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Norhan Eid from SleekFlow
Customers deploying AI agents at scale need visibility into both individual AI conversations and aggregate AI performance in order to evaluate quality, identify improvement opportunities, and scale AI responsibly.
Today, labels can be used for segmentation and operational tracking, but there is limited native reporting specifically focused on AI conversation outcomes, customer intent, sentiment themes, and AI performance trends.
Requested Enhancement:
Provide dedicated AI conversation analytics and reporting capabilities, including:
Conversation-Level Analysis
  • Ability to review AI conversation outcomes on a per-customer basis.
  • Visibility into whether an AI conversation was successful.
  • Visibility into handoff reasons.
  • AI resolution/completion tracking.
  • AI vs human escalation tracking.
Aggregate AI Reporting
  • AI engagement rates.
  • AI completion rates.
  • Human handoff rates.
  • Conversion outcomes from AI conversations.
  • Complaint/escalation trends.
  • Performance by AI use case or chatbot.
Intent Analytics
  • Automatic identification and reporting of common customer intents.
  • Intent distribution trends over time.
  • Ability to filter/report by intent category.
Sentiment Analytics
  • Common themes surfaced in customer sentiment.
  • Positive/neutral/negative sentiment trends.
  • Escalation risk indicators.
Business Impact:
As customers expand AI usage, they need both conversation-level visibility and aggregate reporting to:
  • evaluate AI effectiveness.
  • identify areas for optimization.
  • understand customer behaviour.
  • measure AI ROI.
  • confidently scale AI across additional use cases.