The Scenario
Your contact form has been running on Formcarry for three months. You just got a Slack message from the sales lead asking for a segmented breakdown of all 350 submissions by lead source before the end of the day so they can prioritize outreach for this week.
You've looked at the Formcarry dashboard. The data is there. But there's no built-in way to group by field value, and the export is a flat CSV that drops into your Downloads folder with column names that don't match your CRM.
The bad version:
- Export the CSV, open it in a new worksheet, manually rename the headers to match your convention, then scan for the 'source' field column and start building a COUNTIF table by hand
- Realize partway through that twelve submissions have a blank 'source' value, decide whether to count those as "direct" or exclude them, go back and fix your formulas
- Copy-paste the finished summary into a second worksheet, notice the formatting is inconsistent, fix it, then send the link to sales — and get a reply ten minutes later asking why the total doesn't add up to 350
The constraint here isn't time, exactly. It's that the work you just described is data janitorial work that should take a script thirty seconds, not a person forty-five minutes. And you were supposed to spend this afternoon actually analyzing what those leads mean, not wrangling the format.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads your workbook, understands the structure, and through its built-in Formcarry integration it can fetch submissions directly and reshape them however you need — no exports, no formula gymnastics.
Fetch all submissions from my Formcarry form and paste them into this sheet starting at row 2 — one submission per row, each field as its own column with headers in row 1
What You Get
- Row 1 fills with column headers pulled directly from the form field names — no manual renaming
- Each submission lands in its own row starting at row 2, with blank cells for any field that wasn't completed on that entry
- If a submission has a field that most others don't, a new column is added automatically to accommodate it
- The worksheet is ready to filter, pivot, or COUNTIF against without any additional cleanup
What If the Data Is Not Quite Ready
The 'source' field has inconsistent values across submissions
Some entries say "Google," some say "google," some say "Google Ads" — the field wasn't validated at the form level.
Fetch all Formcarry submissions, normalize the 'source' field values to title case, then count unique values and write a summary table to a new worksheet called 'Source Breakdown'
Some submissions are missing the 'email' field entirely
Old form versions didn't require email, so a chunk of submissions came in without one.
Pull all Formcarry submissions and write them to this sheet — flag any row where the 'email' field is blank by writing "missing" in column H instead of leaving it empty
You need to join submission data against an existing lead list in column A
You have a list of known prospect emails in column A of the 'Known Leads' worksheet and want to see which Formcarry submissions overlap.
Fetch all Formcarry submissions, then compare each submission's 'email' field against the list in 'Known Leads'!A:A and mark matching rows "existing lead" in column I, non-matching rows "new lead"
Full pipeline: clean, segment, count, and flag in one shot
Pull all Formcarry submissions, normalize 'source' to title case, write every submission to this sheet with headers in row 1, add a column J that flags any row missing an email as "incomplete", then write a source breakdown count table to a new worksheet called 'Source Breakdown'
One prompt handles the data pull, the normalization, the structural flag, and the summary. The pattern is to ask for cleanup and output in the same instruction rather than running them in sequence.
Try It
Open an Excel workbook where you want to land your Formcarry submissions, then get the 7-day free trial of SheetXAI and ask it to fetch all entries and write them with a source breakdown. For large historical datasets, see the spoke on paginating through all submissions. For time-based comparison work, see the spoke on comparing Q1 vs Q2 submission volumes.