Deep-dive analysis

Combine multiple insights for comprehensive analysis and recommendations.

Prompt	What you’ll learn
”Review 100 inquiry summaries from yesterday where CSAT was low and share recommendations on what knowledge, coaching, or playbooks we should add or update.”	Actionable recommendations based on recent low-satisfaction conversations.
”For all conversations today that weren’t resolved by our AI Agent, what were the most common reasons for failure?”	Pattern analysis across unresolved conversations.
”Compare our AR this month vs. last month and analyze what might be driving any changes.”	Month-over-month analysis with potential root causes.

Tips for better results:

Be specific about timeframes (for example, “last 7 days”, “yesterday”, “this month”).
Include the number of conversations to analyze (for example, “Review 50 summaries…”).
Ask follow-up questions to dig deeper into initial findings.
Use available filters: CSAT scores (1–5), resolution status, topics, playbooks, language, channel, browser, device, and more. See get_available_filters for the full list.
When creating visualizations, ask follow-up questions to refine the chart (for example, “adjust the y-axis to start at 30%”).