The Scenario
Earlier this year the church ran a contact re-verification campaign — a short form sent to the full congregation asking members to confirm or correct their phone number and email address. The responses came back through a third-party form tool, were aggregated by a volunteer, and landed in a Google Sheet: CHMeetings person ID in column A, corrected mobile in column B, corrected email in column C. Some rows also have an "opt-out" flag in column D for email and column E for text.
There are 120 rows. Every one of them needs to be applied to the corresponding CHMeetings person record.
You found out about this sheet this morning. The data steward who compiled it has been out this week and left a note asking you to "just get it into the system." The note did not include instructions for how to do that.
The bad version:
- Open CHMeetings, search for the person by ID or name, open their record, click Edit, update the mobile, update the email, save, go back, search for the next person.
- After 15 rows, realize you've been pasting the corrected email into the mobile field on alternating records because the CHMeetings form layout isn't the same order as the sheet.
- Hit row 40 and notice the opt-out flags — which require a separate toggle in the record, not the same field as email and mobile — and realize you've been skipping them entirely.
120 records. Each one is four to six clicks and two to three field updates. This is four hours of work that produces no insight, serves no member, and will need to be done again in six months.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the correction data already in the sheet and applies each update to the matching CHMeetings person record in one pass.
For each person ID in column A, update the CHMeetings record with the new mobile number from column B and new email from column C — write "updated" or the error message into column D.
SheetXAI processes all 120 rows, sends the update call for each person ID, and writes the outcome into column D. Then the opt-out flags:
Update all CHMeetings person records listed in column A to set do_not_email to true where column D says "opt-out", and do_not_text to true where column E says "opt-out".
Two prompts cover what would have been a full day of manual record navigation.
What You Get
- Column D fills with "updated" for each record successfully corrected, or an error description for any that failed — ID not found, mobile format invalid, etc.
- Opt-out preferences applied in a second pass across the same person IDs.
- A complete audit trail in the sheet: you can see which rows succeeded, which errored, and why — without logging into CHMeetings to verify each one.
- 120 records corrected in the time it takes to handle five manually.
What If the Data Is Not Quite Ready
Some mobile numbers in column B are in inconsistent formats — some have country codes, some don't
Normalize all mobile numbers in column B to E.164 format using a +1 country code for any that lack one, then update the CHMeetings person records in column A with the normalized mobile and the email from column C — write "updated" or the error into column D.
A few person IDs in column A are blank — rows where the volunteer didn't match the form response to a record
For each row where column A is not blank, update the CHMeetings record with the corrected mobile from column B and email from column C — for rows where column A is blank, write "no ID — manual match needed" in column D.
The email corrections in column C need to be validated before being applied
Check each email in column C against basic format validation — flag any that don't match a valid email pattern with "invalid email" in column D — then update CHMeetings person records in column A with the corrected mobile from column B and any emails that passed validation, writing "updated" or the error into column D.
Normalize phone formats, validate emails, apply all corrections, set opt-out flags, and summarize what's left to do
Normalize mobiles in column B to E.164, validate emails in column C and flag invalid ones, update CHMeetings records in column A with normalized mobile and validated email, set do_not_email to true where column D is "opt-out" and do_not_text to true where column E is "opt-out", write the outcome into column F, and at the bottom of column F write a count of how many records updated successfully and how many need manual review.
One prompt applies every correction category and produces the summary the data steward will need when they're back next week.
Try It
Open the Google Sheet with your re-verification campaign responses and get the 7-day free trial of SheetXAI — apply all 120 corrections to CHMeetings in a single ask, including opt-out flags, and get a row-by-row outcome log. Then see how to pull the full CHMeetings member directory into a sheet for segmentation, or return to the CHMeetings integration overview.