Exploring Conversation Insights

  • Release version: Australia
  • Updated March 12, 2026
  • 4 minutes to read
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    Summary of Exploring Conversation Insights

    Conversation Insights in ServiceNow leverages AI to provide real-time Inferred Customer Satisfaction (CSAT) scores and explanatory factors for interactions handled by Virtual Agent and agentic AI workflows. These insights are derived solely from conversation transcripts without requiring direct user input, enabling you to gauge customer satisfaction with a score ranging from 1 (least satisfied) to 5 (most satisfied).

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    This capability helps ServiceNow customers improve Virtual Agent and live agent experiences by delivering actionable insights into conversation quality and user sentiment immediately after interactions.

    Key Features

    • Inferred CSAT Scores: AI-generated satisfaction ratings based on full conversation transcripts, updated in real time.
    • CSAT Factors: Detailed contributing elements such as Resolution, Confusion, Effort, Empathy, Next Steps, Frustration, and Transfers/Escalations that explain why a particular score was assigned.
    • Data Storage and Retention: Insights are stored in the Conversation Insights [snaciinsights] table with a two-year retention period, allowing historical analysis.
    • Dashboard Integration: Out-of-the-box visualization of CSAT scores and factors is available in the AI Agent Analytics dashboard, with options for creating customized dashboards and workflows based on this data.
    • Support for Multiple Channels: Conversation Insights supports Virtual Agent, Voice Agents, Now Assist Panel, and Now Assist Virtual Agent, with dedicated dashboards for each.
    • Inferred CSAT by Intent: Enables segmentation of satisfaction scores by customer intent, helping you identify and address issues related to specific customer needs, with automatic updates based on conversation volume.

    How Conversation Insights Works

    • Conversation Capture: Transcripts and metadata from Virtual Agent and agentic AI interactions are collected in the Conversation table [syscsconversation].
    • Insight Generation: AI models analyze transcripts to generate CSAT scores and associated factors.
    • Insight Storage: Generated insights are stored in the Conversation Insights table [snaciinsights].
    • Dashboard Visualization: Insights are accessible via dashboards for trend analysis, performance measurement, and targeted improvements.

    Benefits for ServiceNow Customers

    • Eliminates Survey Bias: Automated CSAT scoring reduces reliance on explicit surveys, which often suffer from low response rates and biased extremes.
    • Real-Time Feedback: Immediate availability of satisfaction scores accelerates issue detection and response.
    • Actionable Insights: Detailed CSAT factors provide clarity on specific areas needing improvement, enabling focused enhancements in Virtual Agent and agent-assisted experiences.

    Next Steps

    To maximize the value of Conversation Insights, you can proceed with installing and configuring the Conversation Insights application and explore reference materials for advanced usage and customization. This will enable you to tailor dashboards and workflows to your organization's specific needs and improve overall customer engagement and satisfaction.

    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 are also supported by Voice Agents, Now Assist Panel and Now Assist Virtual Agent.

    To view the conversation insights for Voice Agents, go to Assistant Designer > Analytics > Sentiment dashboard.

    To view the conversation insights for Now Assist Panel or Now Assist Virtual Agent, go to Platform Analytics > Dashboards > AI Engagement Analytics.

    Inferred CSAT segmented by intent

    Use the additional insights introduced to report on Inferred CSAT segmented by intent. It gives better insights into why customers contact the brand and how well their needs are met. This helps companies quickly spot and fix issues with their top customer intents.

    The score for Inferred CSAT segmented by intent is automatically generated when customers upgrade the Conversation Insights app. It requires a minimum sample size of 500 conversations to start and samples up to 2000 records every 15 minutes.

    These metrics are available in the AI Agent Analytics dashboard, under the Insights tab, where you can find two new charts.

    You can also explore the conversational insights for Now Assist Panel and Now Assist Virtual Agent. To view the dashboard, go to Dashboards > AI Engagement Analytics > Platform Analytics section or Dashboards > AI Engagement Analytics > Voice Agents Agents section.

    Additionally you can also go to Assistant Designer > Analytics > Sentiment dashboard to view these insights.

    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: