Use case: customer feedback
Customer Feedback Thematic Analysis with AI
Analyze customer feedback from NPS, support tickets, reviews and surveys. Apercu groups comments into AI-labeled themes with counts and real examples.
By Apercu team · Last updated
Customer feedback thematic analysis turns unstructured comments into recurring topics your team can act on. Apercu analyzes NPS comments, support tickets, reviews, survey answers, interview notes and chat transcripts by grouping similar comments into AI-labeled themes with counts and examples.
Why customer feedback is hard to analyze by hand
Most teams collect more feedback than they can read. The information is there, but turning raw text into priorities takes time nobody has:
- NPS comments: you know the score, but reading 600 comments to learn why it moved takes half a day.
- Support tickets: agents solve tickets one at a time, so nobody sees that one issue drives 30 percent of the volume.
- Reviews: the recurring praise and complaints are buried across hundreds of reviews on several sites.
How to analyze customer feedback with Apercu
- Export your feedback. Any CSV or Excel file with a text column works: exports from your helpdesk, CRM, survey tool or review platform.
- Upload and add context. Confirm the suggested text column and optionally describe the data, for example "Detractor comments from our mobile app". Context helps the AI choose clearer labels.
- Get AI-labeled themes. Apercu groups comments that share similar wording and topics, then names each group.
- Check the themes against real comments. Every theme shows its size, percentage and example comments. Rename themes or move comments if something landed in the wrong group.
- Share the results. Export JSON on any plan, or CSV, Excel and a PDF report with an executive summary and recommendations on paid plans.
- Repeat monthly. Run fresh feedback through Apercu each month and compare saved projects to see which themes grow or shrink after product changes.
What you get from each analysis
- AI-labeled theme names, such as "Billing Confusion" or "Onboarding Gaps"
- Count and percentage for each theme, plus a chart of the distribution
- Real customer comments behind every theme
- Search across all comments to find specific mentions
- On paid plans: an AI-written executive summary, key findings and recommended actions, exportable as a PDF
Teams that use Apercu for customer feedback
- Product managers: rank feature requests and pain points by how many customers raise them
- Customer success teams: understand the most common reasons customers get in touch or leave
- Support managers: find the ticket categories that drive the most volume
- CX researchers: turn large volumes of qualitative feedback into a theme structure for quarterly reports
- Marketing teams: learn the words customers use, and reuse them in messaging
- Founders: see what early users praise, struggle with and ask for
Related feedback analysis pages
- NPS comment analysis for finding what drives your score
- Support ticket analysis for top contact drivers
- User interview analysis for research sessions
- Thematic analysis vs sentiment analysis: which one your feedback needs
- Open-ended survey response analysis for NPS comments and research questionnaires
- Product review thematic analysis for G2, app store and marketplace reviews
- Automated thematic analysis for teams replacing manual coding
Frequently asked questions
What types of customer feedback can Apercu analyze?
Any freeform text you can export to a CSV or Excel column: NPS comments, support ticket descriptions, customer interview notes, post-purchase survey answers, in-app feedback, product reviews and chat transcripts.
How does Apercu categorize customer feedback?
Apercu groups comments that talk about the same thing, then uses AI to read each group and propose a descriptive name such as 'Billing and Invoice Issues' or 'Onboarding Friction'. You see the theme name, how many comments it contains and real examples.
Can Apercu analyze support ticket data?
Yes. Export your tickets with the subject or description in one column and upload the file. Apercu groups the tickets into recurring issue categories, so you can see where support volume is concentrated and which product fixes would remove the most tickets.
How accurate is the AI at categorizing customer feedback?
Accuracy depends on the size and quality of the dataset, but Apercu reliably surfaces the main recurring topics in typical business feedback. It applies the same method to every comment, which avoids the drift you get when several people tag by hand. Check the example comments in each theme before acting, and rename themes or move comments where needed.
Can I analyze feedback over time and compare results?
Yes. Apercu saves every analysis as a project, so you can run a new analysis each month on fresh feedback and compare the theme percentages to see whether issues are shrinking, growing or changing after product updates.
What is the difference between thematic analysis and sentiment analysis?
Sentiment analysis tells you whether feedback is positive, negative or neutral. Thematic analysis tells you what the feedback is about. Themes are usually more actionable: knowing that 20 percent of complaints are about slow response times gives your team a specific problem to fix. Apercu focuses on themes and does not score sentiment.