Customer Feedback Prioritizer
Group manually tagged feedback into themes and compare frequency, pain and evidence confidence.
- 1Explore the example
- 2Edit your assumptions
- 3Compare & export
Loading the worked example…
Your working data
Edit cells directly or import a CSV. Download “Input CSV” for the required column names. Up to 200 rows; scroll the table sideways for additional fields.
Detailed analysis
Exports include all results, regardless of the search filter. The HTML report includes assumptions and source data; use your browser’s Print command for PDF.
Chart shows the first 20 rows; negative values use a warm color. Click table headers to sort.
Compare up to four scenarios
Capture the current inputs, change assumptions and capture again. Scenarios are included in downloaded projects.
What this model includes
Manual themes only; no AI sentiment detection. Deduplicate people before entering counts. Weighted score = mentions × pain × confidence fraction.
Import and save safely
CSV imports use your current assumptions. Project files restore assumptions, rows and saved scenarios. No automatic saving: download a project before closing this page. Imports are limited to 10 MiB and 200 rows. Calculations run in your browser; the site’s advertising and analytics follow its privacy policy.
Using Customer Feedback Prioritizer
Group manually tagged feedback into themes and compare frequency, pain and evidence confidence.
- Review the preloaded example and the model limits before entering real data.
- Adjust minimum mentions to highlight.
- Edit the theme rows or import a CSV using the Input CSV column template.
- Open Results & chart to inspect details, sort columns and filter rows.
- Capture alternative scenarios, then download a project, result CSV or printable report.
Example
The worked example includes Slow export, Slow export, More themes, Keyboard access. Change the first minimum mentions to highlight setting and compare the result with a saved baseline.
Questions & answers
What does Customer Feedback Prioritizer calculate?
Group manually tagged feedback into themes and compare frequency, pain and evidence confidence.
What are the important limitations?
Manual themes only; no AI sentiment detection. Deduplicate people before entering counts. Weighted score = mentions × pain × confidence fraction.
Can I continue my work later?
Yes. Save a project JSON file and reopen it with Import CSV / project. It contains the working data, assumptions and up to four scenarios. Nothing is saved automatically.
What CSV columns do I need?
theme, source, mentions, pain, confidence, quote. Download Input CSV for a compatible example. CSV imports keep your current assumptions.
Are these example figures current industry benchmarks?
No. They are illustrative values to demonstrate the model. Replace them with figures appropriate to your situation.
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