You’ve seen that AI can automatically identify themes in customer feedback.
The technology works. But a common concern remains: what if the AI's output doesn’t match your business terminology? What if you need more control?
This is where AI theming tools prove their real value.
The best platforms give you flexibility to customize themes, handle complex multi-topic feedback, track how themes evolve, and connect themes to the metrics that matter.
Atlassian processes 60,000 monthly feedback items across their product suite.
Their team modifies theme structures on the fly, and the AI learns from these changes. As their insights team noted: "The amazing thing about teaching the model once is that it learns to think like you."
The result? Theme structures that match your business language, insights you can trust, and the ability to catch issues before they escalate.
Here’s how you can take control of AI-generated themes while keeping the speed that makes automation worthwhile.
With the right platform, yes. You can merge, rename, and restructure AI-generated themes to match your business terminology.
Not all solutions offer this flexibility.
Some trap you in black-box AI where you only see outputs, others require specialists to train custom models, and some force manual rule-building.
Thematic's Theme Editor takes a different approach. You're not locked into the AI's initial output. Rather, it works like a file and folder system.
Drag and drop to reorganize. Merge similar concepts. Delete themes that add noise.
The interface is visual and intuitive. No coding required.
Here’s what you can do:
Your changes train the AI. When you merge or rename themes, the system learns and applies your decisions to future analysis.
Your edits teach the AI your business preferences and terminology, so future feedback gets categorized using your validated framework. You get automation speed with human precision.
AI feedback tools handle theme customization differently, and the right fit depends on your team's needs.
Some platforms prioritize simplicity with pre-built AI models. You get fast results, though theme structures are fixed.
Others offer custom model training for organizations that need highly tailored outputs. This works well for teams with data science resources and longer implementation timelines.
Rule-based tools give you precise control through keyword matching. They're effective for predictable feedback patterns, though they require ongoing maintenance as customer language evolves.
Thematic's Theme Editor is designed for teams that want flexibility without technical overhead. The drag-and-drop interface lets you restructure themes yourself, and your edits train the AI. When you merge "delivery issues" and "shipping problems," the system applies that decision to future feedback automatically. You're not starting from scratch each time or waiting for a vendor to retrain a model.
Knowing when to intervene saves time and improves results. The AI typically achieves 80% or higher accuracy out of the box.
Here are specific signals to watch for.
Signs you should customize:
Signs you can trust the output:
Once you’ve decided to customize, sort themes by frequency first, then review the most common ones. This follows theme editing best practices and ensures your effort has maximum impact.
Mitre 10, a hardware retailer processing 20,000 customer comments per month across 84 stores, used this approach.
They connected VoC insights directly to their OKRs and discovered which themes had the biggest impact on NPS. As a result, they were able to make data-driven decisions instead of gut-feel prioritization.
In Thematic, AI tags the response to multiple themes simultaneously.
This is called multi-label assignment, and not all platforms support it.
A review mentioning both "pricing" and "customer service" gets tagged to both themes, not forced into one.
Take note that real customer feedback is rarely about just one thing. A single response might praise product quality, complain about shipping, and ask about a feature. Forcing that into one category loses two-thirds of the insight.
Single-label systems force each piece of feedback into one category. This creates a distorted picture.
An issue that appears alongside other topics gets underreported. It loses to the "primary" theme every time, even when it matters just as much.
Over time, this blind spot grows. You miss patterns that only become visible when you track co-occurring themes.
Multi-label assignment solves this.
Each comment can be tagged by more than one theme, but never the same theme twice. This is how Thematic's theme creation ensures complex feedback is fully captured.
At scale, this accuracy compounds. When analyzing thousands of responses, even small miscategorization rates add up fast.
Consider this example feedback:
"I love the product but the shipping took forever and customer service was unhelpful when I called."
A single-label system forces this into ONE category. If tagged as "Shipping," you completely lose the product quality praise and the customer service complaint.
A multi-label system tags to ALL relevant themes:
You get the complete picture and can route insights to the right teams.
Yes. AI theming tools like Thematic track how themes emerge, grow, or decline over time. This helps you spot emerging issues before they become widespread. It also lets you measure the impact of changes you’ve made.
Static snapshots tell you what people said last month. Trend analysis tells you what's getting worse, what's getting better, and what new issues are surfacing.
This transforms your feedback program from reactive to proactive. You stop responding to crises and start preventing them.
Trend tracking reveals theme movement by volume, sentiment, or impact on CX metrics. You see whether an issue is a one-time spike or a sustained trend that demands attention.
