The Scenario
Three days before a Mailchimp import, your email marketing manager opened the sign-up form export: 2,000 email addresses collected over four months, sitting in column A of an Excel workbook with no validation applied at intake.
Some came from paid ad traffic. Some came from a partner co-registration where the incentive was a discount, not genuine interest. A portion are almost certainly disposable or syntax-broken.
Mailchimp's import will flag bad addresses on the way in — but that happens after the campaign is already configured, after the audience segment is already built, and after 400 of your contacts turn out to have never been real.
The bad version:
- Export column A as a CSV, run it through a free email validation tool with a 500-row cap, get back results that don't match the original row order.
- Manually cross-reference which flagged emails belong to which rows in the workbook, mark them, repeat for the next 500-row batch.
- Realize the tool flagged valid business addresses as "catch-all" and now you're second-guessing which rows to remove.
There are 2,000 rows and the import window is Thursday.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Excel workbook that reads your data and calls BigDataCloud's email verification API across the full column — in one pass, without the CSV export cycle.
Open SheetXAI from the Excel add-in panel and type:
Go through all emails in my Excel sheet, mark any invalid or disposable address in column B, and give me a count of how many should be removed
What You Get
- Column B: blank for valid addresses; for invalid or disposable ones, a short reason (e.g., "invalid syntax", "disposable provider", "domain does not exist")
- A summary note in the add-in panel after the run: total valid, total invalid, total disposable, and total catch-all
- Rows marked in column B are ready to filter out before the Mailchimp import
What If the Data Is Not Quite Ready
The email column has whitespace or mixed-case formatting
Verify every email in column A using BigDataCloud — trim whitespace and normalize to lowercase before the verification call, then mark invalid and disposable addresses in column B with the failure reason
You want to filter out bad rows immediately, not just flag them
Verify all emails in column A using BigDataCloud, mark invalid and disposable addresses in column B, then create a new worksheet called "Clean List" with only the rows where column B is blank (verified valid)
The workbook has a mix of personal and business emails and you want to segment them
Verify every email in column A using BigDataCloud — mark invalid and disposable addresses in column B, then add a column C that classifies each valid email as "business" (non-free-provider domain) or "consumer" (gmail, yahoo, hotmail, etc.)
Full list hygiene pass before the import
Verify all 2,000 emails in column A using BigDataCloud — trim and normalize formatting, mark invalid and disposable addresses in column B with failure reason, create a "Mailchimp Ready" worksheet with only valid business emails, and add a summary row at the top of that worksheet showing how many were removed and why
One prompt covers the normalization, the verification, the segmentation, and the export worksheet.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook with a sign-up form email export — run the verification prompt above before your next campaign import and know exactly what's going in. See the phone number validation spoke if you also need to clean the phone column in the same workbook.