Automate thematic analysis
Automated Thematic Analysis for Open-Ended Text
Automate thematic analysis for surveys, interviews, reviews and support tickets. Apercu groups text, labels themes and writes a report in minutes.
By Apercu team · Last updated
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 coding in Excel, NVivo or Python, Apercu gives you AI-labeled themes, counts and examples in minutes, plus a written report on paid plans.
Why manual thematic analysis takes so long
The Braun and Clarke method is the standard for rigorous qualitative research. It was built for in-depth studies, not for an analyst who needs themes from 800 survey responses by Friday.
You read every response. Getting familiar with the data comes first. For 500 responses, that alone takes 1 to 2 hours of focused reading.
You code by hand. You go through the data again and apply codes to each response. It is slow, interpretive work that is hard to keep consistent across a large dataset or several coders.
You build themes by hand. You group codes into candidate themes, review them, refine them and name them, often over several rounds.
You still have to write it up. An executive summary, theme descriptions, supporting quotes and recommendations add another hour or two.
For a full walkthrough of the manual process, see how to analyze open-ended survey responses.
How automated thematic analysis works in Apercu
Apercu automates the core steps of thematic analysis: grouping similar responses, naming themes and counting how often they occur.
- Upload your file. Upload a CSV or Excel file and confirm the text column Apercu suggests.
- Choose theme granularity. Choose broad themes for an overview or a balanced level of detail. Paid plans add a detailed option and a custom number of themes. Optionally describe your dataset to improve the labels.
- AI groups and labels your responses. Apercu groups responses that talk about the same thing. AI then reads each group and proposes a descriptive name, giving you a usable code frame.
- Review themes and export. Browse each theme with its count, percentage and real example responses, rename themes or move responses, then export. JSON is available on every plan. Paid plans add CSV, Excel and a PDF report with a summary, key findings and recommendations.
Manual coding vs Python scripts vs Apercu
Estimates for a dataset of 500 open-ended responses.
| Factor | Manual | Python scripts | Apercu |
|---|---|---|---|
| Time for 500 responses | 4 to 8 hours | 2 to 4 hours (setup and run) | Minutes, plus review |
| Coding skills needed | None | Python | None |
| Theme naming | You write labels | You write labels | AI proposes labels |
| Multiple themes per response | Yes | Depends on method | One theme per response |
| Consistency | Varies between coders | Repeatable | Repeatable |
| Written report | Written by hand | Written by hand | AI-generated (paid plans) |
| Cost | Staff time | Staff time and compute | Free plan, paid from $14.99/mo |
| Re-run on new data | Start over | Re-run the script | Upload the new file |
Best use cases for automated thematic analysis
- UX researchers: analyze open-ended usability study responses without days in a spreadsheet
- Product managers: summarize feature requests, support tickets and NPS comments without waiting for a data team
- Market researchers: process survey open-ends faster with a consistent, repeatable method
- Customer success teams: understand what customers say across hundreds of feedback submissions
- Academics: get a first-pass code frame for qualitative survey data before detailed manual coding
- Consultants and analysts: deliver client-ready theme reports in hours rather than days
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: find what customers praise, criticize and request in reviews
- What is thematic analysis?: definition, the six steps and examples
- Apercu vs NVivo for researchers deciding between AI themes and qualitative software
Frequently asked questions
What is thematic analysis?
Thematic analysis is a qualitative research method for identifying, analyzing and reporting patterns (themes) in text. It was formalized by Braun and Clarke in 2006 and is widely used in social science, UX research, market research and customer experience work. The process involves reading the data, applying codes, grouping codes into themes and naming those themes.
How long does manual thematic analysis take?
It depends on the dataset and the depth required. Coding 300 to 500 open-ended survey responses by hand typically takes 4 to 8 hours, including building a codebook. Datasets with 1,000 or more responses can take days.
Can AI do thematic analysis automatically?
AI can automate the most time-consuming steps. Apercu groups responses that talk about the same thing, then uses AI to propose a descriptive label for each group. You get themes, counts and example responses in minutes, and you can rename themes or move responses, just as you would review a junior analyst's first pass.
Is AI thematic analysis as accurate as manual coding?
It works best as a fast first pass for applied research such as surveys, customer feedback, product reviews and UX research. It applies the same method to every response, which avoids coder drift, but each response is assigned to one theme, and human review is still recommended for sensitive, academic or highly interpretive research.
What is the difference between Apercu and doing it in Python?
Python gives you full control but requires writing code, setting up an environment, tuning the analysis and labeling each group yourself. Apercu handles the grouping, AI theme labeling and reporting in a no-code interface, so you get labeled themes without writing any code.
What file formats does Apercu accept?
CSV and Excel (.xlsx and .xls) files with at least one column of text. You can analyze up to 2,500 rows on the free plan, 50,000 on Analyst and 100,000 on Pro.