How to Remove Duplicate Company Names From a CSV File (Even When They Don't Match Exactly)

Last week I saw someone on Reddit ask: "I have a CSV with 3,000 company names and I know there are duplicates, but they're spelled differently. How do I find them?"

The top reply? "Just use Remove Duplicates in Excel."

That advice is wrong. And if you follow it, you'll miss most of your actual duplicates.

Here's why, and what to do instead.

Why "Remove Duplicates" Doesn't Work for Company Names

Excel's built-in Remove Duplicates feature does one thing: it finds rows where the text is exactly identical, character for character, and removes the extras.

That's fine if your duplicates look like this:

• Acme Corp → Acme Corp → Acme Corp

But real-world duplicates almost never look like that. They look like this:

• Acme Corp
• ACME Corporation
• Acme Corp.
• Acme, Corp

These are all the same company. But Excel's Remove Duplicates sees four unique entries and keeps all of them.

If your CSV came from merging CRM exports, combining vendor lists, or consolidating data from multiple teams, you're dealing with this kind of messy duplication. Every person who entered the data spelled things slightly differently.

The Manual Approach (And Why It Breaks at Scale)

Some people try to find duplicates manually. The process usually looks like this:

1. Sort the column alphabetically
2. Scan through the list looking for similar names next to each other
3. Manually flag or merge the ones that look like duplicates

This sort of works for small lists. But it has three big problems.

First, alphabetical sorting doesn't always group duplicates together. "The Boeing Company" and "Boeing Co" end up far apart because one starts with "The" and the other starts with "B."

Second, it's slow. If you have 1,000 rows, you're spending 2-3 hours on this. At 5,000 rows, it's a full day. At 10,000 rows, you should probably take the day off instead because it's going to be more productive.

Third, you'll miss things. Your eyes get tired. "Johnsen & Johnsen" and "Johnson & Johnson" — is that a duplicate with a typo, or two different companies? After scanning 500 names, your brain starts skipping things.

The Conditional Formatting Trick

A slightly better approach: use conditional formatting to highlight duplicates.

Select your column, go to Home → Conditional Formatting → Highlight Cell Rules → Duplicate Values.

This highlights exact duplicates. It's fast and visual. But it has the same core limitation as Remove Duplicates — it only catches exact matches. "Acme Corp" and "ACME Corporation" won't highlight.

You can improve this slightly by adding a helper column with a cleaned version of the name:

=UPPER(TRIM(SUBSTITUTE(A2,".","")))

This normalizes capitalization, whitespace, and periods. Then run conditional formatting on the helper column. You'll catch a few more duplicates, but still miss abbreviation differences, typos, and word order variations.

What Actually Catches Real-World Duplicates

The fundamental problem is that all the Excel-native approaches need exact text matches. Company names are inherently fuzzy — people abbreviate, misspell, and format them differently every time.

You need fuzzy matching to find real duplicates.

Fuzzy matching compares two strings and calculates how similar they are, expressed as a percentage. "Acme Corp" and "ACME Corporation" might be 87% similar — clearly a duplicate. "Acme Corp" and "Amazon" would be 12% similar — clearly not.

Here's how different approaches handle the same CSV file with 1,000 company names:

The difference is dramatic. In this example, there were 112 actual duplicate companies in the list. Excel's built-in tools found less than a quarter of them.

How to Fuzzy Match Your CSV (Step by Step)

If You're Comfortable With Code

Python's rapidfuzz library is excellent for this:

from rapidfuzz import process, fuzz
import pandas as pd

df = pd.read_csv("companies.csv")
names = df["company_name"].tolist()

for i, name in enumerate(names):
    matches = process.extract(name, names[i+1:],
        scorer=fuzz.token_sort_ratio, limit=5)
    for match, score, idx in matches:
        if score > 80:
            print(f"Duplicate: '{name}' ≈ '{match}' ({score}%)")

This works well, but you need Python installed, you need to be comfortable reading code, and you'll need to handle the output formatting yourself.

If You Just Want It Done

Upload your CSV to an online fuzzy matching tool. The process is:

1. Go to a tool like DedupFuzzy
2. Upload your CSV or Excel file
3. Select the column with company names
4. Review the matches the tool finds
5. Download the cleaned results

No code, no formulas, no installation. The AI handles the abbreviations, typos, capitalization, and formatting differences automatically.

For files up to 500 rows, most tools (including DedupFuzzy) let you do this for free without even creating an account.

Tips for Cleaner Data Going Forward

Standardize at entry. If you control the input form, use dropdown menus or auto-complete for company names instead of free text fields. This prevents variations from being created in the first place.

Pick one canonical format. Decide whether you use "Corp" or "Corporation," "Inc." or "Incorporated." Document it. Share it with your team.

Run deduplication regularly. Don't wait until your list has 10,000 entries. Run a fuzzy match check monthly or quarterly. It's much easier to review 20 potential duplicates than 200.

Keep your raw data. Before merging or deleting duplicates, save a copy of the original file. You might find that two entries you thought were duplicates are actually different companies.

The Real Cost of Duplicate Data

Duplicate company names aren't just an aesthetic problem. They cause real business issues:

• You send the same email twice to the same client (once to "Acme Corp" and once to "ACME Corporation")
• Your reports show inflated customer counts
• Your sales team doesn't realize a "new lead" is actually an existing customer
• You pay for duplicate records in your CRM

Most people don't think about deduplication until it causes an embarrassing mistake. Don't wait for that email. Clean your data now.