Do you ever find yourself swimming in a sea of qualitative data, wondering where to even begin? That’s where thematic analysis jumps in like a trusty life raft. It’s like detective work for researchers, helping them to uncover hidden gems and insights within all that text or imagery.

Thematic analysis is a qualitative research method for identifying, analysing and interpreting patterns and themes within a dataset. It isn’t just about skimming the surface; it’s about diving deep into the heart of qualitative data.

Thematic analysis can be applied in various contexts. It is widely used in qualitative studies to understand participants’ perspectives and analyse written, visual or audio content, such as interviews, documents, images or videos. Thematic analysis can also help businesses understand consumer opinions and attitudes by analysing customer feedback, reviews or social media content.

Thematic analysis is a qualitative method of data analysis that involves identifying, analysing and reporting patterns in data to find themes. It is a common method used to analyse text, video and audio data.

This method is not a linear process but rather a dynamic journey where researchers continuously refine their understanding through iterations. Through embracing this flexibility, thematic analysis empowers researchers to delve deeper into the diverse perspectives and lived experiences of research participants, enriching their understanding of the participants’ stories.

Thematic analysis helps researchers extract meaningful insights from qualitative data. It provides a structured framework for organising and synthesising large volumes of qualitative data. It makes data more manageable and accessible for analysis. The insights gained from thematic analysis can inform decision-making processes in various aspects of business.

The goal of thematic analysis is to identify themes or patterns in the data that are important or interesting and to use these themes to address the research or say something about an issue. Thematic analysis is beneficial when you’re working with large bodies of data, such as open-ended survey responses, app review comments or social media posts. It is particularly useful when you’re looking for subjective information such as experiences and opinions. 

For example, you may want to conduct thematic analysis to understand the interviews that you conducted regarding the extent to which customers like or dislike the new product. Another example is that you can use thematic analysis to gather insights into what will affect employees’ motivation and productivity at work within your organisation.

Thematic analysis is great for uncovering underlying meanings and concepts. It is more suited to exploratory and interpretive research. Thematic analysis prioritises the voices and perspectives of participants. It is best for listening to your customers and audiences.

Other qualitative analyses, such as content and narrative analyses, focus on the content itself. They are more suitable for descriptive research. Content analysis is better for quantitative assessment and categorisation of the data. Narrative analysis emphasises understanding how individuals construct their storytelling.

Taking one example to understand the differences, let’s suppose that you would like to understand the customer feedback about your product and you have collected all your product reviews online. Thematic analysis can help you identify recurring themes, patterns and sentiments in the reviews. It answers questions such as “Do customers mostly like or dislike it?” and “What do customers like about it?”.

Content analysis of the same material will quantify the elements of the data. You might code for the frequency of keywords related to different features of the product (e.g. “easy to set up”, “durability”) and the overall rating given by customers (e.g. the number of stars). You can then summarise and visualise the quantified data to show the whole picture of customer reviews.

Narrative analysis will examine the structure and emotional tone of individual stories shared by customers. It is more about how each individual tells and constructs the story. 

Thematic analysis is a versatile method that can be applied to various types of data sources. It is particularly suitable for analysing interview data, whether from individual interviews or focus groups. Thematic analysis can analyse open-ended survey responses, where participants provide qualitative feedback in their own words. Another type of data that it can handle well is textual data, such as written documents, social media posts, online forums or news articles.

Related article: Qualitative vs. quantitative research: What’s the difference?

Here are some instances where you may use thematic analysis:

Inductive and deductive thematic approaches represent two distinct paths in qualitative research, each with its own set of strengths and implications.

Inductive thematic analysis starts with raw data and allows themes and patterns to emerge organically through the process of coding and analysis. It is a bottom-up approach. Researchers remain open to new ideas and insights as they engage deeply with the data.

The inductive approach offers flexibility to adapt to the nuances and complexities of the data. It allows for the discovery of unexpected themes and insights. By starting with the data itself, inductive analysis can provide a rich understanding of participants’ experiences and perspectives. However, the iterative nature of inductive analysis can be time-consuming. It requires careful and thorough engagement with the data.

For example, you may want to understand how consumers perceive sustainable packaging in the food industry, so you conduct interviews with consumers. Through the inductive thematic analysis, you immerse yourself in the interview transcripts and code the data without predefined categories.

With this approach, themes may emerge from the transcripts. You may conclude that ‘Environmental Concerns’, ‘Perceived Benefits of Sustainable Packaging’ and ‘Barriers to Adoption’ are the common themes around sustainable packaging. You may also uncover insights that you did not expect to find, such as consumers’ motivations for choosing sustainable packaging options and the challenges that they face in terms of understanding such products.

