Dimensionality in Satisfaction Ratings
We used a large language model (GPT-4.1) to annotate the text of about 9,000 support conversations at a global consumer-goods firm, decomposing customer-care satisfaction into component axes (overall, agent, outcome, product, and customer effort), and validated the LLM annotations against the satisfaction ratings customers gave themselves. Four of five axes track self-reported satisfaction closely (overall, agent, and outcome near an unadjusted 0.65; effort -0.54), while product satisfaction is weak against the available proxy. The unadjusted correlation also understates the alignment: the disagreements concentrate in a small, readable tail of divergent sessions rather than in general dri...
| Authors | Andrew Hong, Jason Potteiger |
|---|---|
| Primary category | cs.CL |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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