The Scenario
Someone left the company three months ago and their job included tracking estimate conversion. Their successor just inherited the role, opened RepairShopr, and found 200 estimates created in the past 90 days — accepted, declined, and pending — with no documentation of what the conversion rate actually is. The new owner of the role is a sales coordinator, not a data analyst. They need the estimates in a Google Sheet so they can calculate the conversion rate and present it to leadership next week.
The bad version:
- Export estimates from RepairShopr to CSV. Open the CSV in Sheets. Discover the "status" column has five different values spelled inconsistently — "Accepted," "accepted," "ACCEPTED," "Invoice Created," and blank.
- Spend an hour normalizing status values with find-and-replace before the pivot table will count them correctly.
- Build the pivot, present it to leadership, and then get asked why the numbers don't match what's in RepairShopr.
The handoff is already awkward. The data prep making it worse is avoidable.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet, understands what you're working with, and through its built-in RepairShopr integration it can pull all estimates with clean, consistent field values — no status normalization required.
Fetch all RepairShopr estimates and write estimate number, customer name, subtotal, tax, total, and status into columns A through F
What You Get
- Column A: estimate number
- Column B: customer name
- Column C: subtotal (numeric)
- Column D: tax (numeric)
- Column E: total (numeric)
- Column F: status as returned by RepairShopr
What If the Data Is Not Quite Ready
You only want estimates from the past 90 days
List all RepairShopr estimates from the last 90 days with customer name, total amount, and status in columns A through C
You want a conversion rate formula alongside the data
Fetch all RepairShopr estimates from the last 90 days and write estimate number, customer name, total, and status into columns A through D; then in cell F2 write a COUNTIF formula counting rows where column D equals "Accepted," and in F3 write a formula dividing F2 by the total number of estimate rows to calculate the conversion rate
Some estimates are linked to invoices and you want to flag those separately
Pull all RepairShopr estimates from the last 90 days and write estimate number, customer name, total, status, and linked invoice number into columns A through E; put "CONVERTED" in column F for any row where column E is not empty
Full conversion analysis in one pass: pull estimates, classify, summarize
Fetch all RepairShopr estimates from the last 90 days and write estimate number, customer name, total, and status into columns A through D; categorize each in column E as "Won," "Lost," or "Open" based on the status value; add a summary table starting in column G showing total count, total value, and percentage for each category
The pattern: one prompt delivers the analysis-ready output, not just the raw export.
Try It
Open a Google Sheet and get the 7-day free trial of SheetXAI — ask it to pull all your RepairShopr estimates from the last quarter, classify them by outcome, and add a summary table for your leadership presentation. For related work, see how to export unpaid invoices for collections or the RepairShopr integration overview.