Automating Voice of Customer Analysis with AI Workflows Built on GPT-4o-Mini Model

Traditional Voice of Customer (VOC) data analysis methods are labor-intensive and time-consuming, often requiring hundreds of hours for qualitative analysis. This paper introduces an AI-powered workflow based on commercially available large language models that automates the most laborious steps of VOC qualitative data analysis, reducing processing time from hundreds of hours to minutes while maintaining acceptable accuracy and improving consistency. The proposed workflow leverages the generic GPT-4o-Mini model to process and prepare transcript data, identify customer need statements, and organize these statements into a hierarchical structure for further quantitative analysis. The workflow includes stages such as transcript preparation, chunking text for analysis, identifying and scoring needs, and clustering needs into prioritized categories. Evaluation results demonstrate a significant reduction in time and effort, with the AI workflow achieving a 99 % reduction in qualitative analysis time compared to traditional methods with sufficient specificity of customer need if the chunk sizes are adequately small. Furthermore, the results help to overcome several typical cognitive and motivational biases of the analysts. The findings highlight the potential of AI to enhance the efficiency and consistency of VOC analysis, providing valuable insights for product development and customer satisfaction improvement.