Agent Analytics
The Agent Analytics Dashboard in Qiscus Helpdesk provides a detailed view of performance metrics for each individual support agent. This dashboard allows supervisors, admins, and owners to evaluate how each agent handles tickets over time, including response speed and resolution efficiency. It supports better resource planning, accountability, and recognition of top performers.
Access Permissions
Access to agent-level analytics is based on user roles:
- Owner, Admin, and Supervisor roles have full access. They can view the analytics of any agent by selecting from the agent list.
- Agent role users can only view analytics related to their own performance. They cannot view other agents’ data. Therefore, when logged in as an agent, the agent list sidebar and selector will be hidden, and the dashboard will display only their personal analytics.
Agent Selection and Division Filter
On the left sidebar, users can search and filter agents by their division.
- By default, the filter is set to “All Divisions”, showing the complete list of agents.
- When a specific division is selected, the agent list dynamically updates to show only agents who belong to that division.
- This helps supervisors or admins focus on monitoring team performance within a particular organizational unit.
Date Filter
The Date Filter at the top of the dashboard allows users to analyze agent performance for a specific timeframe (e.g., This Year, Last Month). All metrics and graphs on the dashboard will adjust according to the selected period.
Dashboard Metrics
The dashboard is divided into several key performance areas:
A. Ticket Statistics
This section helps you understand how many tickets were assigned and resolved by the selected agent during the selected time period. It includes both total counts and visual trends to support performance assessment over time.
| Metric | Description |
|---|---|
| Tickets Handled in This Period | The total number of tickets that were created and assigned to the agent within the selected timeframe. This metric tracks direct ticket ownership. |
| Graph of Daily Handled Tickets | A line graph showing the number of tickets created and assigned to the agent per day. It helps visualize the agent's daily ticket intake. |
| Tickets Resolved in This Period | The number of tickets that were resolved by the agent during the selected timeframe. |
| Graph of Daily Resolved Tickets | A line graph showing the number of tickets resolved by the agent each day. Useful for analyzing resolution trends. |
B. Response Time
This section provides insights into the agent’s communication speed, both for the initial response and throughout the entire ticket lifecycle. These metrics are important for evaluating SLA compliance and customer experience.
| Metric | Description |
|---|---|
| Agent Average First Response Time | The average time taken by the agent to send their first reply after a ticket is created. Key for measuring responsiveness. |
| Agent Average Response Time | The average duration between all responses sent by the agent during a ticket’s lifecycle. Reflects ongoing communication pace. |
C. Resolution Time
This section helps evaluate how efficiently the agent resolves customer issues. Longer resolution times may indicate complex cases or capacity issues.
| Metric | Description |
|---|---|
| Agent Average Resolution Time | The average time the agent takes to fully resolve tickets from creation to closure. Helps identify complexity or performance gaps. |
Exporting Data
- Users can export the entire dashboard view as a PDF by clicking the "Export as PDF" option at the top-right of the dashboard.
- This is useful for performance reviews, reporting, or sharing analytics with external stakeholders.
Summary
The Agent Analytics Dashboard is designed to support transparent and data-driven performance monitoring at the individual level. With built-in access control, real-time division-based filtering, and visual trend tracking, it empowers supervisors and administrators to make informed decisions while ensuring agents can only access data relevant to themselves.