The Scenario
You've just exported 900 contacts from the old CRM into an Excel workbook. The migration plan says: clean the data, validate it, reimport. Your manager wants the first batch in the new system by Thursday. You're staring at column B (emails) and column C (phone numbers), and both need to be checked before anything gets uploaded.
The person who owned this CRM data left the company three months ago. You inherited the workbook, the migration timeline, and whatever assumptions the previous admin made about data quality.
The bad version:
- You run an email validation tool on column B, export the results, paste them back into column D — but the tool's output has 901 rows and your workbook has 900, and now you're debugging which row is off
- You search for a phone validation tool, find two options, sign up for trials on both, realize they have different output formats, and pick one arbitrarily
- You run the phone validation separately, paste those results into column E, and discover that 14 rows have the phone column in a different country format that neither tool flagged cleanly
By the time you've patched all of that together, Thursday is tomorrow and the first batch still isn't clean.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the contact data, calls Abstract's email and phone validation endpoints simultaneously, and writes the results back into your workbook in a single pass — no separate tools, no format mismatches.
Here's the prompt:
Validate the emails in column B and the phone numbers in column C for all 900 rows and add separate status columns for each
What You Get
- Column D: email validation status — deliverable, undeliverable, disposable, or unknown
- Column E: phone validation status — valid, invalid, or unrecognized format
- Rows with problems in both columns are immediately filterable, so you can triage which contacts need manual review versus which can be dropped entirely
What If the Data Is Not Quite Ready
The phone numbers are in mixed formats — some with country codes, some without
Normalize all phone numbers in column C to E.164 format first, then validate emails in column B and normalized phones in column C using Abstract API and write status columns for each
Some contacts have the email in column B but the phone is blank, and vice versa
Validate non-empty cells in column B as emails and non-empty cells in column C as phone numbers using Abstract API — write email status to column D and phone status to column E, leave cells blank where the source field was empty
Contacts are split — current customers in one worksheet, prospects in another
Validate emails in column B and phones in column C for all rows in the Customers worksheet and the Prospects worksheet using Abstract API, and write separate status columns to each worksheet
The full pre-import cleanup in one shot
Deduplicate rows by email address in column B, validate unique emails and phone numbers in columns B and C using Abstract API, mark rows where either field is invalid with 'Review' in column F, and sort so all Review rows are at the top of the workbook
The dedup, the dual validation, and the flagging happen as one instruction — the workbook that comes out is ready to hand to whoever's doing the import.
Try It
Open your contact export and Get the 7-day free trial of SheetXAI — ask it to validate both the email and phone columns in one pass using Abstract API before your migration deadline. Also relevant: how to bulk validate emails before a campaign send and the Abstract API hub overview.