The Scenario
The lead-gen form your team ran last quarter collected 600 phone numbers from prospects across seven countries. They landed in an Excel workbook exactly as submitted — some with country codes, some without, some with formatting characters, some that look like they were typed while running.
The sales ops manager inherited this workbook and now needs to import it into Salesforce before Friday's pipeline review. Salesforce will reject malformed numbers at import time, which means fixing them post-import would be even worse than catching them now.
The bad version:
- Scroll through 600 rows and eyeball which numbers look wrong — catch maybe 40% of the actual problems.
- Export the column as a CSV, run it through a phone validation library, get back a result file that uses different column names than Salesforce expects, manually map the fields.
- Discover the library flagged numbers from certain countries as invalid because it defaulted to US formatting rules, re-run with country hints, create a third output file, reimport into the workbook.
The import window is tomorrow morning. Nobody else on the team has touched this workbook before.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Excel workbook that reads your data and calls BigDataCloud's phone number validation API for each row. You write the task in plain language — it handles the API calls and the writeback.
Open SheetXAI from the Excel add-in panel and type:
Check all phone numbers in my Form Submissions sheet and mark invalid entries in column C — leave valid rows blank
What You Get
- Column C: blank for valid numbers; for invalid numbers, a short reason (e.g., "invalid format", "unrecognized country prefix", "too few digits")
- Column D (optional, if you ask): detected country code per number
- Column E (optional): number type — "mobile" or "landline"
- SheetXAI notes the total count of valid, invalid, and ambiguous numbers in the add-in panel after the run
What If the Data Is Not Quite Ready
Numbers have inconsistent formatting characters — parentheses, dashes, spaces
Validate each phone number in column B of the Form Submissions sheet using BigDataCloud — normalize formatting before validation (remove parentheses, dashes, spaces) and mark invalid entries in column C with the failure reason
The sheet mixes numbers with and without country code prefixes
Validate the phone numbers in column B using BigDataCloud — for rows that don't include a country code, assume the country from the "Target Country" column in column A and add the appropriate prefix before validating, then mark invalid entries in column C
Some numbers look valid but belong to the wrong country for this campaign
Validate all phone numbers in column B using BigDataCloud, mark invalid entries in column C, and add a column D that flags any row where the detected country code does not match the value in the "Expected Country" column in column E
Full cleanup and CRM-ready output in one pass
Validate each phone number in column B using BigDataCloud — normalize formatting, detect country code, classify type (mobile/landline), mark invalid rows in column C, and create a new worksheet called "CRM Ready" containing only valid rows with the phone number in E.164 format
One prompt handles the normalization, the validation, the classification, and the filtered export.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook with a column of form-submitted phone numbers — try the prompt above before your next CRM import and know exactly what's going in. Also see the email address verification spoke if you need to clean the email column in the same workbook.