Company Enrichment in Excel: The Workflow That Survives Contact With Real Data

Company enrichment in Excel means adding facts about each company that your spreadsheet does not contain. Things like the domain, the industry, the employee count, the head office country. Excel is excellent at joining and cleaning what you already have. It cannot invent a fact that is missing. And that one sentence explains almost every frustrating afternoon I have spent with a company list.

I want to tell you about the Munich exhibitor list first, because I got it wrong in three different ways before I got it right.

Let’s get into it.

📌 TL;DR: Excel joins and reshapes the data you already own, so no formula will add industry or employee count on its own. The workflow that survives real data: normalise the company-name column, resolve those names to domains, enrich on the domain, then bring the columns back into your workbook.

What Is Company Enrichment in Excel?

Company enrichment in Excel is the job of filling empty columns beside a list of company names. You start with names. You finish with a profile.

The fields people actually want are pretty consistent. Domain, industry, employee count, head office city and country, founding year, a revenue band, the LinkedIn page, a switchboard phone number, and sometimes the technologies the company runs. Together those make up its firmographic data, which is just a formal name for the descriptive facts about a business.

Here’s the part that trips people up. Those facts have to come from somewhere outside your file. A company database holds them, or a colleague’s export holds them, or you research them by hand. Excel is the place you assemble the result, not the place the result comes from.

So before we talk about tools, let’s be fair to Excel and look at what it genuinely does well.

What Can Excel Actually Do on Its Own?

Excel joins, cleans and reshapes data you already own. It cannot add facts you do not have.

That sounds harsh. It isn’t. Three of its features are genuinely strong, and you should use all three.

Lookup functions. XLOOKUP and its older sibling VLOOKUP pull a value from one table into another. That is a join, and it is exactly right when you already hold the answer in a second sheet. My colleague in sales ops enriches half her reports this way, and she is not doing anything wrong. I’ve written the longer version of this argument in the guide to why formulas can’t enrich data in Excel, so I won’t repeat it here.

Power Query. Power Query connects to sources you can already reach and transforms them on the way in. For cleaning 1,150 messy rows, it beats a column of nested formulas every time. Still a transformer, though. It moves and reshapes; it does not discover.

Linked data types. Excel’s linked data types cover stocks and geography in most copies of the product. Richer organisation data depends on your licence and on data your own organisation has published. So check what your version gives you before you plan a workflow around it. Mine gave me currency conversions and a lot of nothing about German industrial suppliers.

The verdict, then: Excel is your workbench, not your data source. Which brings us to the part that ruins most company lists long before enrichment even starts.

Why Is Matching Company Names in Excel So Painful?

Because the same company is written five different ways, and Excel only matches text exactly. One stray character and the row misses.

January 2024. Someone in operations sent me an .xlsx of 1,150 exhibitor names from a Munich industrial fair. One column. That was the whole file.

My first move was to match those names against our internal customer export with XLOOKUP. Sixty-odd matches out of 1,150. I assumed the export was broken. It wasn’t. The exhibitor list said “Krämer Antriebstechnik GmbH & Co. KG” and our records said “Kraemer Antriebstechnik”. Umlaut, legal form, ampersand. Three separate ways to miss the same company in one line of text.

Here are the six failure modes I now check for before I join anything:

• Legal suffixes. GmbH, GmbH & Co. KG, AG, Inc., Ltd., S.A., Pty Ltd, B.V. The same business appears with and without them, depending on who typed it.
• Diacritics and transliteration. Krämer, Kraemer, Kramer. All three show up in real files, and Excel treats them as three companies.
• Punctuation and connectors. “Marlow & Sons” versus “Marlow and Sons”. Commas after the name. A trailing period. A hyphen someone typed as an en dash.
• Trading names versus registered names. A brand can share nothing with its legal entity. The invoice says one thing, the trade-fair badge says another.
• Parent, subsidiary, or brand. Is “Nordwind Logistics UK” the same row as “Nordwind Logistics Group”? Sometimes yes, sometimes very much no.
• Whitespace and casing. Copy-paste leaves trailing spaces, non-breaking spaces, and inconsistent capitals. Invisible, and fatal to an exact match.

So I spent a Tuesday building a normalisation column. If you want to copy the recipe, it goes like this. Run TRIM and CLEAN to strip stray spaces and control characters. Force one consistent case with LOWER. Use SUBSTITUTE to swap “&” for “and” and to replace ä, ö and ü with ae, oe and ue. Pull the legal suffix into its own column instead of deleting it, because you will want it later. Only then compare.

My match count went from 60 to just over 300. Better! And still not enrichment. That was just better joining, which is a different thing. What I was really doing there is called data matching, and the tidying half of it is data cleansing.

