Customer feedback teams today are drowning in data from product reviews and support tickets to annual NPS surveys. Making sense of it all requires powerful qualitative methods like thematic analysis to uncover patterns.

But counts and topics alone won’t tell you why customers feel the way they do.

Reflexive thematic analysis offers a way to uncover deeper meaning by embracing, not erasing, the analyst’s perspective. This method helps CX and insights teams move beyond surface summaries to interpret patterns that drive action.

In this article, we’ll

• cover why reflexivity matters,
• break down the six phases of analysis, and
• show how to avoid common pitfalls.

You’ll also see an example of how a team can uncover hidden VIP frustration and how reflexive analysis fits seamlessly into agile workflows.

Why Reflexivity Matters in CX Research

In customer experience (CX) research, it’s tempting to treat the analyst as a neutral bystander, but reflexive thematic analysis flips that notion. Your subjectivity isn’t just inevitable; it can actually be an insight engine rather than a source of error.

Braun and Clarke, originators of reflexive TA, argue that analysis is “a situated and interactive process, reflecting both the data and the positionality of the researcher”.

In other words, themes don’t simply emerge from data waiting to be discovered; they are actively co-created by you (the analyst) and the data.

This matters because customer feedback often needs interpretation. For example, ten customers might all mention “price,” but why they mention it could vary from feeling something is unfair to thinking it’s great value. Your background—be it in support, product, or industry knowledge—helps you interpret such nuances.

Reflexivity means being aware of your perspectives throughout the analysis. Instead of trying to erase your influence, you leverage it thoughtfully:

• question your assumptions,
• consider alternative explanations, and
• acknowledge how your viewpoint shapes the insights.

The result is a richer understanding of customer feedback that is both systematic and deeply contextual.

The Six Phases of Reflexive Thematic Analysis

Reflexive thematic analysis still follows a structured process to ensure rigor, but it’s a flexible structure. There are six key phases, originally outlined by Braun & Clarke, that guide you from raw feedback to insightful themes.

Let’s talk about each phase and how reflexivity plays a role in each:

1. Familiarization with the Data

Begin by immersing yourself in the feedback. Read through customer comments, support call transcripts, and survey responses multiple times.

During this phase, take notes on initial impressions. What stands out? How do certain remarks make you feel or what do they remind you of?

This deep familiarization ensures you understand the context of feedback before jumping into coding.

2. Generating Initial Codes

Next, systematically go through the data and tag segments with initial codes.

A code is a brief label describing an aspect of the feedback (e.g., “login difficulty” or “friendly service”). In reflexive TA, coding is often open and evolving; you don’t start with a rigid codebook.

Allow your codes to develop and change as you identify new insights. For example, you might start coding comments about “slow website” and later realize they relate to a broader notion of reliability.

Feel free to modify or merge codes as your understanding deepens. This flexible coding contrasts with fixed codebook approaches and lets your growing insight shape the analysis.

3. Constructing Initial Themes

Once you have a set of codes, look for patterns among them to form initial themes.

A theme is a broader concept that captures something important about the data.

In this phase, you’re clustering related codes and thinking about the story they tell. Reflexivity is key here: ask yourself why you see those patterns. For instance, if several codes (like “long wait time” and “confusing menu”) seem to cluster under a potential theme of “onboarding friction,” consider how your perspective identifies that connection. Are you focusing on it because of past experience with a similar issue?

Ensuring you understand your own rationale helps solidify that the theme has a meaningful central concept, not just a topic label.

4. Reviewing and Refining Themes

With initial themes in hand, circle back to the data and verify that each theme is truly supported and distinctive. This often means rereading feedback for each theme and checking if the theme accurately reflects those data excerpts. It’s common to merge, split, or even drop themes at this stage.

As you review, be reflexive; remain open to the idea that you might have missed something or over-interpreted due to your expectations. Perhaps two themes you created are actually facets of a single larger theme, or one theme might be too broad (just a summary of a topic) and lacks a unifying meaning.

This iterative review ensures your themes are robust and grounded in what customers actually said.

