The Scenario
Your collections rate has been slipping and you're not sure which part of the billing system is the problem. Is it one specific payment form? A particular plan tier? You've been staring at the MoonClerk dashboard aggregate numbers, but those don't break down by form — they show you the total failed payment count across the whole account, not which forms are generating most of the failures.
You need a per-form breakdown: how many payments went through each form, how many failed, and what the failure rate looks like as a percentage. That data is sitting inside MoonClerk. It's not in your spreadsheet.
The bad version:
- Export the last quarter's payment history as a CSV, open it in a new tab, and build a pivot table grouped by form name and status
- Realize the form name field in the CSV is sometimes blank — some older payments don't have a form association, and the pivot totals are off
- Try to fill in the blanks by cross-referencing the customer record against the form list, which requires a vlookup that breaks on any form name with a comma in it
The collections review is Friday. Your pivot table has a structural gap and you have no clean way to resolve it before then.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet and through its built-in MoonClerk integration can pull the payment history and build the per-form summary in one step — so the analysis doesn't start from a broken CSV export.
Fetch all MoonClerk payments grouped by form ID and write form name, total payment count, total successful, total failed, and success rate percentage into my Google Sheet 'Form Performance'
What You Get
- One summary row per payment form written to the 'Form Performance' tab
- Columns: form name, total payment count, total successful, total failed, and success rate as a percentage (e.g., 87.3%)
- Forms with zero failures appear in the list with 100% success rate — so the ranking is complete, not just the problem cases
- If a payment has no form association, it appears in a row labeled 'Unassigned' so the totals still reconcile
What If the Data Is Not Quite Ready
I want the raw payment rows first, then I'll build the summary myself
List all MoonClerk payments from the last quarter and write payment date, form name, customer name, amount, and status into the 'Payments Q3' sheet — one row per payment, include successful, failed, and refunded
I want to flag any form with a failure rate above 10%
Fetch all MoonClerk payments grouped by form name and write form name, total payments, total successful, total failed, and success rate into 'Form Performance' — add a column called 'At Risk' and mark any form where the success rate is below 90%
Some payments have no form name — I want to trace them to a customer
Pull all MoonClerk payments from the last quarter and write them into 'Payments Q3' with payment date, form name, customer name, amount, and status — for any row where the form name is blank, look up the customer name in the 'Customer Export' tab and pull the form name from there if available
Full collections analysis with flags and drill-down in one shot
Fetch all MoonClerk payments from the last quarter, write each payment into 'Payments Q3' with date, form name, customer name, amount, and status — then in a separate 'Form Performance' tab write a summary grouped by form name showing total count, successful count, failed count, and success rate percentage — mark any form with a success rate below 90% as 'High Risk' and sort the summary by success rate ascending
Start with the full dataset and the summary in one pass — so the drill-down rows and the executive summary are both ready at the same time.
Try It
Get the 7-day free trial of SheetXAI and open the Google Sheet where you track billing performance — ask it to pull your MoonClerk payment history and build a per-form failure rate breakdown. See also: exporting payments for quarterly reconciliation and the full MoonClerk overview.