The Scenario
You are a digital experience analyst at a financial services firm. Every month, your team presents a board-ready executive dashboard. This month's slide needs NPS numbers from your Mopinion app feedback report — specifically: average score, total response count, and the detractor/passive/promoter breakdown from 800 raw responses.
You've done this calculation before. You pull the data, paste it into a sheet, write the COUNTIF formulas, check the averages, and build the summary table. It takes about 45 minutes when everything goes right.
The bad version:
- Export the Mopinion report as CSV, import it into a new sheet, and realize the score column is formatted as text instead of numbers — so your AVERAGE formula returns an error.
- Fix the column format, rebuild the formulas, and then realize you forgot to filter out test submissions that have scores outside the 0–10 range.
- Redo the summary table. It's now 30 minutes before the deck review, and the numbers are still not validated.
The slide is going to a board meeting. The calculation has to be right. There is no buffer for a second iteration.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It connects to Mopinion and can pull the raw responses and compute the summary statistics in a single step — no formula debugging, no text-to-number conversions, no separate cleanup pass.
Fetch all feedback from Mopinion report 22511, write each response to a Data tab, then in a Summary tab calculate average score, total response count, and count of scores 0–6, 7–8, and 9–10.
What You Get
- The Data tab holds all 800 responses — one row per submission, with timestamp, NPS score, and open-text comment.
- The Summary tab shows: average NPS score, total response count, detractor count (0–6), passive count (7–8), and promoter count (9–10).
- The summary is ready to drop into the board slide without additional formatting.
- If you need percentages, ask for them in the same prompt.
What If the Data Is Not Quite Ready
Some responses have scores outside the 0–10 range (test submissions)
You need to exclude any row where the score is null or outside the valid NPS range before computing the summary.
Fetch all feedback from Mopinion report 22511, skip any rows where the score is blank or outside 0–10, write the clean responses to the Data tab, then compute the NPS summary in the Summary tab.
You need promoter/detractor percentages, not just counts
The board slide format requires percentage columns, not raw counts.
Pull all feedback from report 22511 to the Data tab, then in the Summary tab show total response count, average score, detractor count and percentage (scores 0–6), passive count and percentage (7–8), and promoter count and percentage (9–10).
You need to break the summary down by dataset
The report spans three datasets — post-onboarding, in-app, and post-support. You need the NPS breakdown per source.
Fetch all feedback from Mopinion report 22511, write each response to the Data tab with dataset name, then in the Summary tab compute average score and detractor/passive/promoter counts separately for each dataset.
Pull responses, clean outliers, compute the summary, and add a trend comparison in one step
Last month's numbers are in the Prev Month tab. You want the current period summary next to a variance column.
Fetch all feedback from Mopinion report 22511 submitted in May 2026, exclude any rows with blank or out-of-range scores, write the clean data to the Data tab, then compute average score and detractor/passive/promoter counts in the Summary tab. Add a Variance column comparing each metric to the corresponding value in the Prev Month tab column B.
Computing the delta and the summary in the same prompt means the slide is ready, not just the raw data.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet where you're building an executive feedback dashboard. Pull your Mopinion NPS data and compute the summary in one ask, then see the cross-dataset report pull spoke or return to the Mopinion integration overview.