5. Defining and Naming Themes

Now refine what each theme means and give it a clear, concise name. Write a brief definition or description for each theme, capturing the “essence” of what’s going on.

Instead of calling a theme “Product Issues,” for example, you might define it more specifically as “Reliability gaps undermining trust” if that’s the core issue tying feedback together. This is where you articulate the central organizing concept of the theme.

Being reflexive here means articulating why that concept matters. Ensure that for each theme you can answer: “What question or concern from our customers does this theme address at a deeper level?”

By defining themes in this way, you avoid vagueness and guarantee that each theme tells a distinct story about the customer experience.

6. Producing the Report (Narrative-Rich Reporting)

Finally, you’ll produce a report or share-out of the analysis. This could be a presentation to stakeholders or a written report.

In reflexive thematic analysis, reporting isn’t just listing themes; it’s storytelling with evidence.

For each theme, include compelling customer quotes or examples that illustrate the theme, and explain the interpretation (the “why”) behind the theme. A narrative-rich report might say:

“Customers described feeling ‘left in the dark’ (Theme: Communication Breakdowns) — for example, one respondent noted, ‘I never know if my order went through or not.’ This suggests an underlying anxiety about process transparency, not just a delay notification issue.”

Here you are transparent about how you, as the researcher, interpret the quotes.

Good reporting also includes a bit of your reflexive commentary: you might mention if a theme was unexpected or how it answered a question the team had. The result is a set of themes that are not only data-backed and insightful, but also actionable. Each theme should ideally point to a decision or improvement (the “so what?” for your organization).

It’s worth noting that these phases are not rigid steps you can only do once. Reflexive analysis is an iterative process; you might recode some data after defining themes or refine a theme even while writing the report. That flexibility is a strength, allowing new insights to emerge at any stage. The key is to document your changes and reasoning as you go, which brings us to the importance of memoing and positionality.

Memoing and Positionality: Documenting Your Perspective

In reflexive thematic analysis, memoing involves writing down thoughts, questions, and emotional reactions as you work through the data. This reflection helps you track how your perspective influences interpretation and keeps your process transparent.

For example, if you notice frustration in feedback about “data limits,” and you personally hate data caps, you might jot down: “Need to confirm if I’m projecting or if this is a trend.” That’s reflexivity in action.

Positionality means openly stating your standpoint, like being a UX researcher with past support experience. This context gives your interpretation more depth, not less credibility.

Practical tips:

• Start early and keep going: Begin memoing in the familiarization phase and continue throughout. Capture your assumptions and shifts in understanding.
• Ask good questions: Prompts like “Why does this quote stand out?” or “Am I overlooking something?” help expose blind spots.
• Use a memo template: Track date, data snippet, your reaction, and possible influence of your background.
• Create an audit trail: Memos document how and why themes evolved, making your process transparent to others.

Done well, memoing and positionality explain your thinking, but more importantly, build trust in your insights.

Avoiding “Topic-Summary Creep” in Theme Development

A common pitfall in thematic analysis is mistaking a broad topic for a theme. Braun & Clarke call these “topic summaries”—lists of related feedback that lack a unifying insight. For example, “Mobile App Feedback” may cover various issues, but doesn’t explain what ties them together or why they matter.

Reflexive thematic analysis aims for meaning-based themes that interpret patterns, not just catalog comments. If you label a theme “Pricing,” ask: what’s the underlying issue? Perhaps it’s “Perceived unfairness in pricing,” a sharper, more actionable concept.

To avoid topic-summary creep:

• Test whether your theme answers a why question.
• Avoid generic labels like “Pros and Cons.”
• Ask peers to review your theme clarity.
• Look for consistent emotional or functional undercurrents across the data.

How to Tell a Topic Summary from a Meaning-Based Theme

This practice is critical to producing valid, insight-rich analysis. As covered in our overview of thematic analysis advantages, well-defined themes enable better decision-making and clearer storytelling. When your themes have interpretive depth, they do more than reflect data—they reveal meaning that drives action.

