How to Export Contacts From LinkedIn to Excel: The 3 Paths, the 7 Columns, and What Is Missing

You want your LinkedIn network in a spreadsheet. Not on a profile page, not in a feed, in rows and columns you can sort, filter and send to a sequencing tool. The good news is that LinkedIn hands you that file for free. The bad news is that almost nobody explains what is actually inside it, and the file breaks the moment you double-click it in Excel.

This guide covers the three different exports people confuse, the exact eight clicks that produce the connections file, the seven columns you get, the nine fields you do not get, and the one formatting step that stops Excel from mangling every accented name in your network.

The Three LinkedIn Exports People Confuse

Most of the frustration around this topic comes from three different jobs wearing the same name. Before touching a single setting, work out which one you actually need.

That first row is the only one LinkedIn gives you for free, and it is the one this guide starts with. Rows two and three need a different approach entirely, which is covered further down. If you are unsure what your account is allowed to do at all, the ceilings are documented in our breakdown of what LinkedIn actually limits in 2026.

How to Export Contacts From LinkedIn to Excel in 8 Steps

This is the native route. It works on a free account, it costs nothing, and it is the only method LinkedIn officially supports for pulling your own network out.

1. Click your profile photo in the top navigation bar, under Me.
2. Choose Settings and Privacy.
3. Open the Data Privacy tab in the left column.
4. Click Get a copy of your data.
5. Select Want something in particular? and tick Connections. Ticking only this box is what gets you the fast file rather than the full archive.
6. Click Request archive and confirm with your password.
7. Wait for the email. LinkedIn states the targeted file is typically ready in about ten minutes, while a complete archive of everything can take up to 24 hours.
8. Download the archive from the link in that email and unzip it. The file you want is Connections.csv.

At this point you have a CSV, not an Excel file, and that distinction is about to cost you twenty minutes if you do the obvious thing.

Why Your Export Breaks When You Open It in Excel

Double-clicking Connections.csv is the single most common mistake in this workflow. Two things go wrong at once.

The header is not on row one. LinkedIn puts a short notice at the top of the file explaining that some members chose not to share their email address. Excel reads that notice as data, so your column headers land several rows down and every filter you apply afterwards is wrong.

The encoding gets guessed. The file is UTF-8. Excel on Windows often assumes a regional encoding instead, so every non-ASCII character in your network turns into noise. Names like Jose, Muller and Lofgren come through mangled, and a mangled name is a name you cannot use in a first line of outreach.

The fix is to import rather than open. In Excel, go to Data, then From Text/CSV, pick the file, and in the preview pane set File Origin to 65001: Unicode (UTF-8) and the delimiter to comma. Delete the notice rows, then promote the real header row. Two minutes, and the file is clean.

If you would rather skip the encoding question entirely, uploading the CSV to Google Sheets handles UTF-8 correctly by default. The trade-offs between the two surfaces are covered in our comparison of the five ways to get LinkedIn data into a spreadsheet.

The 7 Columns You Get, and the 9 You Do Not

Here is the part almost every guide skips. The connections export is a thin file. It identifies people, it does not qualify them.

The email column is the disappointment. LinkedIn only includes an address when that member has allowed their connections to see it, so on a typical network most rows in that column are empty. The file also carries no phone number, no company domain, no company size, no industry, no headquarters country, no funding data, no tech stack, no seniority label and no company LinkedIn URL. Nine fields that any real targeting exercise needs, and none of them are in the box.

The Company and Position columns deserve a second warning. They are a snapshot of the moment you exported, and job data ages faster than people expect. A network you exported eighteen months ago is a network where a meaningful share of the titles are simply wrong. We measured how quickly this happens in the LinkedIn data decay report.

How to Export Contacts From LinkedIn to Excel When the Search Page Has No Button

This is the second job, and it is the one people are usually really asking about. You have run a search, you have 400 people who match your criteria, and you want them in a sheet. LinkedIn offers no export here on any account tier, free or paid.

Three honest paths exist.

Manual capture. Copy the profile URLs page by page. Fine for twenty rows, unusable past fifty, and it burns an afternoon you will not get back.