Automated trend tracking follows a consistent process:
In Thematic, emerging issue detection catches problems at 0.5% mention rate before they become crises.
Without automated detection, most teams don’t notice problems until they hit 5% or 10% mention rate. By then, damage is done.
New themes emerge automatically as fresh feedback arrives. You don’t miss unexpected issues that traditional surveys might never think to ask about.
This early detection capability proved critical for Greyhound.
They used this approach to spot accelerating issues in real time.
They went from waiting 3 to 4 weeks for reports to identifying problems within 10 minutes. That 99.7% reduction in analysis time let them fix issues before revenue impact.
As Matthew Schoolfield, Senior Customer Insights Analyst at Greyhound, explained:
"The over-time feature is always interesting to me, being able to view different themes over time and how they've improved or declined."
The difference between knowing something two weeks late and knowing it today can be millions in prevented churn.
You use trend data by comparing 30, 60, and 90-day windows to spot patterns. These timeframes can be adjusted based on your feedback volume and business cycles.
Then classify themes:
An accelerating issue with moderate impact deserves more attention than a declining issue with high impact. You’re preventing future damage versus cleaning up past problems.
The cost of early intervention is almost always lower than the cost of crisis response.
You find root causes by connecting themes to metrics like NPS, sentiment, and satisfaction scores.
Here's what this means in practice: instead of just counting how often a theme appears, you measure how much that theme affects your scores when customers mention it.
This reveals a critical gap. Most teams prioritize by volume - what customers mention most. But customers leave based on impact - what actually changes their scores. A theme mentioned by 5% of customers might cost you more NPS points than one mentioned by 25%.
When you confuse volume for impact, you chase problems that barely move the needle.
Identifying themes means discovering what customers talk about. Volume tells you the topics on their minds.
Understanding drivers means knowing which themes actually change customer behavior and scores. Impact tells you what makes them stay or leave.
Thematic's Impact Analysis calculates this: Impact = Overall average NPS minus Theme-specific average NPS.
The full methodology is in this NPS root cause analysis guide.
A theme mentioned by 5% of customers might cost you more NPS points than a theme mentioned by 25%. Mention rate becomes irrelevant when you can see actual point drag.
The practical approach is to start with impact analysis, then segment by customer type, then check trend direction.
The Orion Air case study demonstrates this gap after a system migration.
Complaint volume surged. Service issues dominated every dashboard. Leadership mobilized resources for what looked like an obvious crisis.
When they ran NPS root cause analysis, the findings surprised them.
Baggage handling wasn’t mentioned most frequently. But it had the biggest negative impact on NPS. And 80% of those issues were fixable with existing resources.
Orion Air refocused. They fixed baggage handling first.
Result: 1.6-point NPS increase in that segment, contributing to 13% overall improvement.
Measurable revenue gains followed. The fix paid for itself in months.
Volume lies. Impact tells the truth.
This approach follows the IST framework: Impact first, then Segment, then Trend. It ensures you fix what matters to the customers who matter most.
AI theming tools work best when you customize them to your business context.
Theme customization, multi-label assignment, trend tracking, and impact analysis give you both speed and precision.
You’re not choosing between automation and control. You get both.
The teams that get the most value from AI theming treat it as a partnership. Let the AI do the heavy lifting of initial discovery. Then refine, customize, and connect themes to outcomes that matter.
New to AI-powered theme discovery? Start with our guide on how AI identifies themes in customer feedback.
Or do you prefer to see how this works with your own data? Book a demo and we’ll show you which themes are actually moving your metrics.
Yes. When you merge, rename, or restructure themes in the Theme Editor, the system automatically re-analyzes all your existing feedback against the new structure.
This means your historical trends remain consistent and comparable. A comment tagged to "Delivery problems" last month will now appear under your new "Shipping issues" theme if you merged them.
Reprocessing typically completes quickly for most datasets. You receive an email when the new themes are visible in your dashboards.
Review quarterly or when your business changes significantly.
Product launches, pricing changes, and new competitors can generate new themes worth tracking.
Focus on high-volume themes first since they have the greatest impact on your analysis quality.
Yes. Thematic organizes themes into base themes and sub-themes. For example, "Customer Service" might contain "Phone Support," "Live Chat," and "Response Time." Learn more about base and sub-themes.
You can restructure this hierarchy to match how your organization thinks about customer issues.
Add it manually and run additional theme discoveries. Create a new theme with a few mapped phrases, then let the AI suggest similar phrases. This combines your business knowledge with the AI's pattern recognition.
The system learns from your additions and applies them going forward.
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