Ensuring Auditability and Transparency

In reflexive thematic analysis, subjectivity is expected, but your process should still be transparent. Auditability means someone else can understand how you went from raw data to final themes and see that your interpretations are grounded.

A few ways to ensure that:

• Track your process: Keep a running log of key decisions, such as when codes were merged or themes refined.
• Use direct quotes: Show the customer’s words behind each theme. For instance, a theme like “Feeling undervalued” is made real by a quote like, “I spend so much here, but I feel treated like any other customer.”
• Review themes collaboratively: Ask colleagues to challenge or refine your theme definitions—it keeps blind spots in check.
• Refer to best practices: Use frameworks and checklists to document your reflexivity, coding choices, and reasoning.

Observing how thematic analysis is applied in real-world scenarios can provide valuable context. For instance, examining thematic analysis examples across different industries demonstrates how transparency and reflexivity are maintained throughout the analytical process. These examples highlight the importance of a systematic approach to ensure that insights are both credible and actionable.

Mini-Case: Uncovering “VIP Frustration” in Feedback

Picture this: an insights manager at a major e-commerce company notices steady NPS and CSAT scores but a spike in churn, specifically among VIP customers. Traditional metrics aren’t revealing much, so the team turns to reflexive thematic analysis for deeper understanding.

Using open-ended feedback from support calls, chat logs, and surveys, they begin the qualitative data analysis process. Early memos note subtle expressions of disappointment, such as: “I expected better treatment after all I’ve spent.” As they code, themes start forming—not just around service issues or late deliveries, but a broader emotional pattern of feeling overlooked.

Multiple small issues—missed perks, delayed responses, lackluster support—tie together into a theme: “VIP Frustration: Loyal customers feeling undervalued.” This theme wouldn’t emerge from metrics or surface categorization alone. It reflects a perceived gap between customer investment and recognition.

On review, the team validates the theme with strong supporting quotes and decides to act, designing a targeted loyalty initiative. Without this kind of customer review analysis, the emotional driver behind churn might’ve stayed invisible.

With Thematic’s support, the team was able to code quickly, surface patterns, and apply a reflexive lens—all while keeping human interpretation central to the insights.

Integrating Reflexive Analysis into Agile Sprints

Fitting reflexive thematic analysis into fast-moving agile workflows is easier than it seems. When paired with the right tools and rhythm, you can uncover deep insights without slowing velocity.

1. Make it a regular cadence

Add short, recurring qualitative reviews at the end of each sprint. Feedback from support tickets, chat logs, or user tests can be reviewed as a team to catch patterns early. Regular reflection avoids trend blindness and keeps your insights timely.

2. Use AI to accelerate coding

Platforms like Thematic help teams start fast by using AI to theme qualitative data in bulk. The system surfaces clusters (e.g., “checkout issues”), which analysts then refine into deeper patterns like “UX friction.” This blend of automation and human interpretation maintains reflexivity while improving turnaround.

3. Link insights to agile work

Once a meaningful theme is defined—say, “VIPs feel overlooked”—create a Jira ticket or user story tied to it. Many teams use Thematic’s workflows to trigger action when specific patterns spike. It’s real-time text analytics feeding directly into backlog grooming.

4. Track changes with dashboards

Use dashboards to monitor theme frequency and sentiment analysis across sprints. This gives everyone from developers to executives a pulse on how user experience is evolving—and which pain points are persisting.

Embedding RTA in agile keeps product teams close to customer meaning not just customer metrics.

Summary and Next Steps

Reflexive thematic analysis helps CX and insights teams uncover the “why” behind customer feedback, revealing patterns of frustration, loyalty, and unmet expectations. You can turn open-ended feedback into actionable strategy by reflecting on your perspective, documenting decisions, and digging deeper than surface topics. When integrated into agile workflows, this approach balances speed with meaning, especially when supported by AI-powered tools.

Want to uncover emotional drivers like VIP churn in your customer feedback? Request a demo of Thematic and see how it brings reflexive analysis to life using your data.