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Mastering Voice AI Analytics: The Metrics Dashboard Deep Dive

A comprehensive tour of CallSure's analytics what each metric means and how to use data to drive improvement.

Alex Johnson

Alex Johnson

AI Training Specialist

Aug 20, 202415 min read
Mastering Voice AI Analytics: The Metrics Dashboard Deep Dive

"What gets measured gets managed." But in voice AI, what should you actually measure? With dozens of available metrics, it's easy to drown in data without gaining insight. This guide explains every metric in our dashboard and which ones actually matter for your goals.

The Hierarchy of Metrics: Not all metrics are equal. We organize them into Outcome metrics (what you're trying to achieve), Driver metrics (what causes outcomes), and Diagnostic metrics (for troubleshooting).

Outcome Metrics: The Big Picture

Containment Rate

What it is: Percentage of calls resolved by AI without human intervention.

Why it matters: Primary measure of AI effectiveness. Directly impacts cost savings.

Benchmark: 50-70% is good. 70%+ is excellent.

Customer Satisfaction (CSAT)

What it is: Post-call survey rating, typically 1-5 scale.

Why it matters: Ultimate measure of customer experience quality.

Benchmark: 4.0+ is good. 4.5+ is excellent.

First Call Resolution (FCR)

What it is: Percentage of issues resolved without customer calling back.

Why it matters: Repeat calls are expensive and frustrate customers.

Benchmark: 70%+ is good. 85%+ is excellent.

65%
Avg Containment
Across all customers
4.3
Avg CSAT
On 5-point scale
81%
Avg FCR
First call resolution
2.3 min
Avg Handle Time
For AI-resolved calls

Driver Metrics: What Causes Outcomes

Intent Recognition Accuracy

What it is: How often AI correctly identifies why the customer is calling.

Impact: Low accuracy → misrouted calls → low containment, low CSAT.

Sentiment Trend

What it is: How customer emotion changes during the call (improving/declining/stable).

Impact: Declining sentiment during calls predicts low CSAT, even if resolved.

Escalation Rate by Intent

What it is: Which call types require human handoff most often.

Impact: Identifies where AI training is weakest. Priority for improvement.

Diagnostic Metrics: Troubleshooting

Average Response Latency

What it is: Time between customer finishing speech and AI responding.

Red flag: Latency >400ms feels unnatural and damages satisfaction.

Speech Recognition Confidence

What it is: How confident the AI is in what it heard.

Red flag: Low confidence clusters may indicate accent issues or audio problems.

Fallback Rate

What it is: How often AI says "I didn't understand" or uses generic responses.

Red flag: High fallback rate indicates missing training or unclear customer speech.

Building Your Dashboard

Recommended Dashboard Setup:

  • 1 Executive view: Containment, CSAT, cost savings (weekly)
  • 2 Operations view: Volume, handle time, escalation rate (daily)
  • 3 Training view: Intent accuracy, fallbacks, new intents detected (daily)
  • 4 Technical view: Latency, errors, system health (real-time)
Pro Tip: Set up automated alerts for metric thresholds rather than watching dashboards constantly. Get notified when containment drops below 60% or CSAT drops below 4.0. Otherwise, check weekly.

Need Help Setting Up Analytics?

Our customer success team can help you configure dashboards that match your goals.

Schedule Analytics Review →
AnalyticsDashboardMetricsProduct Updates
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Alex Johnson

Written by

Alex Johnson

AI Training Specialist

Alex has trained 200+ AI voice agents across industries.

@alexj_ai