# retellai-call-analytics

> Implement call analytics with transcription analysis, sentiment scoring, and conversation metrics

## Directory Structure

```
retellai-call-analytics/
├── SKILL.md
└── examples/
    └── example.py
```

## File Descriptions

| File | Type | Purpose |
|------|------|---------|
| SKILL.md | Markdown | Core skill instructions for call analytics pipeline |
| examples/example.py | Python | Example analytics pipeline with sentiment and metrics |

## Summary

**Category:** cicd
**Target Audience:** Data engineers, Product managers, Business analysts
**Trigger Phrases:** `retell analytics`, `retell call data`, `retell metrics`, `retell transcription analysis`

### What This Skill Does

This skill implements comprehensive call analytics for Retell AI voice agents. It covers building transcription analysis pipelines, implementing sentiment scoring on conversation segments, calculating key metrics (talk time, resolution rate, escalation rate), and creating dashboards for business visibility.

### Technical Success Criteria

- Analytics pipeline processing all call transcriptions
- Sentiment analysis running on conversation segments
- Key metrics calculated and stored
- Dashboards deployed for visualization
- Historical data retention configured

### Business Success Criteria

- Data-driven optimization of agents
- Improved agent performance tracking
- Complete call analytics within 24 hours of call completion

## Related Skills

- retellai-webhooks-events - Receiving transcription data
- retellai-observability - Operational metrics
- retellai-cost-tuning - Usage analysis for cost optimization
