Clean Your Excel or CSV File Before Importing Contacts into Salesforce

Before you bring a contact, lead, or account list into Salesforce, clean the messy names, emails, phone numbers, addresses, dates, and duplicate rows that can cause import errors or bad CRM records.

Free version available. Clean and export up to 100 records before you import into Salesforce.
Download free View paid options

Clean before the upload

Standardize emails, phone numbers, names, dates, and addresses before they become Leads, Contacts, or Accounts in Salesforce.

Review suggested fixes

The tool suggests corrections where possible, then lets you review the results instead of blindly changing your data.

Export a Salesforce-ready file

After cleaning, export your data in a format that is easier to map during a Salesforce import.

CRM Data Cleaner runs in Excel. It is designed for businesses that already have CRM data in spreadsheets and want a cleaner file before importing into Salesforce.
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
Name cleaning options in Excel Data Cleaner, including proper casing and splitting titles and suffixes into their own columns
Review screen in Excel Data Cleaner comparing original emails and phone numbers with suggested corrections, flagged as typo, invalid, fake or valid
Exporting from Excel Data Cleaner, choosing CSV or XLSX format, an output folder and which columns to include
Before and after in Excel Data Cleaner, with the messy original contact sheet beside the standardised finalised data

Why clean the spreadsheet before importing into Salesforce?

Salesforce imports depend on clean columns, consistent values, and careful field mapping. If the spreadsheet already contains duplicate contacts, mixed date formats, inconsistent phone numbers, or misspelled emails, those issues can create rejected rows, duplicate records, or extra cleanup work after import.

CRM Data Cleaner helps you clean the file first, then export a cleaner file that is easier to review and map during the Salesforce import process.

Lead, contact, and account fields

Prepare common fields like first name, last name, email, phone, company/account name, address, and dates before upload.

Duplicate review

Find duplicate contacts or leads before import by matching on email, phone, name, or any other column in your file.

Cleaner field mapping

Standardize values and split combined fields so the import file is easier to map to Salesforce fields.

1

Open your current file

Start with your existing Excel or CSV list from an old CRM, website form, lead source, or spreadsheet.

2

Clean and review

Run cleanup tools for emails, names, phones, addresses, dates, and duplicates. Review suggestions before exporting.

3

Export for Salesforce

Create a cleaner output file that is easier to upload and map during your Salesforce import.

Salesforce import prep checklist

  • Confirm the target object: Leads, Contacts, Accounts, or another object.
  • Make sure required fields for that object are present.
  • Clean primary email addresses and remove obvious fake or placeholder records.
  • Split full names into first and last name columns when needed.
  • Standardize company/account names for easier matching.
  • Format phone numbers consistently.
  • Split addresses into street, city, state, and ZIP/postal code columns when possible.
  • Convert mixed date formats into one consistent format.
  • Review duplicate contacts or leads before uploading.
  • Check picklist-style values for spelling and consistency before import.

Every version cleans

Click any type for the full detail. The tool auto-suggests fixes wherever it can — you review and approve each one.

