How to Extract Data from LinkedIn to Excel

Getting your hands on the valuable data within LinkedIn can be a game-changer for sales, marketing, and recruiting. But keeping that information locked on the platform limits what you can do with it. This guide will show you exactly how to extract data from LinkedIn to Excel, covering the safe, official methods and the more powerful automated approaches, so you can build dynamic contact lists, conduct market research, and streamline your outreach efforts.

Why Extract LinkedIn Data in the First Place?

Before jumping into the "how," it's helpful to understand the "why." Moving LinkedIn data into a spreadsheet unlocks a new level of analysis and utility that you just can't get within the platform itself. It turns a social network into a powerful database you can sort, filter, and integrate with your other business tools.

Here are a few common reasons people export LinkedIn data to Excel:

• Building a Targeted Sales Pipeline: Instead of manually tracking prospects in a clunky CRM or notebook, you can export a curated list of professionals who fit your ideal customer profile. In Excel, you can add notes, track outreach status (e.g., "Contacted," "Meeting Booked"), and prioritize your warmest leads.
• Sourcing Recruiting Candidates: Recruiters can use LinkedIn's powerful search filters to find potential candidates and then export that list to manage the entire hiring pipeline. You can track interview stages, team feedback, and contact details all in one organized spreadsheet.
• Conducting Market Research: Want to understand the employee landscape at a competitor? Curious about the common job titles in a specific industry? By extracting data, you can analyze company sizes, departmental structures, and employee distributions to gain valuable competitive intelligence.
• Streamlining Networking and Outreach: Creating a list of industry influencers, potential podcast guests, or collaborators is far easier to manage in Excel. You can organize contacts by niche, track your interactions, and plan a more strategic networking approach.

Method 1: The Official Way - Exporting Your Connections Directly

LinkedIn provides an official, built-in method for backing up your data, which includes a list of your 1st-degree connections. This is the safest way to get information and is fully compliant with LinkedIn's terms of service. The downside is that the dataset is fairly limited, but it's an excellent starting point.

Here’s how to do it step-by-step:

1. Log in to your LinkedIn account.
2. Click on your Me icon in the top right corner of the navigation bar, and select Settings & Privacy from the dropdown menu.
3. On the next page, click on the Data privacy tab on the left-hand navigation pane.
4. Look for the section titled "How LinkedIn uses your data" and find the option that says Get a copy of your data. Click it.
5. You'll be presented with two options. You can either download a larger data archive (which includes everything from posts to messages) or choose to export specific data files. For our purpose, select the second option.
6. Check the box for Connections. Then, click the Request archive button.

LinkedIn will process your request, which usually takes about 10 minutes. Once it's ready, you'll receive an email with a download link. After clicking the link and re-entering your password for security, you'll download a .zip file. Inside, you'll find a CSV file named Connections.csv.

You can open this file with any spreadsheet software like Excel or Google Sheets.

What This Export Includes (and What It Doesn't)

When you open the Connections.csv file, you'll see a few useful columns:

• First Name
• Last Name
• Company
• Position
• Date Connected

This is great for a basic list of your network. However, what's often more important is what this export is missing.

Major Limitations of the Official Method:

• No Email Addresses: For privacy reasons, LinkedIn stopped including email addresses in this export several years ago. This is the single biggest drawback for anyone doing sales or marketing outreach.
• No Profile URLs: There's no direct link back to the person's LinkedIn profile, which makes it harder to quickly reference their information.
• Only 1st-Degree Connections: You can't export lists of people outside your immediate network, such as from search results or a LinkedIn Group.

So, while the official export is a good way to back up your connections, it often isn't enough for serious lead generation or research. That’s where other methods come in.

Method 2: Using Third-Party Tools for Richer Data Extraction

To overcome the limitations of the native export, many professionals turn to third-party scraping and automation tools. These tools are designed to automatically visit LinkedIn profiles or search pages and extract the publicly available data into a structured format like a CSV or Excel file.

Important Disclaimer: The use of automated scripts or botting tools to scrape data from LinkedIn is a violation of their User Agreement. Doing so carries a risk of having your account temporarily restricted or permanently banned. Always use these tools responsibly, run them at a slow, human-like speed, and avoid extracting excessive amounts of data in a short period. Check the terms of service of any tool you consider using. This information is for educational purposes only.

These tools generally fall into two categories: browser extensions and standalone applications. They all follow a similar process to get you the data you need.

