🤖 AI Summary
This work proposes a low-barrier, real-time analysis system based on large language models to address the challenge user experience practitioners face in efficiently leveraging open-ended textual feedback. Designed within organizational and technical constraints, the system employs model fine-tuning and engineering optimizations to automatically classify user feedback and extract thematic insights, enabling non-technical stakeholders to independently access real-time analytical results. Evaluation of the prototype demonstrates that this approach substantially lowers the technical threshold for data analysis, enhances the accessibility and efficiency of deriving actionable insights from user feedback, and advances the practice of data democratization within organizations.
📝 Abstract
In this paper we discuss an ongoing multi-year project that aims to make open text feedback more accessible and useful to UX practitioners by automating classification and providing real time access to comments, themes, and analysis. By significantly lowering the time and knowledge cost of implementing automated solutions, we aim to effectively democratize our data analysis processes, allowing and encouraging non-technical stakeholders to access and leverage data on their own. We share both the organizational and technical constraints we have encountered over the course of this project, and the solutions we have prototyped as a result of those constraints.