ITSM Virtual Agent chat analytics

Track closed chats, user engagement patterns, and abandonment rates to measure demand and identify opportunities to improve ITSM Virtual Agent effectiveness.

The Chat analytics tab provides metrics on chat volume, user engagement, abandonment patterns, and departmental distribution. Use these insights to evaluate support demand, identify peak usage periods, and understand where users need assistance.

Track metrics for the Chat analytics - Overview

Analyze chat metrics to understand support demand and user engagement patterns.

Example of chat metrics	Description
Monitor chat volume and user engagement	Track total closed chats and unique users over time to identify peak usage periods and plan capacity.
Analyze abandonment patterns	Review chat abandonment rates to identify potential user experience issues.
Segment performance by dimension	Filter data by date range, caller company, user's department, user's location, handler type, and communication channels.
Compare daily chat volume to unique user counts	Determine whether high volumes represent many users with issues or repeated interactions from fewer users.
View departmental and geographical distribution	Verify that ITSM Virtual Agent capabilities align with demand patterns across your organization.
Track trends over time	Measure the impact of ITSM Virtual Agent enhancements or process improvements.
Select specific data points in visualizations	Drill down to underlying conversation details for deeper analysis.

Filter Chat analytics data

Apply filters to analyze chat data for specific time periods, organizations, or user segments.

• To analyze a date range, select the Date list, select the start and end dates, and select Apply.
• To filter by other dimensions, double-click one or more items in a category to move them from the Available list to the Applied list, then select Apply.

  Filter options include Caller company, User's department, User's location, Handled by, and Channels.

Note:

When location represents a user's personal address rather than a business address, location-based analysis may not provide valuable insights.

Chat analytics—Usage and success

The Usage and success section displays overall chat volume and resolution metrics.

Table 1. Usage and success metrics

Widget	Description
Closed chats	Total closed customer chats within the selected date range. This metric represents overall support demand across all channels and handler types. The trend line identifies patterns in support volume, such as seasonal variations or the impact of product releases. Use this data to understand total workload and plan resource allocation.

Chat analytics—Engagement over time

The Engagement over time section displays daily chat volume, unique user counts, and abandonment rates.

Table 2. Engagement over time metrics

Widget	Description
Number of chats over time - Daily count	Daily chat volume over time. This time-series visualization shows daily chat patterns, identifying peak usage times, day-of-week trends, seasonal variations, and the impact of external events. Use this data to plan staffing levels and understand when users most need support.
Number of unique users over time - Daily count	Daily unique users engaged in chat interactions. This metric shows distinct users rather than total conversations. Comparing unique users to total chats reveals whether issues affect many users or whether some users have repeated interactions.
Chat abandonment rate	Percentage of chats abandoned by users before resolution. The trend line shows abandonment patterns over time. High abandonment rates may indicate issues with conversation length, complexity, or user experience. Monitor this metric to identify and address user experience problems.

Chat analytics—Chat distribution

The Chat distribution section displays chat volume by department and user location.

Table 3. Chat distribution metrics

Widget	Description
Top chats by department	Chat volume by department. This bar chart shows which departments handle the most support conversations. Use this data to understand departmental support loads, identify departments that could benefit from enhanced ITSM Virtual Agent capabilities, and verify that resources align with demand patterns.
Top chats by user's location	Chat volume by user location. This visualization shows support demand by location. Use this data to understand global support patterns, plan regional coverage and language support, identify location-specific issues, and verify that ITSM Virtual Agent content addresses regional needs.