Discover why leading brands chose Caplena with Qualtrics  

Qualtrics is built for enterprise experience management. Teams use it to design and distribute surveys, manage closed-loop workflows, and report on experience data at scale. 

As teams spend more time on open-ended responses and look to connect them with structured data such as segments, scores, and drivers, Text iQ, the text analytics tool within Qualtrics, often becomes harder to work with. Creating and maintaining topics across multiple surveys takes manual effort, and changes are not easy to test or refine. 

Analysis is usually limited to data collected in Qualtrics, and insights are often explored through standard dashboards and bubble charts. While helpful for high-level views, this makes it harder to answer more detailed follow-up questions, compare themes across segments, or link qualitative feedback to key metrics and other data sources. 

This is where Caplena fits in. Not by replacing Qualtrics, but as a feedback intelligence layer on top of your Qualtrics data that extends how open-ended feedback is analyzed and connected to quantitative data, with greater flexibility, depth, and control. 

That’s why leading brands using Qualtrics, like Lufthansa, also work with Caplena for supercharging their feedback analysis.

"

We chose Caplena because they are the frontrunners when it comes to AI. The intuitiveness of the platform and its customization capabilities were key factors for us.

Niklas Melchior

Customer Insights Expert

What Qualtrics Covers in the Feedback Lifecycle 

• Multi-channel survey distribution and sophisticated question design 

• Closed-loop workflows with ticket creation and assignment (including teams, queues, and ticket workflows). 

• Stats iQ for statistical analysis and hypothesis testing 

• Text iQ for semi-automated topic detection and 5-label sentiment scoring 

• XM Discover for bringing together multiple data sources in dashboards 

Where Teams Add Caplena for Deeper Text Analysis

Qualtrics Text iQ provides multiple ways to create topic models, allowing teams to structure open-ended feedback directly within Qualtrics. In practice, the challenge is less about creating topics and more about the ongoing effort required to define, maintain, and refine the rules that determine when those topics are applied over time. 

Iteration in Text iQ is largely configuration-driven, with limited transparency into output quality. As topic structures grow or are reused across surveys, updates become harder to test, slower to apply, and more difficult to manage.

Teams typically add Caplena when they need to:

• Build hierarchical topic structures that match how their stakeholders think about the business, not how a keyword-oriented algorithm clusters text 

• Get coverage rates above 95% so no comment gets lost in "Other" or "Unclassified" 

• Audit and validate AI results to meet quality standards for regulatory or research requirements & are comparable to human analysis 

• Compare text from Qualtrics surveys and multiple sources like online reviews, social media, or surveys run on other platforms in a single analysis 

• Run advanced driver analysis or significance testing on text topics beyond what Stats iQ offers 

• Offer technical and non-technical users an interactive way to to explore insights or ask questions to the data in real time 

The result is a workflow that feels closer to analysis on the fly and less like configuration. 

Features at a glance 

How Caplena Supercharges Your Qualtrics Setup 

Full control over the codebook 

Caplena lets you build MECE (mutually exclusive, collectively exhaustive) topic frameworks that match your research needs. You can create hierarchies, merge topics, split topics, and define exactly how you want to categorize responses. The AI assists, but you make the calls. 

Once topic structures are defined, Caplena automatically applies them when new data is added to your project or allows you to reuse them when creating new projects to analyze new surveys or datasets. 

 Typical coverage: less than 5% untagged across all comments. 

Self-service for modern research teams 

Caplena is built for researchers who want to move fast without waiting on support tickets or vendor services.  

You can: 

• Upload data from Qualtrics (or anywhere else) in minutes via API or file export 

• Design your own codebook or let AI suggest one as a starting point 

• Create custom dashboards with the exact views your stakeholders need 

• Share unlimited read-only access across your organization 

• Control who can edit vs. view at a granular level 

Quality controls built for researchers 

Caplena shows you AI quality scores overall, and at the topic level. You can review and correct classifications. The system learns from your corrections and maintains an audit trail of every change. You can export validation reports that show exactly how well the model performed and where human judgment stepped in. 

