Voice AI is revolutionizing how companies handle customer support—but are you measuring its impact correctly? Just like with human agents, tracking the right KPIs (Key Performance Indicators) is crucial to understand what’s working, what’s not, and how to continuously improve.

In this blog, we’ll walk you through the most important Voice AI support KPIs, how to track them, and what insights they reveal about your CX performance.

Why KPIs Matter in Voice AI Support

• Validate ROI from automation
• Optimize call flows and user experience
• Ensure your AI is aligned with business goals
• Identify areas for tuning or escalation

1. Containment Rate (Self-Service Resolution Rate)

Definition: Percentage of calls resolved by the voice AI without human intervention.

Why It Matters: Higher containment = greater efficiency and cost savings.

How to Track: Analyze session logs and tag outcomes as "resolved" or "transferred."

2. Average Handling Time (AHT)

Definition: Average duration of AI-handled calls.

Why It Matters: Measures AI efficiency without compromising quality.

Best Practice: Monitor for balance—don’t rush calls at the expense of clarity.

3. Customer Satisfaction Score (CSAT)

Definition: User-reported satisfaction after an interaction.

Voice AI Tip: Use post-call surveys via IVR or SMS.

4. First Call Resolution (FCR)

Definition: Issues resolved in the first interaction—without follow-ups.

Why It’s Critical: Strong predictor of user trust and experience.

Voice AI Advantage: Smart routing and contextual memory help boost FCR.

5. Escalation Rate

Definition: How often the AI hands off to a human agent.

Insight: High escalation may indicate poor intent recognition or limited decision trees.

Goal: Reduce unnecessary handovers without frustrating users.

6. Intent Recognition Accuracy

Definition: How correctly the AI identifies customer intent.

Why It Matters: Directly affects FCR and containment rate.

How to Improve: Train with diverse, real-world utterances.

7. Sentiment Score

Definition: Measures customer emotion during and after the call.

Voice AI Capability: Sentiment analysis tools rate tone as positive, neutral, or negative.

Use Case: Trigger alerts for negative sentiment or follow-up callbacks.

8. Call Drop-Off Rate

Definition: Percentage of users who hang up before resolution.

Why It’s Dangerous: May indicate confusion, long pauses, or frustrating flows.

Action Point: Audit drop-off points to fix friction.

Conclusion

To get the most out of your Voice AI system, you need to go beyond deployment—you need to measure. These KPIs will help you continuously improve performance, fine-tune user flows, and deliver support that’s not only fast and scalable—but effective and satisfying.

Need help tracking AI-driven support performance? Talk to Brainey