Post-Call Analytics for Voice Agents: Metrics and Monitoring

Your voice agent dashboard shows perfect metrics. Call success rate: 98%. Average latency: 450ms. Error rate: 0.2%.

But customers keep calling back. Escalations are rising. The CFO wants to know why containment dropped 15% this quarter.

What's happening?

You're capturing transcripts, not analytics.

Post-call analytics for voice agents requires real-time data pipelines capturing audio signals, latency breakdowns, and semantic quality across every layer of the stack. Most teams log transcripts and call outcomes. That's like monitoring a web app by logging HTTP status codes—you'll know something failed, but not why.

At Hamming, we've analyzed 4M+ voice agent calls across 10K+ voice agents. The pattern is consistent: teams with transcript-only analytics discover issues 2-3 days after customers experience them. Teams with proper observability catch degradation in minutes.

TL;DR: Implement voice agent post-call analytics using Hamming's 4-Layer Analytics Framework:

Layer 1: Telephony & Audio — Track packet loss, jitter, SNR, codec performance

Layer 2: ASR & Transcription — Monitor WER, confidence scores, transcription latency (target p95 <300ms)

Layer 3: LLM & Semantic — Measure TTFT, intent accuracy, hallucination rate, prompt compliance

Layer 4: TTS & Generation — Track synthesis latency, MOS scores, voice consistency

The goal: correlate any conversation issue to a specific layer within 5 minutes, not 5 hours.

Methodology Note: Metrics, benchmarks, and framework recommendations in this guide are derived from Hamming's analysis of 4M+ voice agent interactions across 10K+ production voice agents (2025-2026).

Thresholds validated across healthcare, financial services, e-commerce, and customer support verticals.

Last Updated: February 2026

Related Guides:

• Voice Agent Monitoring KPIs: 10 Production Metrics Guide — The 10 critical KPIs with formulas and alert thresholds
• Voice Agent Observability: End-to-End Tracing — OpenTelemetry integration for distributed tracing
• How to Monitor Voice Agent Outages in Real Time — 4-Layer Monitoring Framework
• Voice Agent Evaluation Metrics Guide — Definitions, formulas, and benchmarks
• Voice Agent Dashboard Template — 6-Metric Framework with executive reports

Quick Reality Check

Running a demo with 50 test calls per week? Basic logging and transcript review work fine. Bookmark this guide for when you scale.

Already using a managed voice platform with built-in analytics? Check whether their metrics span all four layers. Most platforms provide transcript analysis but miss audio quality, component-level latency, and semantic evaluation.

This guide is for teams operating voice agents at production scale who need to debug issues across distributed components and correlate user experience to specific failure modes.

How Voice Agent Analytics Differs from Traditional Call Analytics

Traditional call center analytics focuses on operational efficiency: average handle time, queue wait, agent utilization. Voice agents generate entirely different data requiring different analysis approaches.

The fundamental difference: Human agents generate qualitative signals requiring interpretation. Voice agents generate structured interaction data—intent classification, tool calls, confidence scores, latency traces—that can be analyzed programmatically at scale.

Voice agents also fail differently. A human agent who doesn't understand a request asks for clarification. A voice agent that misclassifies intent confidently routes the caller to the wrong flow. Both calls might complete, but only one achieves the customer's goal.

Hamming's 4-Layer Voice Analytics Framework

Voice analytics spans four interdependent layers. Each layer has distinct metrics, failure modes, and instrumentation requirements:

Issues cascade across layers. An audio quality problem causes transcription errors, which cause intent misclassification, which causes task failure. Without layer-by-layer instrumentation, you'll see the task failure but not the root cause.

Core Voice Agent Performance Metrics

Containment Rate and Escalation Patterns

Containment rate measures the percentage of calls handled entirely by the AI agent without transfer to a human:

Containment Rate = (AI-resolved calls / Total calls) × 100

Industry benchmarks: Leading voice agent deployments achieve 80%+ containment, though this varies significantly by use case complexity. Healthcare triage may target 60-70% while appointment scheduling targets 85%+.

Critical caveat: Optimizing containment alone can prioritize cost over resolution quality. High containment with low CSAT indicates "false containment"—users giving up rather than getting helped. Always pair containment tracking with task completion and satisfaction metrics.

Track escalation patterns by reason category:

• Knowledge gap (agent lacks required information)
• Authentication failure
• User preference (explicitly requested human)
• Conversation breakdown (intent confusion, loops)
• Policy requirement (regulatory escalation triggers)

First Call Resolution (FCR) and Task Completion

First Call Resolution (FCR) measures issues resolved during initial interaction without callbacks:

FCR = (Resolved first contact / Total contacts) × 100

Task Success Rate (TSR) measures goal completion independent of escalation:

TSR = (Completed tasks / Attempted tasks) × 100

Voice agents should achieve 75%+ FCR with task completion verified through structured outcome tracking. Higher targets (85%+) are achievable for well-defined transactional flows like appointment scheduling.

