Clean an Email List Before Import

A sign-up list collected over time has typos, repeats and blank lines. This example shows a format-level cleanup on a fictional list, and is clear about the one thing it does not do: test whether a mailbox exists.

ℹ️ A worked example you can check. The before and after tables below were produced by the free tool on this site from a fictional sample file. Open the same file yourself and you will get the same result.

What Usually Goes Wrong

• The same subscriber twice, once with capital letters
• Addresses with no domain ending, or with [at] typed instead of @
• A domain that looks like a misspelling of a common provider
• Blank lines and a header row pasted in again
• Sign-up dates in two formats

The Sample File, Before

10 rows of fictional data with the problems above. Every person and company is invented.

Open This File in the Workspace Download the CSV

Clean an Email List Before Import sample before cleaning
Email Address	First Name	Last Name	Signup Date	Tags
olivia.marsh@example.com	Olivia	Marsh	2026-02-03	newsletter
Theo.Banerjee@Example.org	Theo	Banerjee	2026-02-07	newsletter

olivia.marsh@example.com	Olivia	Marsh	2026-02-03	newsletter
camille.duret@example	Camille	Duret	2/11/2026	webinar
theo.banerjee@example.org	Theo	Banerjee	2026-02-07	newsletter
Email Address	First Name	Last Name	Signup Date	Tags
ivo.petrov[at]example.net	Ivo	Petrov	2/19/2026	newsletter
nia.okafor@hotmial.com	Nia	Okafor	2/21/2026	webinar
wren.calloway@example.com			2/25/2026	newsletter

How to Clean It

1. Export the list as CSV and keep an untouched copy.
2. Run it with duplicates, lowercase emails, Merge duplicate emails, email flags and dates.
3. Sort or filter by the email_check column. Fix the addresses you are sure about by hand; remove or hold back the rest.
4. Delete the email_check column, or keep it as a tag, and import.
5. If deliverability matters, run the cleaned list through a mailbox verification service. This tool checks format only.

Prefer the command line or an AI agent? The same result comes from smartdata-clean clean email-list.csv --dedupe-rows --lower-emails --merge-duplicate-emails --flag-emails --dates. See tools for agents.

The Result

10 rows in, 4 removed, 6 out. Blank lines, the pasted header and repeated subscribers are gone, and three addresses are flagged: two with an invalid format and one with a likely misspelled domain. None of the flagged addresses is changed or removed — that is your decision.

Clean an Email List Before Import sample after cleaning
Email Address	First Name	Last Name	Signup Date	Tags	email_check
olivia.marsh@example.com	Olivia	Marsh	2026-02-03	newsletter	ok
theo.banerjee@example.org	Theo	Banerjee	2026-02-07	newsletter	ok
camille.duret@example	Camille	Duret	2026-02-11	webinar	invalid format
ivo.petrov[at]example.net	Ivo	Petrov	2026-02-19	newsletter	invalid format
nia.okafor@hotmial.com	Nia	Okafor	2026-02-21	webinar	check domain (did you mean hotmail.com?)
wren.calloway@example.com			2026-02-25	newsletter	ok

What the tool left for you

The tool listed these 3 items for a person to check:

• Dates in "Signup Date" were read as month first (MM/DD) because other dates in that column can only be read that way. 1 date(s) that could be read either way on their own were converted on that basis (rows 6). Check them, or set the date order yourself.
• Invalid email format: 2 (rows 6, 9)
• Possible email domain typos: 1 (rows 10)

Checked, not just claimed

The original file, the cleaned file and the change log were run through the separate verifier. Result: PASS.

✓ Removed rows with a valid, verified reason: 4 of 4
✓ Rows: 10 in, 4 removed with a logged reason, 6 out
✓ Cells compared: 30; changed: 5; every change matched to the log: yes
✓ Log entries: 9; not matching the files: 0

The verifier confirms that the change log explains every difference between the two files. It does not judge whether each cleaning choice suits your situation; that part is yours.

The cleaning report

Report text for the sample file

SMARTDATAAI — CLEANING REPORT
Source: email-list.csv (fictional practice data)

SUMMARY
Rows in (excluding header): 10
Rows out: 6
Rows removed: 4
  Empty rows: 1
  Exact duplicates: 2
  Repeated header rows: 1
Cells changed: 5
  Emails normalized: 1
  Dates standardized to YYYY-MM-DD: 4
Columns added: email_check

COLUMNS DETECTED
Email: Email Address
Phone: none
Date: Signup Date
Name: First Name, Last Name

NEEDS REVIEW
- Dates in "Signup Date" were read as month first (MM/DD) because other dates in that column can only be read that way. 1 date(s) that could be read either way on their own were converted on that basis (rows 6). Check them, or set the date order yourself.
- Invalid email format: 2 (rows 6, 9)
- Possible email domain typos: 1 (rows 10)

SAMPLE OF CHANGES (first 5 of 5)
Row 3 · Email Address: "Theo.Banerjee@Example.org" → "theo.banerjee@example.org"  [email]
Row 6 · Signup Date: "2/11/2026" → "2026-02-11"  [date]
Row 9 · Signup Date: "2/19/2026" → "2026-02-19"  [date]
Row 10 · Signup Date: "2/21/2026" → "2026-02-21"  [date]
Row 11 · Signup Date: "2/25/2026" → "2026-02-25"  [date]

Values the tool cannot settle are left as they were and listed above. Where the day/month order of a date was worked out from other dates in the same column, that is listed above too.
Row numbers refer to the original file (header = row 1). Email checks cover format only; they do not confirm that a mailbox exists.

Where a Tool Stops and a Person Starts

A person can correct obvious typos with your approval, reconcile tags across lists, and prepare the file for a specific tool's import template. That is the Quick Clean or Complex Cleanup service, depending on size.

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Format checks cannot tell you whether an address will bounce. No tool on this site verifies mailboxes.