AI Voice Analytics: Quality Scores, Sales Signals, Compliance

Why it matters

Subjective QA misses signals and wastes time. AI voice analytics:

• Tracks tone, pace, emphasis, and silence in real time.
• Spots sales signals (interest, objections, price sensitivity).
• Audits compliance on actual speech, not just transcripts.

4 proven use cases

1. Sentiment + escalation: Negative tone → human handoff with a full summary.
2. Sales signal mining: Map objections and winning rebuttals; refine scripts.
3. Compliance monitoring: Mandatory disclosures and opt-ins checked automatically.
4. Agent coaching: Speaking/listening ratio, filler words, tempo insights.

Deployment guide

• Define keyword/phrase lists: brand, product, objections, campaigns.
• Set alert thresholds: sentiment, silence, speaking ratio.
• Build dashboards: score trends, agent benchmarks, topic-level gaps.
• Integrate: push summaries/actions into CRM/ticketing.

KPIs

• CSAT/NPS lift (+0.3 to +0.8 target)
• Objection-to-close rate
• Compliance completion (95%+ target)
• Coaching time vs. quality score improvement

Pro tips

• A/B objection handlers and measure sentiment recovery.
• Track silence spikes to shorten scripts.
• Save “golden phrases” from top performers and reuse.

Try it

Pilot in a week; get scorecards and lock in gains within a month.

From trend to rollout

• Define the taxonomy: intents, objections, competitor mentions, compliance phrases.
• Start with call recording and diarization quality checks; tune microphones/IVR prompts to reduce noise.
• Deploy alerts for sudden spikes in negative sentiment or silence; route to the QA lead within the hour.
• Feed “golden calls” back to training so playbooks improve weekly.

Metrics and guardrails

• QA hours saved versus automation accuracy; re-listen to 10% of auto-passed calls.
• False positive/negative rate on compliance; pair with periodic human audits.
• Coaching actions closed per week and impact on close rate/CSAT.