Exporting Data

Export normalized analysis records, KPI summaries, and tagged signals to BI systems and operational tooling.

Export Types

- Call-level structured records for transcript-linked analysis.
- Metric and score-oriented exports for team reporting.
- Canonical analysis bundles for downstream systems.
- API-driven retrieval and export-friendly response payloads.

Call-Level vs Aggregate Exports

Call-Level

- Transcript and signal context
- Findings/entities/metrics per interaction
- Best for detailed QA and workflow automation

Aggregate

- Trend and benchmark-oriented metrics
- Team, queue, and period rollups
- Best for management reporting and BI dashboards

Common Export Destinations

- BI dashboards and reporting pipelines
- CRM and customer-success systems
- QA review and governance workflows
- Data warehouse tables and analytics marts
- Webhook-triggered or automation orchestration layers

Export Field Consistency

Export fields should preserve canonical metric names, stable identifiers, and interpretation context. Keep field semantics aligned across dashboard labels, API records, and exported datasets.

Governance and Versioning Considerations

- Version metric and rubric changes before broad export rollout.
- Validate downstream mapping whenever canonical definitions change.
- Preserve data lineage for audit and interpretation traceability.

Operational Use Examples

- Daily KPI exports for manager scorecards and trend reviews.
- Call-level signal bundles pushed to follow-up tasking workflows.
- Risk and compliance exception feeds for prioritized QA review.
- Entity/topic export streams used for voice-of-customer analysis.

Implementation Caveats

Export availability and schema shape can depend on tenant-level controls, governance settings, and processing completeness. Treat destination mappings as implementation-specific and validate in lower environments before production rollout.