By Apercu team ·
What Is Thematic Analysis? Definition, Steps and Examples
Thematic analysis explained: what it is, the six steps from Braun and Clarke, inductive vs deductive approaches, a worked example, and how AI speeds it up.
Definition: thematic analysis is a method for finding, analyzing and reporting patterns of meaning, called themes, in qualitative data such as interview transcripts, open-ended survey answers, reviews or support tickets. You label pieces of text with codes, group related codes into themes, and describe what each theme says about the question you are investigating.
It is one of the most widely used qualitative methods because it is flexible: it works on almost any kind of text, for academic research and for business questions like "why are customers leaving?"
A quick example
Imagine 200 answers to "Why did you cancel your subscription?"
| Response | Codes |
|---|---|
| "Too expensive for how often I used it." | Price, Low usage |
| "Couldn't figure out how to export my data." | Usability, Export |
| "We moved to a tool our whole company uses." | Switched to company tool |
| "Only used it twice a month, not worth it." | Low usage, Price |
After coding all 200 answers, related codes group into themes such as "The product is priced for heavy users, but most cancellers were light users" and "Teams consolidate onto company-wide tools." Each theme is then counted, described and supported with quotes.
The six steps of thematic analysis
The best-known framework comes from Virginia Braun and Victoria Clarke's 2006 paper "Using thematic analysis in psychology".
- Familiarize yourself with the data. Read the responses, often more than once, and note first impressions.
- Generate initial codes. Label interesting features of the data systematically across the whole dataset.
- Search for themes. Group codes into candidate themes and gather the data relevant to each.
- Review themes. Check that each theme fits its coded extracts and the dataset as a whole. Split, merge or drop themes.
- Define and name themes. Write a clear definition and a name that says what the theme is about.
- Produce the report. Tell the story of the data with themes, counts where useful, and vivid, representative quotes.
Braun and Clarke later refined their approach as reflexive thematic analysis, which emphasizes the researcher's active interpretation rather than themes "emerging" from the data.
Types of thematic analysis
| Choice | Option A | Option B |
|---|---|---|
| Where codes come from | Inductive: from the data itself | Deductive: from a theory or existing framework |
| Level of meaning | Semantic: what people explicitly said | Latent: underlying ideas and assumptions |
| Approach | Reflexive: interpretation by the researcher | Codebook or coding reliability: a structured code frame applied consistently, often by several coders |
Business projects usually combine inductive coding, semantic meaning and a codebook, because they need consistent counts that a team can act on. See inductive vs deductive coding for more.
Thematic analysis vs other methods
| Method | Focus | Typical output |
|---|---|---|
| Thematic analysis | Patterns of meaning across a dataset | Themes with descriptions, quotes and often counts |
| Content analysis | Systematic counting of categories | Frequencies of predefined categories |
| Grounded theory | Building a new theory from data | A theoretical model |
| Sentiment analysis | Emotional tone | Positive, negative or neutral labels |
For the last one, see thematic analysis vs sentiment analysis.
Strengths and limits
Strengths: flexible, accessible to non-specialists, works with small and large datasets, and produces findings that are easy to communicate.
Limits: time-consuming by hand, open to inconsistency between coders, and themes can end up as vague topic summaries if they are not defined carefully.
Thematic analysis for business data
In business settings, thematic analysis is applied to:
- Open-ended survey responses
- NPS comments
- Product reviews and app store reviews
- Support tickets
- Employee survey comments
The goal is usually practical: a ranked list of themes, each with a count, quotes and an owner.
Can AI do thematic analysis?
AI can automate the slowest steps: grouping similar responses, proposing theme names and counting them. Tools like Apercu produce a first set of themes in minutes, which the analyst then reviews, renames and refines. That fits steps 2 to 5 well for applied research. For academic work, treat AI output as a starting point and document how you reviewed it, since reflexive thematic analysis depends on the researcher's interpretation.
FAQ
What is thematic analysis in simple terms?
It is a way of reading lots of text, labeling what each part is about, grouping those labels into bigger patterns called themes, and reporting what the themes mean.
What are the 6 steps of thematic analysis?
Familiarize yourself with the data, generate initial codes, search for themes, review themes, define and name themes, and produce the report. The framework comes from Braun and Clarke (2006).
What is the difference between a code and a theme?
A code labels one specific idea in the data, such as "Price". A theme is a broader pattern that groups related codes and says something meaningful, such as "Light users feel the product is overpriced."
Is thematic analysis qualitative or quantitative?
It is a qualitative method. Business applications often add counts and percentages to themes, which gives a mixed view of what people said and how many said it.
How many themes should a thematic analysis have?
There is no fixed number. Many projects settle on 4 to 10 main themes, sometimes with sub-themes. Too many themes usually means some should be merged.