This matters in regulated industries, market research, and anywhere you need to prove how your methodology holds up. 

Insight Agent for conversational analysis 

Your stakeholders don't want to learn how to build dashboard filters. They want to ask: "Why did NPS drop in the UK?" or "What are enterprise customers saying about onboarding?" 

Insight Agent layers on top of your coded data and answers questions. Not by generating plausible-sounding text (like ChatGPT would), but by running actual analysis on your dataset. It calculates statistical significance, identifies drivers, and surfaces the specific comments that back up its findings. 

You can choose to share Insight Agent access with unlimited users and soon, they can query your data directly from Slack or Teams.  

Compare across multiple feedback sources 

Qualtrics excels at managing feedback inside its ecosystem and some external sources with XM Discover. Caplena is designed to look across multiple sources, in a single dashboard.  

You can bring in: 

• Multiple Qualtrics programs and survey waves 

• Data from other survey platforms, markets and external projects 

• Google Reviews, Trustpilot, and other review sites 

• Competitor reviews for benchmarking 

• Social media comments, transcripts, and any text verbatims up to 20,000 characters. 

Then analyze everything with consistent topic definitions and sentiment scoring. See how sentiment on a specific issue differs between NPS surveys and Google Reviews. Compare your product feedback to what customers say about competitors. Set up alerts to notify you when topics or sentiment shift significantly across any of these sources. 

Flexible data enrichment with Smart Columns 

Smart Columns support data enrichment tasks with the ease of LLMs, such as normalizing responses, extracting specific attributes, or classifying comments into custom categories. 

Examples include mapping product names to standard categories, removing or standardizing language, identifying attributes mentioned in text, or extracting dates and locations.  

The transformation logic is defined once and applied consistently across the dataset. You define the logic. The system applies it across your dataset. 

 Advanced segmentation and statistical analysis 

Caplena allows teams to segment text topics and sentiment by any variable in the dataset. This includes running cross-tabs and comparing segments to understand how topics vary by region, customer type, or program. 

Text topics can also be analyzed alongside metrics such as NPS or CSAT to explore relationships between qualitative feedback and quantitative outcomes. 

Award-winning Customer Success 

Caplena is designed for teams that want to work independently with their Qualtrics data. At the same time, our Customer Success team is always available when support is needed. 

The team comes from insight and research backgrounds and works closely with customers to: 

• design codebooks for initial Qualtrics datasets and programs 

• set up validation and QA processes that align with internal research or compliance standards 

• get started with Smart Columns to standardize and enrich Qualtrics open-ended data 

• define Insight Agent use cases that support common stakeholder questions 

Support is available directly in the product, making it easy to get help without slowing down ongoing analysis. 

Getting started with Qualtrics + Caplena:  

Step 1: Identify the gap 
Where does your current Qualtrics text analytics fall short? What questions can't you answer? What analysis takes too long or requires too many workarounds? 

Step 2: Run a pilot 
Export one dataset from Qualtrics. Connect it to Caplena via the integration or direct upload. Compare the results, the ease of use, the depth of insight. 

Step 3: Scale what works 
Keep using Qualtrics for operations. Add Caplena for the analysis that requires more depth, control, or flexibility. 

Closing Thoughts 

All-in-one platforms like Qualtrics are designed to support the full experience management lifecycle in a single system. They simplify operations and governance and work well for running structured feedback programs at scale.

Text analysis, however, is just one component. As open-ended feedback volumes grow and analysis needs become more specific, teams can reach the limits of what a general-purpose platform is built to optimize.

Specialized solutions focus on doing one thing exceptionally well. For open-ended feedback, this means faster iteration, more flexible topic models, greater transparency into AI results, and stronger support for ad-hoc, recurring, and cross-source analysis.

That’s why many enterprise teams use both. Qualtrics remains the system of record for collecting feedback, while a specialized tool like Caplena is added when deeper text analysis is needed.

Note: Qualtrics is a registered trademark of Qualtrics LLC. This comparison is based on publicly available information from Qualtrics materials as of December 2025.