💡 Pro Tip: Never overwrite the original name column. Put every cleaned version in a NEW column beside it. When a match looks wrong three weeks later, you'll want to see exactly what your formulas changed, and you cannot audit what you deleted.

Now here’s the punchline, and it is the most useful thing in this article. Normalisation raises your join rate. It does not fix the underlying problem. The real answer is to stop matching on names at all.

Resolve each name to a domain, then enrich on the domain. A domain is unique, it is spelled exactly one way, and it does not care whether you wrote GmbH or AG. That step is its own little job, and I’ve covered both directions of it: turning names into websites, and the reverse trip when you need to get a company name from a domain. If you want the name-to-domain step as a service, that’s company name to domain, and it’s the first thing I run on any exhibitor list now.

Enrich on the domain, never on the name. Say it out loud once and you’ll save yourself a Tuesday.

How Do You Enrich a Company List From Excel, Step by Step?

Export to CSV, enrich in Google Sheets with an add-on, then bring the file back into Excel. It’s a round trip, and it works.

I’ll be honest about what this is: a workaround. Excel has no add-on that fills company fields the way the Sheets one does, so the file takes a short holiday and comes home fuller. Five minutes of clicking, most of it waiting.

1. Save your workbook as CSV UTF-8. Not plain CSV. Microsoft’s own guide lists both options, and the difference cost me an hour. My umlauts came back as mojibake, and 340 German company names had to be re-imported from scratch.
2. Open the file in Google Sheets. Google’s import help covers both dragging the file in and importing into an existing sheet.
3. Install the add-on. Grab the CUFinder add-on from the Google Workspace Marketplace. If add-ons are new to you, Google’s help page explains where they install and how they appear.
4. Copy your API key. Open your CUFinder dashboard, copy the API key from there, and paste it into the add-on once.
5. Pick the service, map the columns, run. Open the add-on from the Google Sheets menu. It opens as a right panel listing all the available enrichment services. Choose company enrichment for firmographic fields, set your input column (domains, ideally), set the output column, set the row range like rows 2 to 1150, and run it.
6. Download as .xlsx and reopen in Excel. Your columns come home.

If your list is already in Sheets and you never needed Excel in the first place, skip the trip entirely and read the company enrichment in Google Sheets walkthrough instead. Same job, one fewer file format. And when you’re moving whole files around rather than working inside one workbook, the CSV enrichment guide covers that file-level view.

One expectation to set before you run anything: some rows will come back empty. No provider matches every company on earth, and a blank cell is the honest answer rather than a guess dressed up as data.

A Worked Example: 5 Rows, Before and After

Say your file looks like this. Company names in column A, exactly as the trade fair typed them, and three empty columns waiting:

	A	B	C	D
1	Company	Domain	Industry	Employees
2	Krämer Antriebstechnik GmbH & Co. KG
3	Nordwind Logistics B.V.
4	Valbruna Systems S.p.A.
5	Hollis & Frame Ltd
6	Steinbach Werkzeugbau

Then I ran it: name to domain first into column B, then company enrichment on column B, output into C and D, rows 2 to 6. A minute or so later the same sheet looked like this.

	A	B	C	D
1	Company	Domain	Industry	Employees
2	Krämer Antriebstechnik GmbH & Co. KG	kraemer-antriebstechnik.de	Industrial Machinery	240
3	Nordwind Logistics B.V.	nordwindlogistics.nl	Freight & Logistics	1,100
4	Valbruna Systems S.p.A.	valbrunasystems.it	Metal Fabrication	85
5	Hollis & Frame Ltd	hollisframe.co.uk	Engineering Services	62
6	Steinbach Werkzeugbau

Row 6 stayed blank, and that’s the row worth talking about. Steinbach Werkzeugbau is a four-person family workshop with no website and no registered web presence. There was nothing to find, so nothing was invented. I’d rather have an empty cell than a plausible-looking guess I then send to a colleague.

Notice something else. The domain column did the heavy lifting. Once B was filled, C and D matched cleanly, because the enrichment never had to interpret “GmbH & Co. KG” at all.

→ 1,150 exhibitor names → 1,090 domains → industry, size and location for 94% of the list → an account list your CRM will actually accept on import.

📌 Example: Same file, two attempts. Saved as plain CSV, the German names came back as mojibake and I re-imported 340 rows. Saved as CSV UTF-8, everything survived the round trip. One dropdown, one hour.

Which Company Fields Are Worth Adding, and Which Age Fastest?

Add the fields you will actually filter or sort by. Then remember that some of them go stale within a quarter.

