Some profiles display:

• Specific city: “Austin, Texas, United States”
• Metropolitan area: “San Francisco Bay Area”
• Country only: “United States”
• Region: “Greater New York City Area”

According to a study by Datablist analyzing 100,000 enriched profiles, approximately:

• 68% display city-level precision
• 22% display metro area or region
• 7% display country only
• 3% have no location data

How to handle broad locations:

If you see “San Francisco Bay Area” but need a specific city, you’ll need to make assumptions based on your targeting criteria. For instance:

• “San Francisco Bay Area” could be San Francisco, San Jose, Oakland, or Palo Alto
• “Greater New York City Area” could be Manhattan, Brooklyn, Queens, or Jersey City

Most enrichment tools (including Derrick) extract the location string exactly as it appears on LinkedIn. They don’t attempt to guess or normalize broad areas into specific cities, which prevents false positives.

International Location Formats

Different countries format locations differently:

• US format: City, State, Country (e.g., “Seattle, Washington, United States”)
• UK format: City, Country (e.g., “London, United Kingdom”)
• EU format: City, Region, Country (e.g., “Lyon, Auvergne-Rhône-Alpes, France”)

Your enrichment tool should preserve the original format. If you need standardized formatting for database imports, you’ll need a normalization step (covered in the next section).

Empty or Missing Locations

Some profiles legitimately have no location data:

• Reasons for missing location:
  • Profile set to private or connection-restricted
  • User never filled in the location field
  • Profile is inactive or outdated

When enrichment returns an empty location field, it typically means the data isn’t publicly visible. Don’t assume this means the profile is invalid—it might just be privacy-restricted.

Cleaning and Normalizing Location Data

Raw location data from LinkedIn often needs cleaning before you can use it effectively in campaigns or CRM systems.

Problem 1: Inconsistent Formatting

Symptom: Your City column shows variations like:

• “San Francisco”
• “San Francisco, CA”
• “SF Bay Area”
• “San Francisco Bay Area”

Impact: When you filter for “San Francisco,” you miss prospects in “SF Bay Area”

Solution: Use a normalization rule or formula to standardize city names

In Google Sheets, create a helper column with a formula like:

=IF(OR(REGEXMATCH(B2,"San Francisco"),REGEXMATCH(B2,"SF"),REGEXMATCH(B2,"Bay Area")),"San Francisco",B2)

This formula checks if column B (Location) contains any San Francisco variation and normalizes it to “San Francisco.”

Alternative no-code solution: Use Derrick’s “Data Normalization” feature, which automatically cleans and standardizes location strings based on common patterns.

Problem 2: Special Characters and Encoding Issues

Symptom: Cities with accents or special characters display incorrectly:

• “São Paulo” becomes “S?o Paulo”
• “Montréal” becomes “Montréal”

Impact: Filters and searches fail to match these locations

Solution: Ensure your spreadsheet and CRM use UTF-8 encoding, which supports international characters. When exporting to CSV, select “UTF-8” as the encoding format.

Problem 3: Outdated Location Data

Symptom: A prospect’s LinkedIn shows “Austin, Texas” but they’ve recently moved to Miami (and haven’t updated LinkedIn)

Impact: Your geo-targeted message references Austin when they’re no longer there

Solution: For high-value prospects, manually verify location through:

• Recent LinkedIn posts or activity (often shows current city)
• Company announcements about office locations
• Other social profiles (Twitter bio, personal website)

According to HubSpot’s data quality research, approximately 35% of CRM location data becomes outdated within 12 months. For time-sensitive geo-targeting, consider re-enriching profiles quarterly.

Alternative Method: Finding City from Company Data

If you don’t have direct profile URLs but have company information, you can still infer likely city location through a company-based approach.

When to Use This Method

This approach works when:

• You have company names but not individual profile URLs
• You’re targeting decision-makers at specific companies
• You need to build a prospecting list from scratch

The Workflow

Step 1: Start with company names or company LinkedIn URLs in your spreadsheet

Step 2: Use Derrick’s “LinkedIn Company Finder” or “LinkedIn Company Scraper” to enrich company data

This returns:

• Company headquarters city
• Company locations (if multiple offices)
• Employee count and industry

Step 3: Use the company headquarters city as a proxy for employee location

Limitation: This only works for employees likely to be at headquarters. Remote employees or those at satellite offices won’t match the HQ city. For distributed teams, you’ll need individual profile enrichment (Method covered in Steps 1-4).

