What is thematic analysis? Complete method guide

What is thematic analysis?

Thematic analysis is a qualitative research method that helps you organize complex data by identifying recurring patterns and themes. It offers a systematic process for preparing your data, coding it, and grouping similar ideas in a way that highlights the underlying message. In addition, this approach stands apart from other qualitative methods by combining a clear step-by-step technique with solid academic foundations. Practical examples drawn from real-world research help illustrate how thematic analysis can simplify the process of interpreting qualitative data and overcome common challenges. Whether you're a student or a seasoned researcher, understanding this method can clarify your path to insightful findings.

Let’s begin by breaking down each step of the process and exploring what it means for your research journey.

What is thematic analysis and how does it work?

Thematic analysis answers with a resounding yes. This powerful qualitative research method transforms seemingly disconnected data points into meaningful patterns that reveal deeper insights about consumer experiences, behaviors, and perceptions.

At its core, thematic analysis is a systematic approach to identifying, analyzing, and interpreting patterns of meaning (themes) within qualitative data. Unlike quantitative methods that focus on numbers and statistics, thematic analysis examines the underlying ideas, assumptions, and conceptualizations within text data such as interview transcripts, open-ended survey responses, focus group discussions, or social media comments.

The beauty of thematic analysis lies in its flexibility. It works across different theoretical frameworks and research questions, making it particularly valuable for consumer insights professionals dealing with diverse product categories and market segments. The method can be:

• Inductive – where themes emerge naturally from the data without preconceived notions

• Deductive – where analysis is guided by existing theoretical frameworks

• Semantic – focusing on explicit meanings in the data

• Latent – examining underlying assumptions and ideologies

For CPG professionals, thematic analysis offers a structured way to understand the "why" behind consumer behaviors. When consumers describe their experiences with your product in their own words, thematic analysis helps identify recurring patterns that might not be captured through quantitative metrics alone.

The process works by immersing yourself in the data, generating initial codes for interesting features, searching for potential themes, reviewing those themes against the dataset, defining and naming themes, and finally producing a report that tells the compelling story hidden within your qualitative data.

How to do thematic analysis

Following a structured approach to thematic analysis ensures your findings are both rigorous and insightful. Here's a practical roadmap for CPG professionals looking to extract meaningful themes from consumer feedback.

1. Familiarize yourself with the data

Begin by immersing yourself in your qualitative dataset. Read through all consumer comments, interview transcripts, or open-ended survey responses multiple times. Make initial notes about potential patterns or interesting observations. This familiarization phase is crucial for developing an intuitive understanding of what consumers are expressing about your products.

What makes this step effective? Set aside uninterrupted time blocks for deep reading rather than skimming. Consider having multiple team members independently review the data to capture different perspectives.

2. Generate initial codes

Systematically work through your entire dataset, identifying features relevant to your research questions. Create concise labels (codes) that capture the essence of data segments. For example, when analyzing feedback on a new beverage product, you might code statements like "I couldn't get the cap off easily" as "packaging difficulty."

How to code efficiently:

• Keep codes simple and precise

• Maintain a coding journal to track your decision-making

• Code for as many potential patterns as possible

• Remember that data excerpts can be coded multiple times

3. Search for themes

Examine your codes to identify broader patterns that could form potential themes. This involves sorting and collating relevant coded data extracts and considering how different codes might combine into overarching themes. For instance, codes related to "packaging difficulty," "hard to open," and "needs better design" might coalesce into a theme about "packaging usability issues."

4. Review potential themes

Refine your themes by checking if they work in relation to both the coded extracts and the entire dataset. This two-level review ensures themes accurately represent your data. Some themes might collapse into each other, while others might need to be broken down or discarded.

5. Define and name themes

Clearly define what each theme is about and determine what aspect of the data each theme captures. Create concise, evocative names that immediately tell the reader what the theme is about. For example, "The Convenience Paradox" might capture how consumers value convenience but find your product's convenience features counterintuitively difficult to use.

6. Produce the report

Finally, transform your analysis into a coherent narrative that tells the story of your data. Select compelling examples that capture the essence of each theme and relate your analysis back to your research question and business objectives.

