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User Reported Metrics, while important for assessing user perception, are difficult to operationalize due to their unstructured nature.
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User Reported Metrics, while important for assessing user perception, are difficult to operationalize due to their unstructured nature. However, recent advancements in LLMs allow for the conversion of unstructured user feedback into structured, actionable metrics. This enables teams to better prioritize performance improvement projects by assessing their impact on user perception alongside system-level metrics. While not foolproof, this combined approach provides a more comprehensive understanding of the effectiveness of changes made to conversational AI agents. It's crucial to remember that both types of metrics are valuable for accurately assessing and improving user perception.