Conversation analytics
Conversation analytics turns interaction data into customer and operational signals; validate topic coverage and examples before using dashboards to set priorities.
- 01
How can you reduce handle time without sacrificing customer satisfaction?
Build an AI pilot with handle-time components, resolution checks, survey evidence, and explicit quality guardrails.
- 02
How to investigate customer effort beyond handle time
Combine customer-reported effort with observed repetition, handoffs, waiting, and task outcomes to locate avoidable work.
- 03
Which contact center AI metrics distinguish CSAT, NPS and inferred satisfaction?
Define CSAT, NPS, inferred satisfaction and operational outcomes with formulas, coverage and an evidence-to-action workflow.
- 04
Why do QA averages change when weights and not-applicable answers change?
Use a worked example to distinguish per-contact averages, pooled pass rates, missing evidence and versioned QA weights.
- 05
How to investigate repeat contacts without counting every return as a failure
Define customer-task episodes, matching rules, follow-up windows, and failure reasons before using repeat contacts to prioritize contact center changes.
- 06
How should you evaluate contact center screen and audio analysis?
Test synchronized recordings, retrieval, endpoint fit and visual interpretation before buying screen-and-audio analytics.
- 07
How to investigate a workflow problem with screen and audio evidence
Use synchronized screen, audio, and case records to build a factual event timeline before attributing contact center friction.
- 08
How to evaluate sentiment and intent analysis in customer conversations
Separate inferred emotion, customer intent, and survey feedback before using AI classifications in operations.
- 09
How to compare sentiment trends when models and contact mix change
Check population mix, processing coverage, label rules, and model versions before interpreting a contact center sentiment trend.
- 10
How to turn VoC themes into owned operational actions
Create an action register that connects customer evidence to a responsible team, a bounded change, and a reviewable outcome.
- 11
How to build a VoC theme taxonomy that stays useful
Design customer-feedback categories with clear boundaries, examples, ownership, and version rules before comparing theme trends.
- 12
Choosing a voice-of-the-customer platform for contact centers
Compare conversation-led and feedback-led VoC workflows using evidence, topic quality, and follow-through.