Emailscase, typos, validation, fakes▼
Far more than lowercasing. Each address gets a status (Valid, Typo, Invalid, Fake, Disposable, Multiple, Empty) with a suggested fix when possible:
  • Case & whitespace — lowercases and trims stray spaces.
  • Typo correction — 59 common domain misspellings across Gmail, Yahoo, Outlook, Hotmail, AOL, and iCloud.
  • Validation — flags malformed addresses (missing @, no domain, bad structure).
  • Fake detection — catches placeholder junk like test@test.com, asdf@asdf.com.
  • Disposable detection — flags throwaway inbox domains.
  • Multiple emails — keeps the first, moves extras to their own column for review.
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 numbers10-country formatting▼
Standardizes numbers to the format you choose, with country-aware detection for US, UK, Germany, France, Mexico, Brazil, India, Japan, China, and Australia.
  • Format styles — (555) 123-4567, 555-123-4567, 555.123.4567, or +15551234567.
  • Default country — fallback for numbers with no prefix; a Country column takes priority if present.
  • Country code column — optionally split the detected code into its own column.
5551234567 → (555) 123-4567
5551234567 → 555.123.4567
5551234567 → +15551234567
+44 20 7946 0958 → CC: +44 | 020 7946 0958
Namescase, split, merge, titles, suffixes▼
Handles names in whatever shape your data arrives — one full-name column or separate first/last.
  • Proper casing — fixes ALL CAPS or lowercase.
  • Split full names — break “Dr. Jane Smith III” into Title / First / Last / Suffix columns.
  • Merge — combine First + Last into one Full Name column when your destination needs it.
  • Titles — Dr., Mr., Mrs., Ms., Miss, Prof., Rev., Hon., Sgt., Capt., and more.
  • Suffixes — 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
Datesauto-detect, reformat, time handling▼
Turns a column of inconsistent dates into one uniform format.
  • Auto-detection — reads mixed input formats automatically.
  • Ambiguity resolution — set MDY / DMY / YMD so 03/04/2024 is never guessed wrong.
  • Target formats — YYYY-MM-DD, MM/DD/YYYY, DD/MM/YYYY, MM-DD-YYYY, or “March 15, 2024”.
  • Time handling — strip the time, move it to its own column, or keep it inline.
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
Addressesparse, split, multiple formats▼
Parses free-form addresses and outputs them the way your destination needs.
  • Output formats — separate Street/City/State/ZIP columns; Street + combined City-State-ZIP; a single full-address column; or normalize-only.
  • Apt/unit splitting — pulls Apt, Unit, Suite, Ste, #, Bldg, Floor, Rm, Dept, and more into a separate column.
  • ZIP handling — keep as 5-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
Duplicatesfind & remove by any field▼
Find and remove duplicate records based on whichever field matters to you.
  • Match on any column — email, phone, name, or any field in your data.
  • Review groups — see matched records grouped together before anything is removed.
  • Keep one, remove the rest — clear out the extras based on your chosen match column.
3 rows with john@gmail.com → review group, keep 1, remove 2
match on Phone → same number across rows grouped & deduped
Free
Try the whole thing on small data.
$0
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Includes

  • ✓ All cleaning tools
  • ✓ All 5 CRM exporters
  • ✓ Find & remove dupes

Limited to 100 records.

Download free
CRM Data Cleaner
Clean and dedupe unlimited data. Export your cleaned file and import it wherever you need.
$99
pay once · 2 computers

Unlimited

  • ✓ Email, phone, name, date, address cleaning
  • ✓ Find & remove duplicates
  • ✓ Export your cleaned data as Excel or CSV
Buy $99
Most popular
CRM Data Cleaner + 1 Exporter
Everything in Cleaner, plus import-ready files for your CRM.
$128
pay once · 2 computers

Adds

  • ✓ Validated, CRM-spec-matched export file
  • ✓ Skip the manual reformatting and failed imports
Buy $128
CRM Data Cleaner Complete
Everything in Cleaner, plus import-ready files for any of the 5 CRMs.
$179
pay once · save $65

Adds

  • ✓ Validated import-ready files for all 5 CRMs
  • ✓ HubSpot, Salesforce, Zoho, Pipedrive, Monday

Best value if you work with more than one CRM.

Buy $179

Already own the CRM Data Cleaner? Add more exporters from inside the app.

Salesforce import cleaner FAQ

Can this prepare a CSV for Salesforce?▼
Yes. The tool cleans your spreadsheet first, then lets you export a cleaner Excel or CSV file that is easier to map during a Salesforce import.
Does it replace Salesforce duplicate rules?▼
No. Salesforce duplicate rules and matching rules still matter. This tool is a pre-import cleanup step so you can reduce obvious duplicate rows before the data reaches Salesforce.
Can it clean leads, contacts, and accounts?▼
Yes, as long as the data is in a spreadsheet. It can clean names, emails, phones, dates, addresses, and duplicate rows in the columns you choose.
Does the free version include Salesforce export?▼
The free version lets you try the cleaner and exporters on up to 100 records. Paid licenses remove the record limit.

Salesforce is a trademark of Salesforce, Inc. ExcelGurus is not affiliated with or endorsed by Salesforce.

Using a different CRM?

The same tool exports for all five. These guides cover what each importer expects.

HubSpotZohoPipedrivemonday
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