What is Thematic Analysis?
Thematic analysis is a highly adaptable framework for qualitative data analysis and can be used to improve customer experience. The analysis and implementation of customer data, which may be generated through various consumer research approaches, ensures that CX is better aligned with customer demands.
As per Braun and Clarke, “Thematic analysis is a method for analyzing qualitative data that entails searching across a data set to identify, analyze, and report repeated patterns.”
Let’s delve deeper into the details.
Thematic Analysis in Qualitative Research
A qualitative research method known as thematic analysis organizes, delves into, and thoroughly examines customer data. It involves more than only calculating the number of words or phrases in a text and entirely new things.
The thematic analysis involves researching a particular customer facet while utilizing a specialized and general understanding of the text to theme development. The topics in the given raw data are examined using a predetermined pattern and process.
What are the Advantages of Thematic Analysis?
Here, a thorough description of thematic analysis research using qualitative techniques for better CX is provided.
1. Analysis of Customer Insights Through Different Theories
A correct conclusion can be drawn from research using various approaches and theories while thematically analyzing customer data. We only get inflexible results when we evaluate data without using an analytical technique.
The validity of theme-based analytical research consistently offers a proper grasp of customer preferences and pain issues. One of the main benefits that the thematic analysis reveals is this.
2. Easy Analysis of Large Datasets
Customer data is lengthy. So, it is impossible to go line by line for its analysis. A thematic analysis that makes them simple to understand is the most excellent method for teaching them in such a situation.
It's crucial to comprehend the survey's main points, for instance, if you're studying a sizable survey on a new launch. Theme-based analysis works well for these other forms of data since it can save a significant amount of time while still producing quality research results.
3. Ability to Collaborate
When the notion of thematic analysis is used in qualitative methods, it is possible to work in a team. This is due to the importance of individual viewpoints when conducting this kind of research.
The result will be more appropriate the more people engage in consumer insights study. This is an additional benefit that the analysis research may take advantage of.
4. Application of Theoretical and Personal Knowledge
In other sorts of study, it's crucial to adhere to a predetermined set of guidelines to arrive at the research's conclusion. However, the situation changes when it comes to theme analysis.
The theoretical foundation of the research is vital in this type of study, but personal experiences on the subject are also valued. The optimal study outcome incorporates all facets of the research issue.
5. Development of Several Themes
The thematic analysis aids CX specialists in identifying various themes in the materials provided for research. If one cannot identify all the themes in consumer data, reading or observing it is meaningless.
Applying personal experiences and various qualitative methods to the data gathered for research purposes can help develop and clarify these themes. This is the most suitable method for conducting qualitative research on any text.
6. Both Inductive and Deductive Approaches to Research
The most important aspect of conducting a consumer data analysis is using both deductive and inductive research methods. Deductive research is based on a predetermined research approach, as opposed to inductive study, which utilizes individual experience-based points.
As a result, thematic analysis is the most effective method for taking a comprehensive view of any research data.
What are the Types of Thematic Analysis?
There are four types of thematic analysis:
- Inductive
- Deductive
- Semantic
- Latent
To further comprehend each of the four categories, let's examine them separately:
1. Inductive
The goal of inductive thematic analysis is to construct a theory. When creating a hypothesis from scratch and there is little knowledge already accessible on a subject, this method is employed. Although this strategy is always valid, it is challenging to demonstrate that an observation obtained using this approach is accurate. The three stages of the inductive process are as follows:
- Observe: A website experiences heavy traffic.
- Look for trends: The website is heavily used from 9 am to 6 pm.
- Craft a grounded theory: The website experiences high traffic during business hours.
2. Deductive
Testing an established hypothesis is the primary goal of deductive thematic analysis. It entirely depends on the inductive method because you build on an existing hypothesis from the beginning. You continue to develop the hypothesis and draw a conclusion from it. Depending on how truthful inductive theory is, deductive theory's veracity will vary. There are four steps to the deductive approach:
- Start with a theory: the website has heavy traffic during business hours.
- Formulate a hypothesis: typically, all websites are busy during business hours.
- Collect data to study hypotheses: Observe all the websites during working hours daily.
- Analyze the findings (do the facts collected refute or support the hypothesis?): since all the websites are busy during working hours, support a hypothesis.
3. Semantic
The specifics of the data are the focus of semantic thematic analysis. We investigate the information because it serves specific auxiliary meanings and purposes. This will make it easier to develop insights and knowledge about how the data was used.
4. Latent
Beyond the semantics of the data, the latent thematic analysis concentrates further on the underlying meanings, concepts, and presumptions that we initially formed with the semantic approach.
Before selecting the ideal strategy for your research, examine your study's requirements and the method(es) or a mix of approaches that best correspond with your data.
Why Do Companies Use Thematic Analysis?
The insights gained from a thematic analysis of consumer wants will be used to inform judgments about how people will engage with products and decisions about product content, information architecture, usability design, and other things. Thus companies use thematic analysis for the following:
1. Operation Cost Reduction
You may reduce financial risk, guarantee data security, and take other activities that could help you save money with effective thematic analysis. Organizations also use thematic analysis to identify tasks that consume more funding than they ought to and others that do. This contributes to cost reduction, particularly in operations and production, and ultimately substitutes technology for manual tasks.