Measurement approach: Use 48-72 hour verification windows. If a customer calls back within that window, the original call didn't resolve their issue—even if it was marked "complete."

Customer Satisfaction Proxies: CSAT and NPS in Voice

Voice agents can embed satisfaction measurement directly in conversations, achieving 30%+ higher completion rates than post-call surveys:

CSAT measures individual interaction quality; NPS measures cumulative relationship health. For voice agents, CSAT is the more actionable metric—it correlates directly to specific calls you can analyze.

Speech-level signals: Don't rely solely on explicit ratings. Track caller frustration through sentiment trajectory, interruption patterns, and repetition frequency. Users who say "I already told you that" rarely give 5-star ratings.

Response Latency and Time to First Word

Time to First Word (TTFW) is the most critical conversational metric—the time from user silence detection to first agent audio:

TTFW = VAD silence → Agent audio start

Production reality: Based on Hamming's analysis of 4M+ calls, industry median TTFW is 1.4-1.7 seconds—5x slower than the 300ms human conversational expectation. This explains why users report agents that "feel slow" or "keep getting interrupted."

Track component-level latency breakdown:

• Audio transmission: ~40ms
• STT processing: 150-350ms
• LLM inference (TTFT): 200-800ms (typically 70% of total)
• TTS synthesis: 100-200ms
• Audio playback: ~30ms

Turn-Taking Quality and Interruption Metrics

Turn-taking quality determines whether conversations feel natural or robotic:

Critical insight: Averages hide quality issues in conversational flow. A system with 400ms average TTFW but 15% of turns exceeding 1.5s has a hidden problem affecting thousands of interactions daily.

Track latency distributions (p50, p90, p95, p99) rather than averages. Alert on percentile spikes, not mean degradation.

Voice-Specific Quality Indicators

Word Error Rate (WER) and Transcription Accuracy

Word Error Rate (WER) is the industry standard for ASR accuracy:

WER = (Substitutions + Deletions + Insertions) / Total Words × 100

Test across acoustic conditions: LibriSpeech clean speech achieves 95%+ accuracy. Real-world conditions (accents, background noise, mobile networks) reduce this by 5-15 percentage points. WER benchmarks without environmental variation are misleading.

Track WER distribution, not average. A 7% average WER that spikes to 25% for users with accents indicates a systematic problem affecting specific user segments.

Semantic Accuracy and Intent Classification

Semantic accuracy measures correct intent interpretation—whether the agent understood what users wanted to do, not just the words they used:

Intent Accuracy = (Correct classifications / Total utterances) × 100

Target 80-85% for initial deployments, 90%+ for mature systems. Voice agents face 3-10x higher intent error rates than text systems due to ASR error cascade effects.

Track confidence score distributions across conversation turns. Declining confidence across a conversation signals cumulative confusion that may not trigger individual turn failures but degrades overall experience.

Confidence Scores and Fallback Frequency

Low-confidence outputs and frequent fallbacks signal hallucination risk or knowledge gaps:

Monitor fallback patterns by query category. If "billing" intents have 5% fallback rate but "technical support" has 25%, the knowledge gap is specific and actionable.

Mean Opinion Score (MOS) for Voice Naturalness

Mean Opinion Score (MOS) evaluates TTS naturalness and clarity on a 1-5 scale:

Near-human TTS systems average 4.3-4.5 MOS. Acoustic evaluation catches issues that transcript-only analysis misses—robotic prosody, unnatural pacing, pronunciation errors on domain vocabulary.

MOS testing is resource-intensive (requires human evaluators). Use automated proxies like MOSNet for continuous monitoring, with periodic human evaluation for calibration.

Latency Monitoring and Optimization

Component-Level Latency Breakdown

Track latency at each component boundary to identify bottlenecks:

LLM inference typically accounts for 70% of total latency. When optimizing, start with the LLM layer—model selection, prompt length, caching strategies—before addressing other components.

Latency compounds across the stack. A 50ms regression in each of 4 components becomes 200ms total degradation that users notice.

Time to First Audio (TTFA) Analysis

TTFA measures the complete path from customer silence to agent audio playback—the actual user experience:

TTFA = Silence detection → Audio buffer → STT → LLM → TTS → Playback start

Track TTFA separately from component latencies. Network conditions, audio buffering, and codec overhead add latency not visible in component metrics.