Use case: Sarah, recruiting for a Bay Area startup, targets employees at competitor companies. She enriches competitor company pages to get HQ locations, then runs LinkedIn searches filtered by those cities to build her candidate list.

The 5 Most Common Errors (and How to Fix Them)

Problem 1: No Location Data Returned

Symptom: Enrichment completes but the City column is blank for many profiles

Impact: Can’t geo-target these prospects

Solution:

1. Check if the profile URLs are valid (visit a few manually to verify they load)
2. Verify the profiles are set to public (private profiles don’t expose location data)
3. For profiles with no public location, try the company enrichment method as a fallback
4. Accept that some profiles (typically 5-10%) simply don’t have public location data

Problem 2: Tool Returns “Greater [City] Area” Instead of Specific City

Symptom: Location shows “Greater Boston Area” but you need “Boston”

Impact: Filtering by exact city names excludes these broad locations

Solution: Create a mapping table that converts metro areas to primary cities. For example:

Use VLOOKUP or INDEX-MATCH in your spreadsheet to automatically convert metro areas to cities.

Problem 3: International Locations Don’t Match Your Database

Symptom: LinkedIn returns “München, Bavaria, Germany” but your CRM expects “Munich”

Impact: Location-based automation rules fail to trigger

Solution: Build a normalization table for international cities with alternative names:

Problem 4: Enrichment Tool Gets Rate-Limited

Symptom: Enrichment stops midway with an error like “Rate limit exceeded” or “Too many requests”

Impact: Only partial data extracted

Solution:

1. Most tools (including Derrick) have built-in rate limiting to prevent this
2. If you hit limits, split your list into smaller batches (e.g., 500 profiles at a time)
3. Wait 10-15 minutes between batches to stay within API quotas
4. For large-scale enrichment (10,000+ profiles), consider enterprise plans with higher rate limits

Problem 5: Profiles Show Old Locations

Symptom: Enriched data shows a city where the person used to live, not their current location

Impact: Outreach references the wrong location, appearing impersonal or outdated

Solution:

1. Filter for profiles with recent activity (updated in last 3-6 months)
2. Cross-reference location with recent posts or job changes
3. Use real-time enrichment tools that fetch live data (not cached databases)
4. For critical accounts, manually verify location before outreach

Advanced Use Cases: Beyond Basic City Extraction

Once you have city data, here’s how top sales teams use it strategically.

Use Case 1: Geo-Targeted Outreach Campaigns

Marie, an enterprise SDR at a martech company, segments her prospect list by city and personalizes cold emails with city-specific references:

“Hi [Name], I noticed you’re based in Austin. With SXSW just around the corner, I imagine your team is planning some major activations…”

Result: 34% higher open rates compared to generic outreach (according to Salesforce’s State of Sales report).

Use Case 2: Regional Sales Territory Assignment

Tom, a sales ops manager, uses enriched city data to automatically assign leads to regional reps:

1. Enriches incoming leads with city data
2. Uses a territory mapping table to assign leads based on city
3. Triggers automated handoff to the appropriate rep

Result: Leads reach the right rep 2.3 hours faster, improving qualification rates by 18%.

Use Case 3: Event-Based Prospecting

Jonathan, head of growth at a B2B SaaS company, identifies prospects in cities where his company is hosting events:

1. Filters enriched list for prospects in “San Francisco”
2. Triggers an email sequence: “We’re hosting a dinner for revenue leaders in SF on March 15th…”

Result: 41% attendance rate from targeted prospects vs. 12% from general invitations.

Use Case 4: Market Expansion Analysis

Before expanding to a new city, this strategy helps validate market size:

1. Enrich a database of target personas (e.g., “VP of Sales at 50-500 person SaaS companies”)
2. Group by city to see concentration of prospects
3. Discover “Austin has 240 qualified prospects vs. Salt Lake City with 45”

Result: Data-driven decision to prioritize Austin market expansion, leading to 3x faster ramp than previous expansions without this analysis.

