ITSM Virtual Agent Customer satisfaction analytics
Summarize
Summary of ITSM Virtual Agent Customer satisfaction analytics
ITSM Virtual Agent Customer Satisfaction (CSAT) analytics enables you to track and evaluate customer satisfaction by analyzing AI interaction transcripts alongside direct user feedback. This dual approach helps gauge the quality of customer experiences with both the Virtual Agent and live agents, providing insights into how well customer needs are being met.
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Key Features
- Comprehensive CSAT Metrics: Analyze customer satisfaction scores inferred by AI (using large language model analysis) for Virtual Agent-only chats, live agent chats, and all sessions combined, offering a holistic view of support performance.
- Direct User Feedback Tracking: Collect and analyze explicit user feedback through thumbs-up and thumbs-down reactions on chat messages, enabling identification of successful interactions and points of failure.
- Performance Comparison: Compare ITSM Virtual Agent satisfaction scores against live agents to identify strengths, improvement areas, and automation opportunities.
- Trend Analysis Over Time: Monitor satisfaction trends to measure the impact of improvements, training, or protocol changes, and track progress in reducing negative feedback.
- Drill-Down Capability: Filter feedback data by interaction channels such as Now Assist panel or Virtual Agent, allowing targeted analysis of customer satisfaction across different portals.
Key Outcomes
- Gain a comprehensive understanding of customer experience quality across automated and human support channels.
- Identify failure points and areas needing escalation or protocol adjustment through negative feedback analysis.
- Measure the effectiveness of Virtual Agent improvements and training by tracking CSAT trends.
- Leverage AI-driven sentiment analysis to complement explicit user feedback for a nuanced view of customer satisfaction.
- Make informed decisions to optimize support operations, improve automation, and enhance customer engagement based on data-driven insights.
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
| 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. |
Customer satisfaction (CSAT) metrics
| 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. |
Customer satisfaction - feedback metrics
| 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. |
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 Now Assist panel or Now Assist in Virtual Agent, in which it was given.