AI Call Analytics: Metrics That Matter

Start with a call funnel

For outbound calls, separate attempts, answered calls, right-party contacts, valid conversations and completed outcomes. For inbound calls, separate offered, answered, recognized intent and resolved or transferred outcomes.

A single completion percentage without a clear denominator is easy to misread. Define each event in writing and keep it consistent across dashboards.

Track business and experience together

An operational scorecard should show whether the task was completed and how the caller experienced the path.

• Correct task completion and critical-field accuracy.
• Transfer reason and context quality.
• Abandonment, retries and repeat contact.
• Response and tool latency.
• Opt-outs, complaints and correction rate.
• Cost per valid conversation and completed outcome.

Use review samples for hidden errors

Logs show that a tool returned success, but may not show whether the caller understood or the agent made an unsupported claim. Review stratified samples across languages, outcomes and failure types.

Create a rubric for factuality, tone, policy adherence, pronunciation and handoff. Calibrate reviewers so scores mean the same thing over time.

Turn metrics into ownership

Route speech-recognition issues, stale knowledge, tool failures and policy problems to different owners. Show the largest failure drivers and the estimated outcomes they affect.

After a change, replay regression calls and watch both the target metric and guardrail metrics. A shorter call is not an improvement if repeat contact rises.

Frequently asked questions