Can I remove duplicates from a contact list in Excel?

Yes, but Excel's Remove Duplicates only deletes rows that match exactly. It keeps 'Jon Smith' and 'Jonathan Smith' with the same phone number, 'ACME' and 'Acme Ltd', and the same email with different casing unless you pre-normalize every column first. It also deletes the duplicate row outright rather than merging the best values from both rows into one record.

Why do my Excel fixes break again when I reopen the CSV?

A CSV stores no formatting, so every time Excel opens one it re-guesses each column's type. Leading zeros get stripped again, long phone numbers flip back to scientific notation, and dates get reinterpreted. The fix only sticks while the file stays open. Cleaning the file outside Excel, then never reopening it in Excel before import, avoids the loop.

Is Sift better than Excel for cleaning a contact list?

For a CRM import, usually yes: Sift does fuzzy dedupe with golden-record merging, name-safe casing, phone and country normalization, and a pre-flight check against your CRM's import template, none of which Excel does natively. For general spreadsheet work, calculations, and pivot analysis, Excel remains the right tool. Sift is free and runs in your browser with nothing uploaded.

Comparison

Cleaning a contact list in Excel vs Sift

Excel can clean a contact list, and for small, simple jobs it's fine. But three things make it the wrong tool for a CRM import: Remove Duplicates only catches exact matches, the formulas break on real names, and the fixes un-fix themselves the next time anyone opens the CSV. Here's an honest account of both sides.

Clean your list in Sift, free →

What Excel does well

Credit where due. If your list is a few hundred rows with one column of emails and no duplicates beyond literal copy-paste repeats, Excel handles it: Remove Duplicates for exact repeats, TRIM for stray spaces, Text to Columns for simple splits, and filters to eyeball the outliers. Excel is also the right place for everything Sift doesn't do: calculations, pivot tables, charts, and general spreadsheet work.

Where Excel breaks down

1. Remove Duplicates only sees exact matches

The duplicates that actually pollute a CRM are never exact. The same person appears as "Jon Smith" and "Jonathan Smith", the same company as "ACME" and "Acme Ltd", the same email in two casings. Excel keeps them all. As one HubSpot admin put it on Reddit: companies handle dedupe "with Excel and building formulas... but it's really hard to account for every potential variable, so you are going to end up missing quite a few."

And when Excel does find a duplicate, it deletes the row. It can't merge two half-complete rows, one with the phone number, one with the job title, into a single best record.

2. The formulas break on real data

PROPER() turns McDonald into Mcdonald, O'BRIEN into O'Brien only by luck, and van der Berg into Van Der Berg. Text to Columns splits "Mary Jane van der Berg" and "Smith, John Jr." wrong. The E.164 phone-number formula circulating on forums is a five-deep SUBSTITUTE chain that only works for US numbers. Each fix needs another fix.

3. The fixes un-fix themselves

A CSV stores no formatting, so Excel re-guesses every column's type each time the file is opened. Fix the leading zeros in your postcodes, save, reopen, and they're stripped again. Long phone numbers flip to scientific notation. UK dates get reread as US dates. This is the single most common complaint in Excel communities ("How do I stop Excel from ruining my data?"), and it's not a bug you can turn off; it's how Excel opens CSVs. We wrote a full breakdown of this failure mode.

4. Nothing checks the file will actually import

Excel has no idea Salesforce requires Last Name, that your HubSpot Lifecycle Stage must match a dropdown option exactly, or that a restricted picklist will reject "USA" when it expects "United States". You find out after the import fails, one cryptic error at a time.

What Excel leavesWhat a CRM needs

Jon Smith / Jonathan Smith (2 rows)→One merged record with both fields

MCDONALD → Mcdonald (PROPER)→McDonald

07911123456→+44 7911 123456

USA→United States (picklist-valid)

The same job in Sift

Drop the file in. Sift profiles every column in your browser; nothing is uploaded.
Approve the cleanups with a before/after diff: trim, name-safe casing, email repair, phone normalization to E.164, countries and postcodes standardized.
Dedupe fuzzily, matching on email, phone, or a name-and-company fingerprint, and merge duplicates into one golden record with survivorship rules instead of deleting rows.
Map to your CRM's template (HubSpot, Salesforce, Pipedrive, Dynamics 365, Zoho, Mailchimp) and run the import pre-flight: required fields, types, and allowed values, flagged before you import.
Export a clean file that Excel never touches again on its way to the CRM.

Privacy note: Sift is a static web app with no backend. Your file is processed entirely on your device, which you can verify by disconnecting your internet after the page loads; the cleaning still works. The opposite of pasting your customer list into a web tool or ChatGPT.

When Excel is still the right tool

You need calculations, pivots, or charts; Sift is a cleaner, not a spreadsheet.
You're editing a handful of cells by hand and know exactly which ones.
Your dedupe really is exact-match only and the list is small enough to eyeball.

The honest workflow most ops people land on: Excel for analysis, Sift for the clean-dedupe-map-check step right before an import.