As of 13 August 2026, AI can only partly create a customer feedback dashboard.

What to actually do

1. Hand it to a person

   The route this page recommends

   A person who owns the outcome does this end to end, worth it when the failure is dear.

2. Use a tool built for this

   Second choice

3. Do it yourself

   The distant third

   A chat interface, power-user skill, and roughly 1 hour until you can act on the result.

   How to actually do it

   1. Export the feedback responses from your survey, CRM or support system as CSV or spreadsheet data, remove unnecessary names and contact details, and keep the column headings and response dates.
   2. Open a chatbot, paste the prompt, attach the anonymised export, and ask it to identify the available rating, NPS, date, channel, product, segment, category and comment fields before generating the dashboard.
   3. Copy the returned HTML into a plain-text file named feedback-dashboard.html and open it in a modern browser.
   4. Compare the dashboard response count, date range, rating totals and any NPS calculation with a manual spreadsheet calculation from the original export.
   5. Apply each filter and download a filtered extract, then compare its rows and totals with the corresponding filtered rows in the source spreadsheet.
   6. Read a sample of the theme labels and example comments against the original responses, and change or remove labels that do not match the wording or your team’s agreed categories.
   7. Ask a colleague who understands the customer-service context to check the conclusions and data-quality report before sharing the dashboard internally.

   Prompt

   Create a customer feedback dashboard from the CSV or spreadsheet data attached below. Use only figures present in the data and do not invent missing values. First identify the date, rating or NPS, channel, product, customer segment, issue category and free-text comment columns, and state any assumptions. Then produce a self-contained HTML file that works offline in a modern browser, with no external libraries or network requests.
   
   Include:
   1. Total responses and response rate only if response-rate fields exist.
   2. Average rating and NPS only when the required fields and valid response scales are present. Explain the formula used.
   3. Response volume over time.
   4. Breakdown by rating, channel, product and issue category when those columns exist.
   5. A table of the most common themes from free-text comments, with the supporting response count and example comments labelled as examples.
   6. Filters for date range and every available categorical field.
   7. A download option for the filtered rows.
   
   Do not expose names, email addresses, phone numbers or other unnecessary personal data in the dashboard. Remove or mask those fields in the displayed output. Keep comments verbatim only where they contain no unnecessary personal data; otherwise redact the personal data and mark the redaction. Flag duplicate, blank, invalid or out-of-range rows instead of silently fixing them.
   
   Return the complete HTML in one code block, followed by a short data-quality report listing excluded rows, assumptions, missing fields and checks I must perform. If the data is missing, return a clearly labelled dashboard template and do not fabricate sample business results.
   
   Data:
   [PASTE OR ATTACH YOUR ANONYMISED FEEDBACK EXPORT HERE]

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.