From Text to Feedback Analytics: Insights Beyond Topic Counts

From Text to Feedback Analytics: Insights Beyond Topic Counts

By Caplena

  • article
  • Text Analytics
  • Feedback Analytics
  • Customer Experience (CX) Feedback
  • CX Analytics
  • Digital CX – NPS – CES – CSAT
  • Survey Analysis

This article is a brief summary of Caplena’s Research’s Demo Days session in February 2025.

Watch the full webinar replay here:

Text analytics has advanced significantly, reshaping how organisations interpret customer feedback. Since its launch in 2018 as a text analysis tool, Caplena has grown into a broader feedback analytics system, combining AI capabilities with human expertise.

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About the Platform

Caplena processes diverse feedback sources, including survey responses, customer experience inputs, product testing results, and online reviews. Its unified analysis framework enables organisations to examine multiple data streams at once while ensuring consistency.

The platform ingests data in various formats and integrates with tools like Qualtrics and Brandwatch. While it doesn’t collect primary data (aside from review scraping), it supports 36 languages natively, automatic translation via Google Translate or DeepL and includes advanced anonymisation tools for handling sensitive information. Caplena exports data in various formats (Excel, SPSS, CSV, JSON), with planned Power BI integration and improved chart export options. Its flexible output supports integration into broader research and reporting workflows.

One of the system’s key features is its transparent AI-driven topic classification. Using Large Language Models (LLMs), it automatically groups customer feedback into topic collections based on content. Users can see how topics are associated and can adjust the AI-generated groupings, maintaining human oversight in the analytical process. These topics adhere to the MECE principle (Mutually Exclusive, Collectively Exhaustive), ensuring full coverage without redundancy. F1 scores measure precision and recall – overall and for each topic – helping users prioritize fine-tuning to the highest quality

Adaptable Capabilities Across Research Needs

Beyond topic classification, the platform includes sentiment and driver analysis, helping organisations assess customer feedback and its impact on key metrics like Net Promoter Score (NPS). The system applies driver analysis across different variables, making it adaptable to various research needs.

The reporting interface combines traditional data visualisation with modern interactive features. Users can segment data, apply filters, and compare customer groups or trends over time. A recently added chat enables natural language queries, integrating qualitative and quantitative analysis.

Caplena’s alert system tracks significant changes, emerging topics, or urgent content. This feature is particularly useful in compliance monitoring, such as detecting adverse events in healthcare feedback.

The platform is best suited for short-to-medium-length feedback like survey responses and customer reviews rather than long-form qualitative data like interview transcripts and in-depth qualitative research. Although primarily used for market research and customer experience analysis, the platform is also applied in employee feedback and product management. 

Caplena balances automation with human oversight, enabling efficient feedback analysis while keeping researchers in control. This combination of machine learning and expert assessment ensures insights remain scalable and reliable.

Watch the webinar to learn more about the Caplena platform.

About Caplena

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