Exploring Conversation Insights

  • Rversion finale: Australia
  • Mis à jour 12 mars 2026
  • 3 minutes de lecture

    • Learn how Conversation Insights can help you to augment conversation insights with AI-based Inferred customer satisfaction (CSAT) scores and factors.

    Conversation Insights overview

    Conversation Insights is designed to deliver Inferred CSAT scores and explanatory factors for conversations in Virtual Agent and agentic workflows. It leverages AI to analyze conversations in real time, and provides actionable insights that help improve Virtual Agent and live agent interactions, and agentic workflows.

    Inferred CSAT is a numerical score from 1 (least satisfied) to 5 (most satisfied). It’s predicted entirely from conversation transcripts in real time without any input from the user. In addition to the CSAT score, the model also predicts CSAT factors that contributed to the CSAT score. The following CSAT factors are associated with the Inferred CSAT score.

    • Resolution: Indicates whether the Virtual Agent or AI agent successfully resolved the user's issue without human intervention.
    • Confusion: Indicates how often the Virtual Agent or AI agent misunderstood or failed to interpret the user's intent.
    • Effort: Indicates the number of user turns or interactions required to reach a resolution.
    • Empathy: Indicates how well the Virtual Agent or AI agent acknowledged and responded to the user's emotional tone.
    • Next Steps: Captures whether the Virtual Agent or AI agent clearly communicated what the user should do next.
    • Frustration: Flags signs of user dissatisfaction or repeated failed attempts during the interaction.
    • Transfers and Escalations: Tracks how often the Virtual Agent or AI agent handed off the conversation to a human agent or another system.

    Inferred CSAT scores and factors are calculated for each conversation. Conversational analytics applications can leverage the scores written to the Conversation Insights [sn_aci_insights] table to create custom dashboards and workflows. The AI Agent Analytics dashboard includes visualizations with Inferred CSAT scores and factors by default.

    The data retention period for the Conversation Insights [sn_aci_insights] table is two years. For more information on creating custom dashboards, see Create a dashboard with the in-line editor and Select a table data source for a data visualization.

    Conversation Insights workflow

    The Conversation Insights workflow illustrates how each interaction, whether handled by Virtual Agent or an AI agent, is transformed into actionable insights. You can feed the insights directly into the dashboards for analysis and decision making. The Conversation Insights workflow shows the journey from conversations to insights on dashboards.

    Figure 1. Conversation Insights workflow
    Infographic shows how the Conversation Insights application derives insights from conversations. For the text description, refer to the description that follows.
    1. Conversation sources
      • Agentic AI chats
      • Virtual Agent chats
    2. Data aggregation
      • Agentic AI and Virtual Agent interactions are captured in the Conversation table [sys_cs_conversation].
      • The conversation transcript, including user query, agent response, timestamps, and metadata such as the session ID and channel type are also stored in the Conversation table for processing.
    3. Insight generation
      • The model analyzes the conversation transcript.
      • Inferred CSAT scores are generated for CSAT factors such as Empathy, Resolution, Frustration, and so on.
    4. Insights storage
      • Inferred CSAT scores and factors are stored in the Conversation Insights table [sn_aci_insights].
      • The Conversation Insights table acts as a structured repository for the extracted insights.
    5. Dashboards
      • The insights are made available to create adhoc dashboards and workflows.
      • You can explore trends, performance metrics, and target improvements based on the Inferred CSAT scores.

    Conversation Insights benefits

    Problem Solution
    Traditional surveys often reflect extreme opinions and low response rates. Inferred CSAT helps solve this problem by using AI to estimate CSAT score for conversations in real time, based on full conversation transcript. This CSAT score can help to eliminate bias and reduce the need for reliance on explicit survey feedback.
    Post-interaction feedback delays insights resulting in lagging indicators. CSAT scores are generated immediately after the interaction, enabling faster detection of issues and trends.
    Lack of actionable insight behind CSAT scores. CSAT factors like Resolution, Empathy, Effort, and so on explain user satisfaction or dissatisfaction, helping you to target improvements.

    What to explore next

    To learn more about configuring and using Conversation Insights, see: