CSV list cleaner
Drop a lead list and Ken cleans names, companies, titles and domains, removes duplicates and flags freemail, role and disposable emails, in your browser.
What the list cleaner does
Drop a CSV or Excel export from Apollo, Sales Navigator, Clay, HubSpot or any CRM and every cleaner runs in one pass. The file is parsed inside your browser tab by a background worker, so nothing is uploaded and closing the tab deletes it.
It maps your columns automatically, then cleans names, company names, job titles and domains, removes duplicates and flags the rows that would hurt a campaign.
How to read the result
The stat chips at the top count what happened: duplicates removed, freemail addresses flagged, role and disposable addresses removed, empty or malformed emails dropped. Below them is a before and after table of the rows that changed.
- A removed row goes to a second file, with the reason, so nothing is lost.
- A flagged row stays in the list. You decide whether it goes.
- A fixed cell keeps its column. Only the value changes.
How to use it before a campaign
Run it on every export before it reaches a sending tool. Check the column mapping first, because a wrong guess there spreads through every cleaner. Then look at the removed file for anything you want back, choose whether freemail addresses stay, and download CSV or Excel.
Clean is not the same as deliverable. Verify the addresses next, and run the merge tag checker if your template uses fields the list might leave empty.
Why it matters for cold email
Bad rows cost more than the send. Role addresses like info@ and disposable domains bounce or never reach a person, and bounces count against the sending domain. "Hi JOHN," and "ACME LOGISTICS LLC" in the first line tell the reader the email came from a mail merge. Removing them before the first send is cheaper than repairing a domain's reputation afterwards.