Conversation topics
Understand recurring customer questions and the areas generating the most support demand.
- Topic patterns
- Question frequency
- Trend visibility
Conversation Analytics
Use support conversations to decide what needs fixing next.
Repeated questions show where customers are confused. Escalations show where automation stops being useful. Missing answers show what the knowledge base needs. The analytics should make those patterns visible.
AI Analytics & InsightsConversation patterns
Operational viewIllustrative solution interface
TopicsWhat customers ask
Group recurring questions and themes to see where demand is concentrated.
KnowledgeWhere answers are weak
Identify unanswered questions or areas that repeatedly require qualification.
OperationsWhere people take over
Review handoff patterns and operational reasons for escalation.
Conversations→Classify→Aggregate→Improve
Conversation insight
Look for the question behind the chart.
Dashboards matter only when someone can act on what they show. We focus on recurring topics, unanswered questions, handoff reasons and changes in demand.
Knowledge gaps
See where the assistant lacks sufficient approved information or repeatedly needs qualification.
- Unanswered questions
- Weak evidence areas
- Content improvement
Handoff patterns
Understand when conversations move to people and what business situations drive those escalations.
- Escalation reasons
- Agent workload themes
- Workflow opportunities
Insight loop
Use conversations to improve the system that answers them.
Conversation analytics becomes valuable when findings can feed back into content, routing, workflows and support priorities.
Conversation events
Record the interaction and relevant outcome metadata.
Topics & patterns
Group recurring questions and operational themes.
Knowledge gaps
Find where evidence or content is insufficient.
Handoff reasons
Understand why conversations require people.
Measure change
Update content or workflow and track the effect.
Responsible analytics
Collect what is needed to improve the service, with appropriate privacy and tenant boundaries.
Conversation analytics can involve customer information, operational metadata and model behaviour. Data collection should be purposeful and aligned to tenant isolation, privacy and retention requirements.
TenantMetrics and conversation data should stay scoped to the correct organisation.
PurposeCollect fields that support quality, routing, operations or product improvement.
CostOperational analytics can also include model, token and tool usage where AI cost management is required.
Conversation insight
What would you like to learn from your customer conversations?
Decide first what the analytics should help someone do—improve knowledge, understand handoffs, spot demand patterns or review operational quality.