2. Future Trend Prediction
Through thematic analysis, businesses may anticipate upcoming trends and advances. Organizations can create future-focused products and services using thematic analysis methods and maintain market leadership. These businesses can generate demand for these offerings and increase their market share by using effective marketing. They can even secure patents for cutting-edge inventions to preserve an edge over rivals and increase earnings.
3. Product Performance Monitoring
Thematic analysis is additionally used to monitor consumer attitudes toward goods and services. It can help you figure out why sales are low, what people buy and why they buy it, how much they spend on it, how to promote your items more effectively, and many other things. Investigating consumer behavior aids businesses in making financial choices, such as adjusting product prices or identifying a target market.
4. Security Strengthening
Businesses utilize theme analysis to evaluate previous security lapses and identify the flaws that allowed them to happen. The results of theme analysis assist IT professionals in parsing, processing, and visualizing audit logs to identify the source and trajectory of security breaches. Analytical models recognizing unique or deviant behavioral patterns can also stop future attacks. These models can be configured with monitoring and alerting systems to detect attempted breaches and warn security experts.
5. Risk Management
Risks in business can include employee or customer theft, legal responsibility, excessive inventory of goods, and more. The thematic analysis aids in risk management and risk prevention for businesses. A retail chain, for instance, may use a propensity model to identify which outlets are most likely to experience theft. This would aid in determining whether to relocate the store or beef up security.
6. Decision-Making Improvement
Thematic analysis is a tool that organizations can use to stop financial losses. If a change is made, this analysis can predict future customer behavior and offer advice on how to respond to optimize profit. Let's imagine, for example, that a business wants to raise the cost of its goods. They can create a model to establish whether this change might impact customer demand. Testing can verify the model's conclusions. This would stop poor financial choices.
7. Business Performance Enhancement
Data regarding the supply chain can be gathered and analyzed to identify manufacturing bottlenecks, delays, and potential future issues. Thematic analysis can assist in determining the best supply for an enterprise's products regarding inventory levels. This makes it simple for firms to recognize problems and find speedy solutions.
8. Web Search Result Evaluation
The finest search results can be produced using thematic analysis to organize the material. Thematic analysis analyzes enormous amounts of data given by various pages and organizes it into groups based on keywords while storing online data. It can also assist in determining the relevancy of websites within each group.
9. Marketing Improvement
You can market to your target more successfully when you understand them better. Additionally, the thematic analysis offers businesses helpful information about the effectiveness of their efforts so they may make adjustments for the best results.
Businesses learn which audience segments are most likely to engage with and respond to a campaign. They can use this data to manually or automatically change your targeting criteria, or they can utilize it to create unique messaging and content analysis for various target audiences. More conversions and less wasteful ad spending are the outcomes of better targeting.
10. Customer Service Personalization
By giving you a deeper understanding of your clients, thematic analysis enables you to meet their demands through more individualized service and foster stronger bonds.
Your data can provide details about your clients' preferences for communication, as well as information about their hobbies, worries, and more. Your whole customer support staff and your sales and marketing teams will be on the same page if you have a central location for this data.
Example of Thematic Analysis
The theme analysis employed for a company that offers telehealth goods, services, and analytics is described in the case study of CX research that follows.
How can we enhance the user experience of our analytics dashboards? The client questioned.
Customers were canceling their subscriptions to the program, and the dashboards were not being used. Thematic analysis research was used to determine what consumers needed, wanted, and were motivated by to reduce churn and enhance overall CX.
Becoming Familiar with the Customer Research Data
All the input was collected after the client interviews and put on a different tab in a spreadsheet. Three columns make up the spreadsheet:
- User Name
- Codes
- Themes
The qualitative data was organized into rows and placed in the "User Name" column. To do this, the user comment was arranged in rows by topic, sentence, or a naturally occurring pause, preserving the spirit of the feedback. There were also nonverbal observations made.
Generating Initial Codes
The spreadsheet's "codes" column was filled with the first codes. Please note that these codes are merely suggestions based on the input received and the desired outcome for the project.
The phrase "analytics tale" always made the most sense whenever a user talked about something they wanted to see on their analytics dashboard because every data on a dashboard had a purpose.
Coding may also be arbitrary. For instance, it felt natural to assign the user remark "juggling so many balls" a "time management" classification because it referred to being highly busy. Additionally, it can have been "busy" or "overwhelmed."
Reviewing Themes
One of the most challenging phases of thematic analysis is this. Each user's codes were arranged side by side on a different spreadsheet so they could be displayed collectively. When analyzing codes, remember that only some terms will be precisely the same; instead, search for words and concepts.
We are currently searching for potential themes that might be gleaned from the codes. For instance, "customization" was used to describe "enhancements," "changes," and "personalization" of analytics reports.
A user research project may only generate a relatively small amount of data. Final themes may be produced at this point if this occurs.
