Clean and deduplicate your data directly in Excel

Excel Data Cleaner turns messy contact and customer lists into clean, consistent data—fixing names, email addresses, phone numbers, mailing addresses, dates, and duplicate records without formulas or manual cleanup.

Pay once, use it forever. No subscriptions, no renewals.

Excel Data Cleaner home page explaining how it standardises contact data, removes duplicates and exports CRM-ready files

Excel Data Cleaner workflow showing the eleven numbered steps with mapped, staged and approved progress for each one

What the Excel Data Cleaner can clean

Use it with contact lists, customer records, mailing lists, CRM exports, and other Excel or CSV data. The tool suggests fixes wherever possible—you review and approve each one.

Email addresses case, formatting, typos, fakes ▼

Go beyond simple lowercasing. Each address receives a status such as Valid, Typo, Invalid, Fake, Disposable, Multiple, or Empty, with a suggested fix when possible:

Case and whitespace — lowercases addresses and removes stray spaces.
Typo correction — recognizes 59 common domain misspellings across Gmail, Yahoo, Outlook, Hotmail, AOL, and iCloud.
Format checks — flags malformed addresses with a missing @, missing domain, or invalid structure.
Fake detection — catches placeholder entries such as test@test.com and asdf@asdf.com.
Disposable detection — flags known throwaway inbox domains.
Multiple addresses — keeps the first address and moves extras into their own column.

JOHN@GMAIL.COM → john@gmail.com
dg@gmial.com → dg@gmail.com (Typo)
sue@hotmial.com → sue@hotmail.com (Typo)
test@test.com → flagged Fake
bob@x.com; sue@y.com → bob@x.com + 1 more

Phone numbers 10-country formatting ▼

Standardizes phone numbers using your chosen format, with country-aware detection for the US, UK, Germany, France, Mexico, Brazil, India, Japan, China, and Australia.

Format styles — choose (555) 123-4567, 555-123-4567, 555.123.4567, or +15551234567.
Default country — provides a fallback for numbers without a prefix; an existing Country column takes priority.
Country-code column — optionally moves the detected country code into its own column.

5551234567 → (555) 123-4567
5551234567 → 555.123.4567
5551234567 → +15551234567
+44 20 7946 0958 → CC: +44 | 020 7946 0958

Names case, split, merge, titles, suffixes ▼

Handles names whether your data contains one full-name column or separate first- and last-name fields.

Proper casing — fixes ALL CAPS or lowercase text, with the option to preserve intentional casing.
Split full names — separates “Dr. Jane Smith III” into Title, First Name, Last Name, and Suffix columns.
Merge name fields — combines First Name and Last Name into one Full Name column.
Titles — recognizes Dr., Mr., Mrs., Ms., Miss, Prof., Rev., Hon., Sgt., Capt., and more.
Suffixes — recognizes Jr., Sr., II, III, IV, Esq., PhD, MD, DDS, CPA, and more.

john SMITH → John Smith
Dr. Jane Smith → Dr. | Jane | Smith
James Wilson III → James | Wilson | III
First: john + Last: SMITH → Full: John Smith

Dates auto-detect, reformat, time handling ▼

Turns columns containing inconsistent dates into one uniform format.

Automatic detection — recognizes mixed input formats.
Ambiguity control — select MDY, DMY, or YMD so a date such as 03/04/2024 is interpreted correctly.
Output formats — choose YYYY-MM-DD, MM/DD/YYYY, DD/MM/YYYY, MM-DD-YYYY, or “March 15, 2024.”
Time handling — remove the time, move it into its own column, or keep it with the date.

03/15/2024 → 2024-03-15
March 15, 2024 → 2024-03-15
15/03/2024 (DMY) → 2024-03-15
2024-03-15 14:30:00 → Date: 2024-03-15 | Time: 14:30:00

Mailing addresses parse, split, multiple formats ▼

Parses free-form mailing addresses and arranges them in the format your spreadsheet or destination system requires.

Output formats — use separate Street, City, State, and ZIP columns; a combined City-State-ZIP field; one full-address column; or normalization only.
Apartment and unit splitting — moves Apt, Unit, Suite, Ste, #, Bldg, Floor, Rm, Dept, and similar values into a separate column.
ZIP handling — retain a five-digit ZIP or preserve ZIP+4.

123 Main St Apt 4B, Springfield IL 62704-1234
→ Street: 123 Main St  Apt: 4B
→ City: Springfield  State: IL
→ ZIP: 62704  (or 62704-1234)
or → single: 123 Main St Apt 4B, Springfield, IL 62704

Duplicate records find and remove by any field ▼

Find and remove duplicate rows based on whichever field matters for your data.

Match any column — compare email addresses, phone numbers, names, or any other field in your file.
Review matched groups — inspect related records together before removing anything.
Keep one, remove the rest — retain one record and remove the extras using your selected match column.

3 rows with john@gmail.com → review group, keep 1, remove 2
match on Phone → same number across rows grouped and deduplicated

Free

Try every cleaning feature with up to 100 records.

no card required

Includes

✓ All Excel data-cleaning tools
✓ Excel, CSV, and 5 CRM export options
✓ Find and remove duplicates

Limited to 100 records per file.

Download free

Windows PC required — why it does not run on Mac.

Excel Data Cleaner

Clean and deduplicate unlimited Excel or CSV data, then export it in the format you need.

$99

pay once · 2 computers

Unlimited

✓ Clean emails, phones, names, dates, and addresses
✓ Find and remove duplicate records
✓ Export cleaned data as Excel or CSV

Buy $99

Cleaning an export from a specific CRM?

Each guide covers the fields that CRM exports, the problems its files usually carry, and what its importer expects back.

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