Percentile-Based Latency Tracking

Never rely on average latency. Track p50, p95, p99:

A 300ms average can hide 10% of calls spiking to 1500ms. At 10,000 calls/day, that's 1,000 terrible experiences that don't appear in average metrics.

Alert on percentiles: Configure alerts for p95 >800ms rather than average >500ms to catch tail latency issues before they affect significant user populations.

Real-Time Latency Alerting

Configure alerts that catch issues before they compound:

Include component-level breakdown in alerts. "Latency spike" is not actionable. "LLM TTFT spiked from 400ms to 1200ms at 14:32 UTC" enables immediate triage.

End-to-End Observability and Tracing

OpenTelemetry Integration for Voice Pipelines

OpenTelemetry provides the standard framework for distributed voice agent tracing:

User speaks → Audio captured (trace_id: abc123)
                ↓
            STT (span_id: stt_001, parent: abc123)
                ↓
            LLM (span_id: llm_001, parent: abc123)
                ↓
            TTS (span_id: tts_001, parent: abc123)
                ↓
            Audio played (trace_id: abc123)

Every event, metric, and log entry includes trace_id. Query your observability backend for that trace to see the entire conversation flow in one view.

Span attributes to capture:

• Component identity (provider, model version)
• Latency (start, end, duration)
• Confidence scores
• Input/output sizes
• Outcome signals

Audio-Aware Logging and Metadata Capture

Log audio attachments with transcriptions, confidence scores, and acoustic features:

Replay failed calls to diagnose whether issues were STT errors, semantic misunderstanding, or response generation problems. Every production failure becomes a debugging artifact.

Multi-Layer Trace Analysis

Correlate issues across the full stack:

Cascading failures are common. Audio degradation causes transcription errors, which cause intent misclassification, which causes task failure. Without multi-layer correlation, you'll see the task failure but chase the wrong root cause.

Production Call Replay for Root Cause Analysis

Replay production calls against new prompts or models in shadow mode:

1. Capture production audio and transcripts
2. Run through updated agent configuration
3. Compare responses to production baseline
4. Detect regressions before deployment

Every failure becomes a test scenario. Build regression suites from production issues to guard against repeat failures.

Automated Scoring and Evaluation Frameworks

LLM-as-Judge for Conversation Quality

LLM-as-judge evaluators achieve 95%+ agreement with human raters when properly calibrated:

Two-step evaluation pipeline:

1. Initial assessment: Score conversation on dimensions (accuracy, helpfulness, tone, completeness)
2. Calibration review: Check edge cases and low-confidence scores against human judgment

Calibration is critical. Run periodic human evaluation on a sample of LLM-scored conversations to detect evaluator drift.

Task Success and Outcome Verification

Track structured outcome metrics:

Verify task completion through action confirmation—appointment actually booked, payment actually processed, case actually created. Claimed completion without verification leads to false positive metrics.

Custom Business Metrics and Assertions

Define business-critical assertions specific to your use case:

Examples:

• "Must confirm appointment date and time before ending call"
• "Must offer premium option for eligible customers"
• "Must collect insurance information before scheduling"
• "Must not provide medical advice beyond scope"

Automated tagging categorizes calls by outcome:

• outcome:success:appointment_booked
• outcome:failure:authentication_failed
• outcome:escalation:user_requested

Acoustic and Sentiment Analysis

Speech-level analysis detects signals that transcript analysis misses:

Users who sound frustrated but complete the call rarely report satisfaction. Sentiment trajectory—how the call feels over time—predicts CSAT more accurately than final outcome alone.

Regression Detection and Continuous Testing

Automated Regression Testing on Model Updates

Model updates, prompt revisions, and ASR provider changes trigger behavioral drift. Automated regression suites catch quality degradation before production deployment:

Regression testing triggers:

• Prompt version changes
• Model provider updates
• ASR/TTS configuration changes
• Knowledge base updates
• Any component deployment

Regression metrics to track:

• Intent accuracy delta (>2% drop = investigate)
• TTFT delta (>100ms = investigate)
• Task completion delta (>5% drop = block)
• Prompt compliance delta (any drop in safety assertions = block)

Golden Dataset Management

Maintain golden datasets representing critical use cases:

Golden datasets should be version-controlled and updated as the product evolves. Stale test sets miss new failure modes.

CI/CD Integration for Voice Quality Gates

Integrate evaluation into deployment pipelines:

PR opened → Run regression suite → Quality gates → Deploy to canary → Production metrics → Full rollout

Quality gates that block deployment:

• Intent accuracy <95%
• Task completion <85%
• Safety assertion failures >0
• Latency regression >20%

Configure canary deployments with automatic rollback when production metrics breach thresholds.