Use Case 5: Compliance and GDPR Segmentation

For companies with regional compliance requirements, city data enables automatic segmentation:

1. Enrich profiles with city and country
2. Flag EU-based prospects for GDPR-compliant workflows
3. Filter California prospects for CCPA compliance

Result: Reduced legal risk and maintained proper opt-in/opt-out tracking by region.

Legal and Privacy Considerations

Scraping and enriching LinkedIn profile data operates in a grey area legally. Here’s what you need to know to stay compliant.

What LinkedIn’s Terms of Service Say

LinkedIn’s User Agreement prohibits:

• Automated scraping via bots or crawlers
• Using scraped data for commercial purposes without consent
• Circumventing technical measures that restrict data access

However, courts in multiple jurisdictions have ruled that scraping publicly accessible data (data visible without logging in) falls under fair use and doesn’t violate computer fraud laws.

Key case: hiQ Labs v. LinkedIn (2019) – The Ninth Circuit Court ruled that LinkedIn cannot prevent scraping of public profile data. However, this ruling applies specifically to US law.

GDPR Implications (EU Prospects)

If you’re enriching profiles of EU residents, GDPR applies:

Requirements:

• Legitimate interest as your legal basis for processing personal data
• Transparency: Your privacy policy must disclose data enrichment activities
• Right to access and deletion: Prospects can request their data be removed from your systems

Practical compliance:

1. Limit enrichment to necessary data points (city, job title, company)
2. Don’t enrich sensitive personal data (race, religion, health, political views)
3. Implement a process for data deletion requests
4. Document your legitimate interest assessment

CCPA Considerations (California Prospects)

For prospects in California, CCPA grants them rights similar to GDPR:

• Right to know what personal information you’ve collected
• Right to deletion
• Right to opt-out of data sales

Best practice: Include a CCPA-compliant privacy policy link in your outreach emails and website.

Ethical Data Use Guidelines

Beyond legal compliance, ethical use of enriched data builds trust:

1. Use data for intended purpose only: If you enriched profiles for sales outreach, don’t repurpose for marketing without consent
2. Respect location privacy: Don’t use city data to stalk or harass prospects
3. Be transparent: If asked how you got someone’s location, be honest about public profile enrichment
4. Honor opt-outs promptly: If someone asks to be removed from your outreach list, comply immediately

According to a Gartner survey, 78% of B2B buyers are more likely to engage with companies that demonstrate responsible data practices.

Tools Comparison: Derrick vs. Alternatives

While this guide uses Derrick, several other tools can extract city from LinkedIn profiles. Here’s how they compare.

Why choose Derrick for city extraction:

1. Native Google Sheets integration: No exports, no imports—work directly in your spreadsheet
2. No Sales Navigator required: Unlike some tools, Derrick accesses public profiles without needing an expensive Sales Navigator subscription (saves $1,200/year)
3. Credit rollover: Unused credits carry forward, unlike subscription models that reset monthly
4. Transparent pricing: Starting at $9/month for 4,000 credits vs. enterprise-only pricing from competitors

For more complex scenarios like combining LinkedIn city data with other enrichment sources (website scraping, email verification, phone lookup), consider tools like Clay that aggregate multiple data providers.

Workflow Automation: Connecting City Data to Your CRM

Once you’ve extracted city data, integrate it into your sales workflow for maximum impact.

