Once the code frames are finalised, it is quite good at recognising the open ended responses to bucket them in correct categories. It is quite helpful when there are large number of responses to be coded and saves significant time compared to manual coding. Editing the codes are also easy as they can be shifted from one category to another using drag and drop and can also be merged quite fast. The AI engine is quite capable in recognising the sentiments of the responses and that alone saves us precious time.

Caplena is strong on automated topic detection and sentiment analysis, including topic-level sentiment, which helps when a single comment contains mixed feedback. The interface supports iterative refinement of classifications and makes it practical to improve quality over time. Segmentation and filtering are well supported via metadata, which is useful for drilling down by customer characteristics, channels, or time periods. Export options also make it easier to validate results and continue analysis in other tools. From a compliance perspective, the anonymization capabilities and multilingual support are meaningful strengths for teams working with sensitive verbatim data. As a European, Swiss-based solution with EU data hosting, Caplena is also a strong fit for organizations with strict GDPR and data residency requirements.

Caplena provides an abundancy of functions for the analysis of open-ended customer feedbacks. It combines Sentiment Analysis, i.e. the detection of emotions of customers, the classification of categories and topics, visualization via dashboards and - last but not least - anonymization functions which are important for data privacy regulations.