Analyze customer feedback at scale

What it is

Reviews, survey answers, support tickets and call notes are mostly free text, which is hard to count. AI can read that text at volume and turn it into structured fields, such as theme, sentiment, urgency and product area, that you can count, chart and track month to month.

How it works

1. Export feedback from each source with the date, source and customer segment attached. Drop fields you don’t need, such as contact details.
2. Write a theme list with a one-line definition for each theme.
3. Tag each item against that list with structured outputs. OpenAI’s Structured Outputs feature makes the model’s reply adhere to a JSON schema you supply, on supported models, so every item comes back with the same fields.
4. Look for themes you missed. OpenAI lists clustering (grouping text by similarity) and classification among the uses of embeddings. Cluster a sample, then add useful new themes to your list.
5. Process backlogs in bulk. OpenAI’s Batch API runs asynchronous jobs at a 50% discount with a 24-hour turnaround (checked 2026-10-17) (see batch processing).
6. Spot-check, then report. Compare a random sample of tags with the raw text, then report top themes and changes with two or three real examples each.

Benefits

• Product and messaging decisions rest on counted evidence, not the loudest complaint.
• Emerging issues show up early as a rising theme.
• Far less manual reading: people read the examples, not the whole pile.

Limits and risks

• Tags can be wrong. Sample and correct them, and treat small shifts with caution.
• Feedback often contains personal data. Handle it under your data governance rules.
• Themes drift as your product changes. Review the list on a schedule.
• No feedback tool is recommended here. IonBin’s catalog has none for this job yet; the method works with exports and an API.