Here’s my honest read on each, after five years of watching these columns rot:

• Domain. The most stable field you will ever hold, and the key everything else hangs on. Add it first.
• Industry. Stable for years, though the labels differ between providers. Pick one classification and stick to it.
• Employee count. Drifts every quarter, and faster in a company that is hiring or cutting. Treat it as a band, not a number.
• Revenue. For private companies this is an estimate. Sort into bands, never quote a euro figure to your CEO as fact.
• Head office location. Fairly stable, but offices do move and regional entities multiply.
• Phone numbers and addresses. Ageing fast. Verify before any campaign that depends on them.
• Technologies. The fastest-moving field of all. Anything older than a few months is a hypothesis.

All of this is data decay, and it is not a flaw in anyone’s database. Companies move, hire, rebrand and disappear. Your file just doesn’t know yet.

So do two small things. Keep a “last enriched” date column next to the enriched block. And put a reminder in your calendar to refresh the fast-moving fields every six months, before the campaign, not after the bounce report.

One more field worth a mention: if your list starts as a pile of company names and you also want their websites for research, the find company websites workflow is the same round trip with a narrower output.

What Mistakes Should You Avoid With Company Data in Excel?

Most of the damage happens at the file boundary, not in the enrichment. Excel is quietly opinionated about what your data means.

• Saving as plain CSV. Accented characters turn to mojibake on the way back. Choose CSV UTF-8, every single time.
• Losing leading zeros. Postcodes and phone numbers arrive as numbers and lose their first digit. Format those columns as text before import, not after.
• Letting Excel autoformat. Anything that looks remotely like a date becomes one. A founding year of “2015-2016” will not survive.
• Enriching the header row. Start every range at row 2, or you’ll spend a lookup on a company called “Company”.
• Overwriting the original name column. Keep the raw values. Your cleaned versions live in new columns.
• Deduplicating on the raw name. “Krämer Antriebstechnik GmbH” and “Kraemer Antriebstechnik” survive deduplication as two rows. Dedupe on the domain instead.

🔍 Did You Know? Same file, three afternoons: 60 matches with raw names, just over 300 after normalisation, and 1,090 once I matched on domains. The data never changed. Only the key did.

FAQ: Company Enrichment in Excel

What is the purpose of data enrichment in Excel?

To turn a thin list into a useful one. A column of company names tells you nothing you can act on. Add industry, size, location and domain, and the same file becomes something you can segment, prioritise and hand to sales without apologising for it.

What is an example of data enrichment?

A list of 1,150 exhibitor names becomes a list with domains, industries and employee counts beside each one. Nothing in the original file contained those facts. They were matched in from a company database and written into new columns.

How do you enrich data in Excel without a tool?

By hand, or by joining against data you already own. Research each company and type in the fields, or use XLOOKUP against an internal export. Both work. The ceiling is time: roughly two to three minutes per company for manual research, which is fine at 30 rows and brutal at 500.

Is there a data enrichment Excel template?

Templates give you the column layout, not the data. The layout worth copying is simple: raw company name, cleaned name, domain, then your enriched fields, then a “last enriched” date. That structure survives re-runs, which is more than most downloadable templates manage.

How do I get website URLs for a list of companies in Excel?

Resolve the names to domains outside Excel, then bring the column back. There is no formula that maps a company name to its website, because your workbook does not contain the internet. Export, run a name-to-domain lookup, and import the result.

Can I enrich a company list for free in Excel?

Manual research is free and slow, and joining against data you already own costs nothing. Database lookups cost money on every platform once you pass the free tier. Most services, ours included, hand out free credits so you can test coverage on your own list before paying.

Why do my company names not match between two files?

Because one file writes the name differently from the other. One has the legal suffix and the other does not. One spells “ä” where the other spells “ae”. One uses the trading name. Excel matches text exactly, so any of those breaks the row. Normalise both sides first, then match on a domain if you can.

It’s Time to Give That Company Column Some Company

You’ve got the whole workflow now. Normalise the names so you can see what you actually have. Resolve them to domains, because domains do not argue. Enrich on the domain, bring the columns home, and date-stamp what you added.

Picture the file I got in January: one column, 1,150 rows, and no idea which of them mattered. Now picture it sorted by employee band, filtered to two industries, with 94% of the rows carrying a domain. Same file. Two afternoons apart.

Excel isn’t the enemy here. It’s just a workbench, and it works better when you stop asking it to be a database. If you want the wider picture of what else you can add to a company list, the data enrichment in Google Sheets hub covers the rest of the columns. So tell me: how many rows are sitting in your file right now, and how many of them have a domain?