Clustering Voice of the Customer Insights: Identifying Key Needs for AI-Based Early Warning System

In this study, we analyzed voice of customer (VOC) data for an AI-based early warning system from healthcare providers using the BERTopic framework for effective topic modeling. A preprocessing pipeline was implemented, incorporating techniques such as lowercasing, stopword removal, and tokenization to prepare the text for analysis. To refine the model's performance, hyperparameter optimization was conducted using Optuna, with a primary focus on maximizing the Silhouette Score and minimizing the Davies-Bouldin Index, while also assessing the diversity of the generated topics. Ultimately, we identified five major topics that encapsulate key themes within the VOC data.