Synthetic Scenario Generation from Production Failures

Auto-generate test scenarios from production failures:

1. Identify failed calls (task incomplete, escalation, negative sentiment)
2. Extract audio and context
3. Add to regression suite
4. Validate fix doesn't regress other scenarios

Production failures are the highest-value test cases. They represent real user behavior that synthetic generation misses.

Compliance and Security Monitoring

HIPAA Compliance Tracking for Healthcare Voice Agents

Monitor unauthorized PHI disclosures, authentication failures, and consent verification:

Production monitoring catches compliance patterns that synthetic testing misses—real users attempt unexpected disclosures, edge cases appear in live traffic.

PCI DSS Requirements for Payment Handling

Voice agents processing payments require:

• Tokenization of card data (never store PAN in logs)
• Encrypted transmission (TLS 1.2+)
• Access controls with audit logging
• Regular vulnerability scanning
• Penetration testing

Voice-specific consideration: Card numbers spoken aloud must not appear in transcripts or audio recordings. Implement real-time redaction before any logging.

Guardrail Effectiveness and Policy Violations

Track safety violations, prompt injection attempts, and policy breaches:

Automated detection flags conversations requiring compliance review. Manual review of flagged calls builds training data for improved detection.

Audit Logging and Retention Policies

Implement comprehensive audit logs:

Role-based access controls ensure only authorized personnel can access sensitive logs. Maintain signed BAAs with all vendors processing protected data.

Hallucination Detection and Mitigation

Confidence-Based Hallucination Signals

Low confidence scores, responses lacking source attribution, and inconsistent outputs signal hallucination risk:

Track hallucination-related metrics continuously:

• Responses without retrieval support
• Confidence distribution across response types
• Factual accuracy on verifiable claims

Retrieval Coverage and Knowledge Gap Analysis

Track retrieval success rate and identify knowledge gaps:

Retrieval Coverage = (Queries with relevant context / Total queries) × 100

Questions with no matching context drive hallucinations and fallback frequency. Map knowledge gaps to content expansion priorities.

Coverage analysis approach:

1. Log all retrieval queries
2. Identify zero-match and low-relevance retrievals
3. Categorize by topic/intent
4. Prioritize knowledge base expansion

Cross-Generation Consistency Checks

Generate multiple responses to the same prompt and detect inconsistencies:

Higher variance signals hallucination requiring tighter temperature/prompt constraints.

Prompt Engineering for Hallucination Reduction

Reduce hallucination through prompt design:

Dashboard Design and Reporting Workflows

Real-Time Operations Dashboards

Display live metrics that operations teams need:

Alert on threshold breaches with runbook links for immediate action.

Quality Trend Analysis and Drift Detection

Track metrics over time to identify drift:

Gradual degradation is harder to catch than sudden failures. ML-based anomaly detection after 2-4 weeks of baseline data catches drift that static thresholds miss.

Compliance and Audit Reporting

Generate compliance reports for regulatory review:

Automated generation ensures consistent reporting without manual effort.

Executive Performance Summaries

Report business impact metrics for leadership:

Frame metrics in business terms: "Containment improved 8%, reducing escalation costs by $47,000/month."

Implementation Checklist

Instrumentation Setup

• OpenTelemetry spans for each component (STT, LLM, TTS)
• Trace ID propagation across all API calls
• Audio capture with metadata
• Latency breakdown logging
• Confidence score capture

Metrics Configuration

• TTFW tracking (p50, p95, p99)
• WER monitoring with segment breakdowns
• Intent accuracy with confusion matrix
• Task completion with outcome categorization
• Sentiment trajectory tracking

Dashboard Deployment

• Real-time operations view
• Trend analysis panels
• Alert status visibility
• Drill-down to individual calls
• Trace waterfall view

Alerting Configuration

• Latency percentile alerts
• Accuracy degradation alerts
• Compliance violation alerts
• Escalation path definition
• Runbook links in all alerts

Regression Testing

• Golden dataset maintenance
• CI/CD quality gates
• Canary deployment configuration
• Automatic rollback thresholds
• Production failure → test scenario pipeline

Voice agent analytics requires more than logging transcripts. The teams that debug fastest aren't the ones with the best engineers—they're the ones with proper observability across all four layers. Invest in instrumentation now. The debugging time it saves will compound.

Get started with Hamming's voice agent analytics platform →

Related Guides

• Voice Agent Monitoring KPIs — 10 Critical Production Metrics
• Voice Agent Dashboard Template — 6-Metric Framework with Charts
• Real-Time Voice Analytics Dashboards — End-to-end tracing, quality scoring, and automated evals for customer service