How Most LinkedIn Scrapers Work

1. Define Your Target Audience: First, you use LinkedIn's native search filters (or the even more powerful Sales Navigator filters) to create a highly specific list of people. You can filter by job title, industry, geographic location, company size, seniority level, and more. This is the most important step for getting quality data.
2. Set Up the Extraction Tool: Next, you launch the scraper tool, which is often a Google Chrome extension. You'll specify which data points you want to collect (e.g., profile URL, name, job title, company name, location, years of experience, etc.).
3. Run the Scrape: You then start the automation. The tool will begin visiting the LinkedIn search results page-by-page or each profile individually, copying the relevant information from the page's code. Good tools do this slowly to simulate real human browsing behavior, helping to keep your account safe.
4. Export to Excel/CSV: Once the tool has finished running, it will present you with the collected data. You can then download this list as a CSV file, ready for cleaning and use in Excel. Some tools even offer to find and verify email addresses for your contacts using their own databases, giving you a ready-to-use outbound list.

A few examples of well-known tools in this space include PhantomBuster, Lusha, Evaboot, and Octopus CRM. Each has its own focus, such as enriching data with contact info or integrating directly with a CRM.

Method 3: Cleaning and Organizing Your Scraped Data in Excel

Regardless of whether you used the official export or a third-party tool, the raw data you get is often messy. Cleaning it up in Excel is a crucial step before you can actually use it. A clean, organized list is far more effective for outreach and analysis.

Step 1: Open the CSV and Remove Duplicates

First, open your downloaded CSV file in Excel. A common issue, especially with data scraped from multiple searches, is duplicate entries. You can easily remove these:

1. Select the entire dataset by clicking the triangle in the top-left corner of the sheet.
2. Go to the Data tab in Excel's ribbon.
3. Click on Remove Duplicates.
4. A dialog box will appear. Check the box for "My data has headers" if applicable, and then check all the columns to ensure that it only removes rows that are identical across the board. Click OK.

Step 2: Split "Full Name" into First and Last Names

Many scrapers export a "Full Name" column, but for personalizing emails (e.g., "Hi John,"), you need a separate "First Name" column.

1. Insert two new columns next to your "Full Name" column and label them "First Name" and "Last Name".
2. Select the "Full Name" column.
3. Go to the Data tab and click Text to Columns.
4. Choose Delimited and click Next.
5. Check the box for Space and click Next.
6. In the final step, set the destination for your new columns to the cell where your "First Name" column begins (e.g., $B$1 if "First Name" is in column B). Click Finish.

Step 3: Clean Up Text with TRIM and CLEAN

Scraped data can sometimes include pesky hidden characters or extra spaces that mess up your filters and formulas. The TRIM and CLEAN functions are your best friends here.

• The TRIM function removes extra leading or trailing spaces from text.
• The CLEAN function removes non-printable characters often found in web-scraped data.

To use them, you would typically create a temporary "helper" column. For example, to clean up a "Job Title" column (let's say it's column D):

1. Create a new column next to it.
2. In the first cell of the new column, enter the formula: =TRIM(CLEAN(D2))
3. Drag this formula down for all your rows.
4. Finally, copy the cleaned values from your helper column and use "Paste Special -> Values" to paste them over the original "Job Title" column. You can then delete the helper column.

Step 4: Standardize Job Titles and Company Names (Advanced)

This is a more manual process but incredibly valuable. Salespeople might list themselves as "Account Executive," "AE," or "Sales Rep." Standardizing these helps with analysis. You can use Excel's Find and Replace tool (Ctrl+H) to start this process. For instance, you could find all instances of "AE" and replace them with "Account Executive." Fixing these inconsistencies will make sorting and filtering your data far more reliable.

Final Thoughts

Extracting data from LinkedIn to Excel opens up a world of possibilities for growing your business, whether you're using the direct export for a basic list or leveraging advanced tools for richer datasets. The key is to start with a clear goal, follow the steps carefully, and always handle the data you collect responsibly and ethically.

This process of manually pulling data from platforms, cleaning it in spreadsheets, and trying to act on the insights is something we see all the time. Our work on Graphed is focused on eliminating that friction. While Excel is great for static lists, the real challenge for most teams is connecting this lead data to live performance data from sources like Salesforce, HubSpot, or Google Analytics. We built an AI data analyst that allows you to automate this reporting, so instead of spending hours in spreadsheets, you can ask a question in plain English - like "Show me our lead-to-close rate by salesperson this quarter" - and get an instant, live dashboard.