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Quality Assurance (Auto QA)

Quality assurance programs need consistency and scale. Conversation Analytics can help by turning QA standards into structured, reviewable results.

What Auto QA is (high level)

Auto QA applies a structured scorecard/rubric to conversations, such as:

  • required statements and compliance checks
  • process adherence
  • empathy and communication behaviors
  • resolution steps and customer handling

Outputs can be used to: - prioritize manual reviews - track trends and coaching impact - support auditing (where enabled)

Auto QA configuration details (scorecards, tasks, rollout) are covered in the Conversation Analytics – Administration Guide.

How QA teams use Conversation Analytics

1) Increase coverage

Instead of sampling a small percentage of conversations manually, teams can:

  • review outliers and high-risk conversations
  • monitor performance across all teams
  • focus human effort where it adds the most value

2) Improve consistency

Structured results help reduce reviewer variance and provide consistent standards.

3) Coach using evidence

Explanations (and question-level outputs, where available) help managers coach specific behaviors.

  • start with a limited scorecard (high-confidence questions)
  • validate on a representative sample
  • roll out gradually and refine over time