The Scenario
You are a demand-generation manager. The Mailchimp import is scheduled for Thursday morning. You have an Excel workbook with 3,000 email addresses on the Registrations tab from a recent webinar form.
The form had no validation. Your last import had a 12% bounce rate that tanked domain reputation for two weeks.
The bad version of this week:
- Export the list and run it through an external verification tool
- Get back a result file in a different format
- Match it to the original workbook by email address
- Manually flag the disposable addresses
- Import, watch 240 rows get rejected on arrival
The fast version is one prompt and the clean list is ready in the workbook.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Excel workbook that reads the email column on the Registrations tab, calls DaData's email cleaner for every row, and writes the corrected email and classification back.
Open the SheetXAI sidebar and type:
For each email address in column A of the Registrations tab, use DaData to validate and correct it. Write the corrected email into column B. Write the DaData classification — Personal, Corporate, or Disposable — into column C. If DaData cannot fix the email, write "INVALID" in column B and leave column C blank.
SheetXAI reads column A, calls DaData's email validation endpoint for each row, and writes two columns back across all 3,000 rows.
What You Get
Two new columns filled across all 3,000 rows of the Registrations tab:
- Column B — DaData-corrected email address, or "INVALID" if it cannot be fixed
- Column C — classification: Personal, Corporate, or Disposable
Disposable addresses are labeled, not silently dropped, so you decide what to do with them.
What If the Data Is Not Quite Ready
Webinar registration data has patterns that require extra handling. SheetXAI handles them in the same prompt.
When some emails are in mixed case
The form preserved case — "John.Smith@GMAIL.COM" — and your CRM treats case-variant emails as duplicates.
For each email in column A of the Registrations tab, normalize to lowercase before passing to DaData. Write the corrected lowercase email into column B and the classification into column C.
When you want a single import-ready column
Your Mailchimp import template expects one column with only the clean, non-disposable emails.
For each email in column A of the Registrations tab, use DaData to validate and fix it. If the result is Personal or Corporate, write the corrected email into column B. If it is Disposable or INVALID, write "SUPPRESS" into column B. Then filter column B to show only rows without "SUPPRESS" so the import file is ready to copy.
When you need to deduplicate after cleaning
Multiple registrants used different casings or typos of the same email. After cleaning, duplicates appear.
For each email in column A of the Registrations tab, use DaData to validate and correct it into column B. Then identify any duplicate corrected emails in column B and write "DUPLICATE" in column C next to the second and subsequent occurrences.
When you want the full pipeline: clean list plus a deliverability summary for the VP
You need the corrected columns and a summary so the VP of Marketing can approve the send.
For all 3,000 emails in column A of the Registrations tab, validate and correct them into column B and classify into column C using DaData. Then create a new sheet called "Send Approval" with: total emails processed, number corrected, number INVALID, number Disposable, and the estimated sendable list size (Personal + Corporate only).
The pattern: the cleaning and the deliverability summary come from the same prompt.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook with an email list, then ask it to validate and classify the addresses using DaData. The DaData integration is included in every SheetXAI plan. For related workflows, see how to normalize phone numbers in Excel or the DaData in Excel overview.