A browser extension. A third-party extension runs inside your live LinkedIn session, walks the result pages and produces a CSV. It is the only way to capture data that exists solely on a rendered search page. The cost is that the traffic comes from your own account, so your session carries the volume and the risk.

An enrichment layer keyed on identifiers. Rather than scraping the search page, you collect the profile URLs and then enrich them from outside LinkedIn. Nothing runs in your browser session, so there is no account exposure and no profile-view notification sent to the person you are researching. If you only have names and companies rather than URLs, the reverse step is covered in how to work between names and LinkedIn URLs.

For most teams the practical answer is a combination: capture the URL list once, then treat every enrichment afterwards as a separate, repeatable job that does not touch LinkedIn at all.

Turning the Export Into a File You Can Actually Prospect With

A clean import gives you a readable file. It does not give you a workable one. The gap between the two is one enrichment pass.

The URL column is the key that unlocks everything else. Feed it into an enrichment step and the nine missing fields come back as new columns next to the rows you already have. Working from the sidebar in the sheet where your list already lives keeps the whole thing in one place, with no CSV round-trip and no second tool to learn.

The order that wastes the least budget is always the same:

1. Enrich the profile first. Enrich Leads costs 1 credit per profile, is available on the free plan as well as paid ones, and returns the current title, company, location and seniority. This is the step that tells you which rows are worth spending real money on.
2. Enrich the company second. Enrich Companies costs 1 credit per company and adds domain, headcount, industry and location. Run it on the deduplicated company list, not once per person, or you pay for the same account fifteen times.
3. Qualify, then buy contact data. Only now filter down to the rows that match your criteria. Email Finder is 5 credits per email and bills only on a result found. Phone Finder is 150 credits per phone, also billed on results only, which makes it the one step you never run on an unqualified list.

That sequence matters more than the tool. Running a phone lookup across an unfiltered 2,000-row network export is the fastest way to spend a monthly credit allowance on people who were never going to be a fit. Qualify on the cheap fields, then spend on the expensive ones.

How to Export Contacts From LinkedIn to Excel, or Skip the File Entirely

Excel is the right destination when the file is a deliverable. A board pack, a territory plan, a list someone in finance will open. It handles large files well, it works offline, and pivot tables remain excellent.

A connected spreadsheet is the right destination when the file is a workflow. Enrichment runs in place, colleagues see the same version, and there is no download-edit-reupload loop. Since a network export is something most teams refresh every quarter rather than once, the workflow surface usually wins.

You are not limited to a spreadsheet at all. The same enrichment is reachable three ways, and the right one depends on where the work lives rather than on any ranking of the surfaces:

• A list you are cleaning by hand: the Google Sheets sidebar, which enriches the rows in the sheet your list already sits in.
• A question you are asking inside a chat assistant: the MCP server, which lets Claude, ChatGPT or any MCP client pull the same data conversationally.
• A pipeline that runs without you: the REST API, for scheduled refreshes and CRM syncs.

All three read the same data layer, so a workflow that starts as a manual quarterly export can move to an automated refresh later without rebuilding anything.

What the Whole Thing Costs

The export itself is free and always will be. The cost sits entirely in the refill.

Take a realistic 1,500-connection network. Enriching every profile is 1,500 credits. Deduplicating to roughly 900 distinct companies and enriching those is 900 credits. If qualification leaves you with 200 genuinely relevant people and you find emails for most of them, that is around 1,000 credits at 5 per result. Phones, if you need them, are the expensive line at 150 credits each, which is precisely why they come last and only on the shortlist.

The free plan includes 100 credits per month, which is enough to test the sequence on a sample of your network before committing to anything. Paid plans start at 9 EUR per month for MINI and run up to 320 EUR per month for SCALE, where the per-credit cost bottoms out at 0.0016 EUR. Whether you are cleaning 200 rows or 200,000, the same sequence applies and the same sidebar runs it.

The honest summary: LinkedIn gives you a list of names for free, and names are not a pipeline. The export is step one of two, and the second step is the one that decides whether the file is worth having.

Try Derrick free on your own connections export and see how many of the nine missing fields come back on your first 100 credits.