How to code qualitative data for thematic analysis

Effective coding is the bridge between raw consumer feedback and meaningful insights. This critical step in thematic analysis transforms unstructured data into manageable, meaningful segments that lead to powerful themes.

The foundation of good coding

Coding is essentially the process of labeling segments of data that are potentially relevant to your research question. For CPG professionals, this might mean identifying patterns in how consumers describe product experiences, usage occasions, or emotional responses. Good coding requires both systematic thinking and creative interpretation.

When approaching coding for consumer feedback data, consider these fundamental techniques. Open coding begins with a broad approach, creating codes for anything that might be relevant. In-vivo coding uses participants' own language as codes to preserve authentic consumer voice. Descriptive coding summarizes the primary topic of a data excerpt with a word or short phrase, while process coding uses gerunds ("-ing" words) to capture actions or experiences consumers describe.

Practical coding strategies

Start by establishing a clear coding framework that aligns with your research objectives. Are you interested in consumer pain points? Product benefits? Usage contexts? This focus will guide your coding decisions.

Create a coding structure that allows for primary codes (major categories), secondary codes (subcategories), and memos (notes about your thought process). This hierarchical approach keeps your analysis organized while maintaining flexibility.

For example, when analyzing feedback about a skincare product:

Primary Code	Secondary Codes	Example Data Extract
Texture Experience	Absorption, Stickiness, Smoothness	"It feels lightweight but still takes forever to sink in"
Sensory Reactions	Scent, Irritation, Cooling	"The fragrance is subtle but pleasant"
Usage Context	Morning Routine, Travel, Special Occasion	"I only use this when my skin feels extra dry"

Avoiding common coding pitfalls

Watch out for code proliferation—creating too many unique codes makes pattern identification difficult, so regularly review and consolidate similar codes. On the flip side, under-coding by being too general misses important nuances; look for distinctions within seemingly similar comments. Confirmation bias can lead you to see only what you expect to see, so challenge your assumptions by actively looking for contradictory evidence. Finally, inconsistent application means coding similar content differently over time; develop a codebook and refer to it consistently to maintain rigor.

Using software effectively

While manual coding works for smaller datasets, software tools like NVivo, ATLAS.ti, or even Microsoft Excel can significantly improve efficiency and rigor for larger projects. These tools allow you to organize and retrieve coded segments quickly, visualize relationships between codes, track coding frequency and distribution, and collaborate with team members through shared coding frameworks.

Software like Highlight makes this process accessible to lay people with no knowledge of coding. Use features like Instant Reports by Highlight AI to generate presentation-ready slides, summarize qualitative responses, or use AI-powered Scorecards to examine your data meaningfully and at speed.

Remember that software is a tool to support your analysis, not replace your critical thinking. The quality of your coding depends on your understanding of the consumer context and research objectives.

How Highlight can help

At Highlight, we understand the importance of deep, meaningful insights. Our commitment to high-quality data is evident in our rigorous participant screening—with only 1-2% junk data compared to the industry standard of 30%—and a community that's carefully vetted to ensure thoughtful, reliable feedback.

As a product testing software company, we're dedicated to providing CPG brands with the insights they need to improve their products. Our platform is designed to complement the thematic analysis process by gathering high-quality consumer feedback and facilitating the identification of patterns and themes that can inform product development and marketing strategies. By leveraging our software—which consistently delivers 90%+ survey completion rates and enables engagement with even the most niche audiences (as low as 3% IR audiences)—brands can unlock a deeper understanding of their consumers and create more effective, consumer-centric products and experiences.

While traditional product testing methods can take months to deliver actionable data, Highlight delivers product insights in as little as three weeks—from recruit to insights in-hand—ensuring you stay agile in an ever-changing market.

Final thoughts

Thematic analysis is more than just a research method—it's a powerful lens for understanding complex human experiences and perspectives. By systematically identifying, analyzing, and reporting patterns within qualitative data, researchers can uncover rich, nuanced insights that might otherwise remain hidden.

Like a skilled detective piecing together clues, researchers using thematic analysis can transform raw information into meaningful narratives. Whether you're exploring consumer behaviors, academic research, or organizational dynamics, this approach offers a structured yet creative way to make sense of qualitative data.