Option 1: Direct CRM Import (Manual)

Process:

1. Enrich your LinkedIn profile URLs with city data in Google Sheets (Steps 1-4)
2. Export the enriched sheet as CSV
3. Import the CSV into your CRM (HubSpot, Salesforce, Pipedrive)
4. Map the “City” column to your CRM’s location field

Best for: One-time enrichment of existing lead lists

Option 2: Zapier Automation (No-Code)

Process:

1. Set up a Zap: “When new row added to Google Sheets → Enrich with Derrick → Create/Update CRM contact”
2. Configure Derrick to auto-enrich new rows in your sheet
3. Zapier automatically syncs enriched data (including city) to your CRM

Best for: Ongoing enrichment of leads as they’re added to your sheet

Option 3: Make (Integromat) for Complex Workflows

Process:

1. Build a Make scenario: “LinkedIn profile URL → Enrich via Derrick → Check if city matches target territories → If yes, add to outreach sequence”
2. Add conditional logic based on city
3. Automate lead scoring based on location

Best for: Complex workflows with multiple conditions and branching logic

Option 4: API Integration (Developer-Required)

For engineering teams, use Derrick’s API or alternatives like Bright Data to programmatically enrich profiles:

import requests

api_key = "YOUR_DERRICK_API_KEY"
profile_url = "https://www.linkedin.com/in/john-smith/"

response = requests.post(
    "https://api.derrick-app.com/v1/enrich",
    headers={"Authorization": f"Bearer {api_key}"},
    json={"profile_url": profile_url, "fields": ["city", "state", "country"]}
)

data = response.json()
city = data.get("city")
print(f"City: {city}")

Best for: High-volume enrichment integrated directly into your application or data pipeline

Conclusion: Transform Location Data into Revenue

Extracting city from LinkedIn profile URLs might seem like a minor data enrichment step, but as we’ve seen, location intelligence powers some of the most effective B2B prospecting strategies—from geo-targeted outreach to market expansion analysis.

Here’s your action plan:

1. Start small: Enrich 50-100 profiles to validate the workflow
2. Test messaging: Run A/B tests comparing generic vs. location-personalized outreach
3. Scale gradually: Once you see improved conversion rates, scale to your full prospect database
4. Automate: Set up Zapier or Make integration to enrich new leads automatically

The sales teams seeing the biggest wins from location data share one trait: they act on it immediately. Don’t let enriched data sit in a spreadsheet—use it to personalize your very next outreach campaign.

Start Enriching LinkedIn Profiles with City Data

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FAQ

Can I extract city from LinkedIn profiles without logging in?

Yes, but only for profiles set to public. Public profiles display basic information (name, headline, location, current company) without requiring authentication. However, approximately 30-40% of LinkedIn profiles are private or connection-restricted, meaning their location data won’t be accessible without connecting with them first.

How accurate is city data extracted from LinkedIn profiles?

City data accuracy depends on two factors: user-maintained profile accuracy and enrichment tool quality. LinkedIn profiles are self-reported, so accuracy depends on users keeping their location updated. Studies show 85-95% of extracted city data matches reality for active profiles (updated in last 6 months). For profiles not updated in over a year, accuracy drops to 60-70%.

Do I need LinkedIn Sales Navigator to extract city location?

No. Basic location data (city, state, country) is visible on public LinkedIn profiles and doesn’t require Sales Navigator. However, Sales Navigator provides more detailed location filtering and search capabilities if you’re prospecting directly on LinkedIn rather than enriching existing lists.

Is it legal to scrape city data from LinkedIn profiles?

Scraping publicly visible data from LinkedIn profiles exists in a legal grey area. The hiQ Labs v. LinkedIn court case established that scraping public data doesn’t violate the Computer Fraud and Abuse Act in the US. However, LinkedIn’s Terms of Service prohibit automated scraping. Use enrichment responsibly, limit to public data only, and comply with GDPR and CCPA for EU and California prospects respectively.

What’s the difference between personal city and company city?

Personal city is where the individual lives (shown under their name on LinkedIn). Company city is the location of their employer’s headquarters (shown on the company page). For B2B prospecting, personal city is usually more relevant because it indicates the prospect’s local market conditions, time zone, and regulatory environment. Use company city when targeting corporate decision-makers at specific office locations.

Can I extract city from LinkedIn company pages instead of profiles?

Yes. LinkedIn company pages display headquarters location and often list multiple office locations. Use Derrick’s “LinkedIn Company Scraper” feature to enrich company URLs with location data. This method works well for account-based marketing where you’re targeting companies rather than individuals. However, company location doesn’t tell you where specific employees are located (especially remote workers).