LLM-Based Decomposition of Satisfaction Reveals Hidden Customer Sentiment in Support Conversations

Jul 14, 2026

Researchers used GPT-4.1 to annotate approximately 9,000 customer support conversations, breaking down satisfaction into five axes: overall, agent, outcome, product, and effort. Four axes (overall, agent, outcome, effort) closely tracked self-reported customer ratings, while product satisfaction showed weak alignment. The study found that satisfaction scores are significantly lower when measured across all contacts (2.91) compared to only those who responded to surveys (3.62) on a five-point scale, highlighting survey bias.

Why it matters: This work demonstrates that LLM-based decomposition can uncover hidden drivers of customer experience and reveal biases in traditional survey-based satisfaction metrics.

Full story at: arXiv Computation and Language ↗