We collect feedback. Then ignore it.

The plan

Turn a small feedback batch into traceable themes. This is a manual, AI-assisted workflow: you copy approved inputs into Claude, check the output, and move the accepted result into your existing process. Start with the synthetic example below.

You need access to Claude and your team’s approved document or spreadsheet workspace. Use only data your business permits in those services. Tool plans and limits vary; this walkthrough does not require an API connection.

1. Prepare a small, useful input

Put 10–20 anonymized comments in a sheet with feedback_id and exact_text. Define the date range and source. Remove names and order numbers. Keep repeated comments identifiable so a repost does not count as a new customer.

Use this fictional sample for your first run:

F01: Delivery took too long.
F02: The parcel arrived late but the support team was helpful.
F03: The size guide was confusing.
F04: Support answered my question quickly.

2. Give Claude a precise job

Open a new chat at claude.ai. Paste the prompt below, then paste the sample after it under a heading called INPUT. For a PDF, attach the sample file to the chat instead. Keep the source and instructions clearly separated.

Assign each feedback ID one primary theme and optional secondary themes. Return ID, primary_theme, sentiment, and an exact supporting quote. Treat mixed comments as mixed. Do not infer customer identity or root causes. Then suggest one question per theme for the business owner; do not estimate prevalence outside this sample.

INPUT:
[Paste the sample or your approved source here.]

Read the response before continuing. If required columns are missing, ask: “Return the same result using every requested column. Mark unsupported fields UNKNOWN.” Do not fill gaps by asking the model to guess.

3. Check against the expected result

F01 and F02 → delivery; F02 also mentions helpful support. F03 → product information. F04 → support. Two of four primary themes are delivery; this says nothing about all customers.

4. Put the checked result to work

Check every assignment against the original. Count primary themes in the sheet with COUNTIF, for example =COUNTIF(C2:C5,“delivery”) if column C holds primary themes. Keep secondary counts separate. Choose one investigation with an owner and a due date, using real operations data to verify the cause.

Keep a status of draft, needs clarification, approved, or completed beside each item. A draft is not a completed action. If the service is unavailable, use the original source and your normal manual process.

5. Test the awkward cases

Test sarcasm, a mixed review, and a duplicated comment. Flag ambiguous sentiment and do not double-count duplicates. Re-run the same sample to inspect theme consistency before using the categories for a larger batch.

Run the same cases again after changing the prompt or source. Keep the input, output, correction, and reviewer decision together. Expand only when someone can reliably check the results.

If the result is off

Narrow the input to one record and recheck the source. For missing facts, ask the process owner. For an unsupported statement, remove it and add an explicit constraint to the prompt. If the same failure repeats, do the task manually until the source or instructions are fixed.

Scope: This is an original illustrative recipe with synthetic data, adapted from the field guides below. It is not a live-tested integration or a claim of time saved. Claude’s file-upload documentation explains supported attachments; the copy-and-paste path does not need file uploads.