Defining Themes
The final section of the thematic analysis consisted of a condensed list of themes that complemented the primary business goal:
- Customization – Enhancements/opportunities
- Data Usage – Current activities
- Data Stories – Opportunities
- Current Product – Issues, etc.
This might not sound spectacular initially, but remember that it was chosen from thousands of pieces of qualitative feedback gathered over hours of interviews.
We discovered that rather than receiving a "one-size-fits-all" report, clients prefer the option of customizing their analytics dashboards. Additionally, we found numerous narratives that users hoped to convey through their dashboards, including "How is my telehealth program performing?"
This provided the business with valuable knowledge and a clear set of goals to assist the data team in turning the underperforming dashboards into a product that users would want to continue using.
Contrast this with a lengthy spreadsheet of notes gathered during the user interviews without identifying user wants and behaviors. You can see why showing the executive team (and the data team) the final theme was a significant gain.
CX Deliverable
The CX deliverable went beyond a brief report. The qualitative report includes the following, based on the themes:
- An enlarged version of the themes was supplied, along with each theme's corresponding qualitative evaluation.
- Customer stories - Based on the qualitative comments from the interviews, a group of user stories were developed. The importance of each story was assigned a priority rating.
- Wireframes - A rough draft of the analytics dashboard's wireframe was made, emphasizing customization, resolving existing problems, and data consumption.
How to Do Thematic Analysis?
Credible research relies on accurate qualitative analysis. A collection of themes that more accurately reflect the demands, motivations, and behaviors of customers is produced through the systematic process of reflexive thematic analysis:
- Analyzing the CX study findings
- Creating initial codes
- Looking for themes
- Reviewing themes
- Defining themes
- Compiling a CX deliverable
Before beginning the process of thematic analysis, it is a good idea to have a clear understanding of the questions and objectives that are being pursued because the results of qualitative data analysis interpretation can be arbitrary if no clear outcomes have been established.
Step 1: Explore the CX Research Data
Card sorting, daily journals, and client interviews are methods used to collect qualitative CX research data. The objective at this early stage is to gain preliminary insights by reading and rereading the data, not to draw any judgments.
Transcribing spoken data is a good idea. If the data has already been transcribed, divide it into more manageable portions. Taking notes is suggested in either scenario. Practicing keeping an open mind, remaining impartial, and restraining the impulse to formulate specific ideas when in the exploration stage is wise.
By the time this phase is over, the data should have attained a comfortable level of familiarity, and some meta-suggestions should be recorded. Reread it if anything needs to be clarified, but don't go any further.
Step 2: Generate Initial Codes
The objective of this stage is to meaningfully and methodically arrange the data. Highlighting or side notes might be utilized if the data is being evaluated manually (without research software).
What is being highlighted in reality? What are we trying to find? Highlighted client data sections relevant to the project's research topics are given codes.
Because the customer researcher must always keep the project's questions and objectives in mind, coding is a rather complicated procedure. Transcribing all code samples onto a spreadsheet is an excellent idea because it will aid in the next step, which involves looking for themes.
Step 3: Looking for Themes
The next stage of a thematic analysis involves searching through the codes and concepts produced in the previous step for probable themes.
A theme is a sequence of codes that appears repeatedly and encapsulates an essential aspect of the original research subject. For instance, we might highlight several codes like HBO, Netflix, and Hulu. Since video streaming is the subject of the research topic, we may choose the theme of "major streaming services" or, more generally, "services" as our focus.
Most effort should be spent on this process stage because identifying themes is the foundation of thematic analysis. The themes that complement the research questions will be the most accurate.
Step 4: Reviewing Themes
Once the initial themes have been extracted, the next step is to review each one and make sure it fits with the overall meaning of the data. It can be easy to "accept" all the themes at this point and go on to the next level.
As there are frequently themes or notions that should have been noticed the first time around, a more thorough analysis is advised. During the topic review process, the following inquiries might be used as a guide:
- Are the themes consistent with the setting of the research question?
- Are the topics specific or overly broad?
- Any themes that cross over?
- Were any themes overlooked?
Step 5: Defining Themes
The definitive collection of themes should be documented following a comprehensive evaluation. A thematic map that illustrates the connections between the themes and how they complement the main narrative may be helpful.
Step 6: CX Deliverable
The last phase of the thematic analysis approach is the CX deliverable. Consider the audience when conducting the analysis. A thematic analysis report should be brief, logical, and non-repetitive regardless of the audience and create a compelling tale to support the findings. Additionally, it's critical to offer concluding advice and support it with examples drawn from consumer data.
The original qualitative data, codes, and resulting themes are frequently included so the customer may understand how the CX researcher came to their conclusions. It also gives the work more credibility.
Final Thoughts
Thematic analysis can be utilized repeatedly during an iterative CX design process. For instance, a prototype was developed based on the results mentioned above. The prototype was subsequently used for a fresh round of client interviews, yielding further qualitative information, and a second theme analysis was carried out to fine-tune the prototype.
Offering more insight into users' needs, motives, and behaviors, thematic analysis, and a qualitative examination of data can enhance CX and produce better customer experiences.