--- sourceDocument: Australia IT Service Management sourceDocumentLink: https://www.servicenow.com/docs/r/australia/it-service-management Release : - australia ft:locale : - en-US ft:publication_title : - Australia IT Service Management ft:clusterId : - itsm bundleId : - itsm workflow : - Technology --- # Customer satisfaction # ITSM Virtual Agent Customer satisfaction analytics {#ariaid-title1} Release version: Australia Updated August 18, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of ITSM Virtual Agent Customer satisfaction analytics This functionality enables ServiceNow customers to track and analyze customer satisfaction (CSAT) related to ITSM Virtual Agent interactions. By leveraging AI-driven transcript analysis combined with direct user feedback, you can comprehensively evaluate the quality of customer experiences with both Virtual Agents and live agents. This insight helps you understand how well your support operations meet customer needs and identify areas for improvement. Show full answer Show less ## Key Features * **CSAT Metrics Comparison:** Analyze and compare satisfaction scores between ITSM Virtual Agent and live agents to pinpoint strengths, automation opportunities, or escalation adjustments. * **Trend Monitoring:** Track customer satisfaction trends over time to measure the impact of Virtual Agent enhancements or training initiatives. * **AI-Inferred CSAT Scores:** Utilize Large Language Model (LLM) analysis of conversation transcripts to infer satisfaction scores from 0 (low) to 5 (high), considering tone, sentiment, and resolution indicators. * **Direct User Feedback:** Collect and analyze explicit thumbs-up and thumbs-down feedback on chat messages, providing immediate insight into user satisfaction or failure points. * **Comprehensive Session Analysis:** Examine CSAT across all sessions---including those that start with the Virtual Agent and may escalate to live agents---for a holistic view of customer experience. * **Feedback Drill-Downs:** Filter and break down thumbs-up/down feedback by different interaction portals (e.g., ServiceNow Otto panel), enabling detailed source analysis. ## Key Outcomes * Gain a detailed understanding of customer satisfaction levels for Virtual Agent versus live agent engagements. * Identify problematic interactions through negative feedback and AI-inferred dissatisfaction to prioritize improvements. * Monitor the effectiveness of changes over time using trend data from both AI analysis and user feedback. * Support decision-making around automation enhancements and escalation protocols based on data-driven insights. * Improve overall support quality by leveraging a combined view of inferred sentiment and explicit user feedback across multiple channels. Track customer satisfaction through AI interaction transcript analysis and user feedback to gauge experience quality. Track comprehensive metrics to help you evaluate how well Virtual Agent and live agents are meeting customer needs. You can analyze both AI-driven analysis and direct user feedback. ## Track metrics for the Customer Satisfaction (CSAT) analytics - Overview {#now-assist-itsm-conversational-dashboard-customer-satisfaction__section_mt1_nq2_f3c} Analyze CSAT metrics by comparing ITSM Virtual Agent performance against live agents and also analyze customer satisfaction trends over time.{#now-assist-itsm-conversational-dashboard-customer-satisfaction__table_njf_4q2_f3c__entry__2} | Example of CSAT metrics | Description | |-|-| | Compare ITSM Virtual Agent performance against live agents. | Identify automation opportunities or areas where escalation protocols should be adjusted. | | Monitor satisfaction trends over time. | Measure the impact of ITSM Virtual Agent improvements or training updates. | | Analyze if the conversations had positive or negative feedback. | With negative feedback, shown as thumbs-down, you can understand failure points. | | Get CSAT metrics across different time periods. | Analyze CSAT performance at different times. | | Reviewing AI-inferred CSAT scores alongside explicit user feedback. | Gain comprehensive understanding of customer experience quality. | [ ] {#now-assist-itsm-conversational-dashboard-customer-satisfaction__table_njf_4q2_f3c} ## Customer satisfaction (CSAT) metrics {#now-assist-itsm-conversational-dashboard-customer-satisfaction__entry__14} | Indicators | Descriptions | |-|-| | Inferred CSAT: Virtual Agent | Average customer satisfaction score for chats handled solely by ITSM Virtual Agent, assessed through LLM analysis. Scale: 0, which is a low score to 5, which is a high score. This metric uses AI to analyze conversation transcripts and infer customer satisfaction based on tone, sentiment, and resolution indicators. | | Inferred CSAT: Live agents | Average customer satisfaction score for chats with live agents, assessed through LLM analysis. Scale: 0, which is a low score to 5, which is a high score. Provides comparison between ITSM Virtual Agent and human agent performance to identify strengths and improvement areas. | | Inferred CSAT: All sessions | Overall average customer satisfaction score across all sessions, which began with Virtual Agent and may or may not have escalated to a live agent. Measured through LLM analysis. Scale: 0, which is a low score to 5, which is a high score. This metric provides a holistic view of customer experience across your entire support operation. | [Table 1. Customer satisfaction indicators] ## Customer satisfaction - feedback metrics {#now-assist-itsm-conversational-dashboard-customer-satisfaction__section_k4j_cby_g3c} {#now-assist-itsm-conversational-dashboard-customer-satisfaction__table_r35_p1y_g3c__entry__2} | Indicators | Descriptions | |-|-| | Number of chat messages that received a thumbs up | The total count of messages in a chat that received positive feedback as thumbs up from users. This direct feedback metric indicates successful interactions where users explicitly expressed satisfaction. The trend line shows changes over the selected time period, helping identify improvements or issues affecting customer satisfaction. | | Number of chat messages that received a thumbs down | The total count of messages in a chat that received negative feedback as thumbs down from users. This metric highlights problematic interactions requiring investigation. Review associated conversations to understand root causes and implement improvements. The trend line helps track whether changes are reducing negative feedback over time. | [ ] {#now-assist-itsm-conversational-dashboard-customer-satisfaction__table_r35_p1y_g3c} You can drill down into the data by selecting the thumbs-up or thumbs-down icon and then selecting the filter icon in the KPI details page. You can then see the breakdown of the thumbs-up or thumbs-down feedback data based on the different portals, such as ServiceNow Otto panel or ServiceNow Otto for Virtual Agent, in which it was given.