By contrast, deductive thematic analysis begins with a predefined set of codes or themes derived from existing frameworks or research questions. It is a top-down approach. Researchers start with a hypothesis or framework and seek to confirm or refute it through the analysis of data. 

Deductive thematic analysis allows researchers to test hypotheses. By starting with predefined codes or themes, deductive analysis can be more efficient than inductive analysis, particularly when the research question is well defined. However, there’s a risk of bias in deductive analysis. Researchers may overlook or misinterpret data that doesn’t fit neatly into the predefined framework.

Let’s suppose that you want to know how brand loyalty influences consumer purchasing behaviour in the mobile phone market. You may look into existing market research and hypothesise that brand loyalty can promote mobile phone purchases. You use the deductive approach and analyse online reviews and social media comments. You will specifically look for textual mentions and descriptions of brand loyalty and purchasing behaviour.

The analysis then confirms the influence of brand loyalty on purchasing behaviour, with themes emerging around ‘Brand Reputation’, ‘Perceived Value’ and ‘Customer Loyalty Programmes’.

Let’s walk through a step-by-step thematic analysis guide.

Before diving into analysis, familiarise yourself with the data by reading through it multiple times. This will help you gain a comprehensive understanding of the content and context.

Imagine that you’re conducting a study on customer feedback regarding a new online shopping platform. Take time to read through customer reviews, comments and feedback forms to familiarise yourself with the range of opinions and experiences expressed by users.

Start by systematically coding the data. Label segments of text that represent meaningful themes or patterns. Begin with open coding. Allow codes to emerge organically from the data.

As you read through the customer feedback, you might start coding phrases such as “user-friendly interface”, “fast delivery”, “poor customer service” or “wide product selection” in order to capture different aspects of the online shopping experience.

Once you’ve generated initial codes, look for patterns and connections among them to identify potential themes or overarching concepts. Group related codes together to form preliminary themes.

You notice that several codes relate to the website’s usability, delivery speed and customer service quality. These codes could be grouped under a broader theme of ‘User Experience’.

Review the identified themes and their corresponding codes. Ensure that they accurately reflect the data. Refine or combine themes as needed to create a coherent and comprehensive thematic framework.

Upon review, you find that some codes related to customer service could fit under the broader theme of ‘Customer Support’ alongside other codes related to return policies and enquiries.

Define each theme by clearly articulating its underlying concept or idea. Provide descriptive names for each theme that succinctly capture its essence.

You define the theme of ‘User Experience’ to encompass feedback related to website navigation, product search functionality and overall satisfaction with the platform’s usability.

Document and report your findings. Organise them according to the identified themes. Provide illustrative quotes or examples from the data to support each theme and offer deeper insights.

In your research report, present your findings under thematic headings such as ‘User Experience’, ‘Product Quality’ and ‘Customer Support’. Then provide quotes from customers to highlight key points within each theme.

Although thematic analysis is a valuable qualitative research method, it comes with its own set of challenges.

Interpretation of data in thematic analysis can be subjective, as researchers may bring their own biases or perspectives to the analysis process. This subjectivity can influence the identification and interpretation of themes. Therefore, inconsistent or biased findings may arise.

Developing meaningful and relevant codes that accurately capture the data can be challenging. Researchers must strike a balance between creating codes that are too specific and too broad. Overly specific coding can lead to fragmentation of the data. Being too broad, on the other hand, may result in important nuances within the data being overlooked.

Additionally, thematic analysis often involves analysing large volumes of qualitative data, which can be overwhelming and time-consuming. Researchers may struggle to manage and analyse the sheer volume of data. They may experience fatigue or burnout during the analysis process.

Communicating the findings of thematic analysis in a clear and meaningful manner can be challenging too. Researchers must navigate the tension between providing rich data and maintaining brevity in their reporting. Ensuring that the findings are not overgeneralised requires careful attention to detail and reflexivity.

SurveyMonkey can facilitate thematic analysis in your market research. SurveyMonkey can be used to collect qualitative data via open-ended survey questions. They allow researchers to gather textual responses from participants. 

SurveyMonkey offers a few tools for analysing open-ended questions. Sentiment Analysis uses machine learning and natural language processing (NLP) to categorise responses as positive, neutral or negative. Word Cloud counts frequently used words in text responses and presents them visually. You can also tag your responses to uncover more specific information. You can assign multiple tags to the same response.

SurveyMonkey provides tools for organising and managing qualitative data collected via surveys. Researchers can export textual responses from SurveyMonkey in formats compatible with qualitative analysis software, such as NVivo, MAXQDA, or Dedoose or manual coding processes.