Name cleaner
Fixes casing, strips honorifics, credentials and emojis, splits first and last names and flags entries that are not names.
What the name cleaner does
It turns the name in your export into the name you would write after "Hi". "JOHN SMITH, MBA 🚀" becomes John and Smith.
- Strips prefixes such as Dr., Mr. and Ms.
- Strips suffixes and credentials: Jr., III, PhD, CPA, MBA, PMP.
- Removes emojis, pronouns in parentheses and nicknames in quotes.
- Fixes casing with the usual exceptions: McDonald, O'Brien, van der Berg.
- Splits a full name into first and last when the export merged them.
How to read the result
Each row shows the original, the cleaned first name and the cleaned last name. Rows that do not look like a person, such as "Sales Team" or "Accounts Payable", are flagged as non-names. Those are almost always role inboxes and should not get a first-name greeting.
How to use it
Paste a list with one name per line, or drop a CSV and pick the name columns. If the export has separate first and last columns, map both. If it has one full-name column, map that and the tool splits it. Download the cleaned file with the new columns next to the originals, then send flagged non-names to a different template or drop them.
Why it matters for cold email
The greeting is the first thing the reader sees in the preview. "Hi JOHN,", "Hi Dr. Jane Doe PhD," and "Hi Sales Team," each say the email came from a list, and that ends the read before your first sentence. LinkedIn and data vendor exports carry these problems in a share of rows on almost every list, so the pass is worth running every time.