The Scenario
You're a key-account manager inheriting a territory from someone who left last month. Their handoff doc is a Google Sheet — 200 customer emails in column A, account names in column B, last-touched dates in column C. Half of those contacts haven't been reached in over a year. You have no idea how many of them are still at the same company.
The bad version:
- Open each email in LinkedIn, check the "Current position" field manually, mark "Changed" or "Same" in the sheet — 200 rows, maybe two seconds each if you're fast, which means you're still doing this at midnight
- Send a re-engagement email to the full list and watch the bounce rate climb as Datagma's data could have told you which addresses are now stale
- Export the list to Datagma's UI, run the job-change scan, download the results CSV, re-import it, fix the column mapping where "new_employer" landed in the wrong column
You're supposed to be re-qualifying accounts, not auditing contact data. Nobody handed you this territory so you could spend week one doing spreadsheet maintenance.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands which columns hold what, and through its built-in Datagma integration it can run a job-change scan across every email in the list and write the results back inline. No export, no re-import, no column mapping.
For each email in column A, use Datagma to check if the contact has changed jobs and write 'Changed' or 'Same' into column B, along with their new employer in column C if detected
What You Get
- Column B: "Changed" or "Same" for each email, based on Datagma's job-change signal
- Column C: new employer name for contacts where a job change was detected
- Rows where Datagma returned no signal are noted so you can prioritize manual review rather than send to a cold address
What If the Data Is Not Quite Ready
The email column has formatting inconsistencies
Some entries in column A have trailing spaces, some have uppercase characters, a few have "mailto:" prepended from a CRM export. Datagma's API expects clean email addresses.
Clean the emails in column A — trim whitespace, lowercase, strip any "mailto:" prefix — then run Datagma job-change scan for each one and write 'Changed' or 'Same' into column B with the new employer in column C where detected
You only want to scan contacts who haven't been touched in 90+ days
The sheet has a last-contacted date in column D. Running the full scan on recently active contacts is wasted API spend.
Only scan contacts where column D is blank or more than 90 days ago — use Datagma job-change scan on the email in column A and write the result into column B, new employer into column C
You want to highlight movers for immediate follow-up
Your manager wants a visual way to see which accounts need an urgent re-qualification call this week.
Scan all 200 customer emails in my 'Key Accounts' sheet for job changes using Datagma and highlight rows in yellow where a job change was detected
Full audit with cleanup, scoping, and CRM-ready output
The emails need cleaning, you want to skip recently contacted accounts, and you want the output formatted the way your CRM import expects it.
Clean emails in column A, skip rows where column D shows a date within the last 90 days, run Datagma job-change scan for remaining rows, write 'Changed' or 'Same' into column B and new employer into column C, then copy only the 'Changed' rows into a new tab called 'Movers' formatted for CRM import
Try It
If you're managing a contact list that's gone cold and need to know who's still reachable, open the sheet in Google Sheets and get the 7-day free trial of SheetXAI. Tell it to run a Datagma job-change scan and it'll surface the movers without you touching the API. For related workflows, see bulk-enrich a lead list or find verified work emails.