AI call center metrics should connect conversation quality with business outcomes. Universal “good” percentages are misleading because workflows, customers, risk, and escalation policies differ.
Availability metrics
- Answer time and abandonment.
- Coverage by hour and channel.
- Concurrent demand and overflow.
Understanding metrics
- Intent accuracy from reviewed samples.
- Clarification rate.
- Incorrect routing and correction rate.
Outcome metrics
- Resolved requests.
- Completed appointments, tickets, intake, or transactions.
- First-contact resolution and repeat contact.
Human escalation metrics
- Transfer rate by intent.
- Transfer success and wait time.
- Reason for escalation and fallback failures.
Experience and quality
- Customer feedback and complaints.
- Conversation review scores.
- Policy violations and unsupported answers.
Cost metrics
Track total cost per handled request and per successful outcome, including setup, usage, integrations, telephony, monitoring, and human support.
Governance metrics
Monitor incidents, access issues, evaluation coverage, model or knowledge changes, and time to correction. The NIST AI Risk Management Framework provides an official risk-governance structure.
