Conversation Quality
A measure of how well an AI agent handles customer interactions—evaluating accuracy, completeness, tone, and whether the customer's problem was actually resolved, not just whether a response was generated.
Beyond Resolution Rate
Resolution rate tells you whether a conversation ended without human intervention. It doesn't tell you whether the customer actually got a good experience. An AI agent could resolve a query with a technically correct but confusing answer, an abrupt tone, or by missing the real question behind what the customer asked. Conversation quality captures what resolution rate misses.
What Quality Looks Like
High-quality AI conversations share specific characteristics: the response directly addresses what the customer asked, information is accurate and sourced from verified content, the tone matches the situation, follow-up questions are anticipated, and the customer leaves with their problem fully resolved—not just acknowledged. The difference between adequate and excellent AI support is the same as the difference between a correct answer and a helpful one.
Measuring Conversation Quality
The most effective teams measure conversation quality through a combination of automated scoring and human review. Automated checks verify factual accuracy against source material and flag potential issues. Human reviewers evaluate a sample of conversations for tone, completeness, and overall experience. Customer satisfaction scores provide the ultimate validation. Together, these create a feedback loop that continuously improves AI performance—not just on whether it resolves, but on how well it resolves.