Automate thematic analysis
Automated Thematic Analysis for Open-Ended Text
Automate thematic analysis for surveys, interviews, reviews, and support tickets. Apercu clusters text, labels themes, and creates reports in minutes.
Automated thematic analysis uses AI to group similar text responses, label recurring themes, count how often each theme appears, and summarize the findings. Apercu does this for CSV and Excel datasets, so you can analyze survey responses, interview notes, reviews, support tickets, and other open-ended text without coding every response by hand.
If you need a practical alternative to manual thematic coding in Excel, NVivo, or Python, Apercu gives you AI-labeled themes, counts, examples, and a written report in minutes.
Why manual thematic analysis takes so long
The Braun and Clarke method is the gold standard for rigor. But it was designed for doctoral research — not for the analyst who needs themes from 800 survey responses by Friday.
You read every response. Familiarizing yourself with the data is step one. For 500 responses, that alone takes 1–2 hours of focused reading.
You generate codes by hand. You go through the data again, applying descriptive codes to passages. This is slow, interpretive work that is hard to do consistently across a large dataset.
You search for themes manually. After coding, you group codes into candidate themes, review them, refine them, and name them. This cycle can take many hours.
The report is a separate task. Once you have themes, you still need to write them up: executive summary, theme descriptions, supporting quotes, recommendations. Add another hour or two.
How automated thematic analysis works in Apercu
Apercu replicates the core logic of thematic analysis — grouping similar responses, naming themes, counting occurrences — in an automated pipeline:
- Upload your file. Upload a CSV or Excel file with your text responses. Apercu detects the text column automatically.
- Choose theme granularity. Choose whether you want broad themes (few clusters) or fine-grained detail (many clusters). Optionally add context about your dataset to improve labeling quality.
- AI groups and labels your responses. K-means clustering groups similar responses. AI then reads each cluster and proposes a descriptive theme name, turning raw text into a usable code frame.
- Review themes and export. Browse themes with real example responses from your data. Export as CSV, JSON, Excel, or generate a full PDF report with insights and recommendations.
Manual coding vs Python scripts vs Apercu
Estimated for a dataset of 500 open-ended responses.
| Factor | Manual | Python/scripts | Apercu |
|---|---|---|---|
| Time for 500 responses | 4–8 hours | 2–4 hours (setup + run) | Under 5 minutes |
| Coding knowledge needed | None | Python required | None |
| Theme naming | Manual — you write labels | Manual — you write labels | AI proposes labels automatically |
| Consistency across coders | Low (inter-rater variance) | Medium | High (same model every time) |
| PDF report | Manual write-up required | Manual write-up required | AI-generated automatically |
| Reproducibility | Low | Medium | High |
| Cost | Staff hours | Staff hours + API costs | From $0 |
| Can re-run instantly | No | Yes (with effort) | Yes |
Best use cases for automated thematic analysis
- UX Researchers — analyzing open-ended usability study responses without spending days in a spreadsheet
- Product Managers — summarizing feature requests, support tickets, and NPS comments without handing it to a data team
- Market Researchers — processing survey open-ends faster, with a consistent, reproducible method
- Customer Success Teams — understanding what customers are saying across hundreds of feedback submissions
- Academics — running thematic analysis on qualitative survey data without NVivo licenses or weeks of manual coding
- Consultants & Analysts — delivering client-ready theme reports in hours rather than days, with PDF exports ready to share
Related thematic analysis workflows
- Open-ended survey response analysis - turn freeform survey answers into themes, counts, and examples
- Customer feedback analysis - categorize NPS comments, support tickets, reviews, and interview notes
- Product review analysis - identify what customers praise, criticize, and request in reviews
Frequently asked questions
What is thematic analysis?
Thematic analysis is a qualitative research method for identifying, analyzing, and reporting patterns (themes) within a body of text. It was formalized by Braun and Clarke (2006) and is widely used in social sciences, UX research, market research, and customer experience work. The process involves reading through responses, applying codes, grouping codes into themes, and naming those themes.
How long does manual thematic analysis take?
It depends on the dataset size and depth of analysis required. For a typical open-ended survey with 300–500 open-ended responses, manual thematic coding using the Braun and Clarke method usually takes between 4 and 10 hours. With larger datasets (1,000+ responses), it can take days of focused work.
Can AI do thematic analysis automatically?
Yes. Apercu automates the most time-consuming steps of applied thematic analysis: it groups similar responses together using K-means clustering, then uses AI to propose a descriptive label for each cluster. You get themes, counts, example responses, and a written summary in minutes. You can still review and adjust the output, just as you would review a junior analyst's initial pass.
Is AI thematic analysis as accurate as manual coding?
AI thematic analysis works best as a fast first pass for applied research such as surveys, customer feedback, product reviews, and UX research. It applies the same logic across every response, which reduces coder drift, but human review is still recommended for sensitive, academic, or highly interpretive research.
What is the difference between Apercu and doing it in Python with scikit-learn?
Python with scikit-learn gives you full control but requires writing code, setting up an environment, and doing the analysis manually. Apercu wraps K-means clustering, TF-IDF vectorization, and AI topic labeling into a no-code interface. If you want results in minutes without writing a single line of code, Apercu is the faster path.
What file formats does Apercu accept?
Apercu accepts CSV and Excel (.xlsx, .xls) files. As long as your file has at least one column of text responses, it will work. You can upload up to 100,000 rows on the Pro plan.