Use Case - Advanced Status Labels for Confluence
Custom CSAT Dashboards in Jira with AI Sentiment Data
Visualize real-time customer sentiment in Jira dashboards to track satisfaction trends, improve support, and drive proactive service.
Use Case
Custom CSAT Dashboards in Jira with AI Sentiment Data
Challenge:
Enterprise support and service teams often lack a centralized, real-time overview of customer satisfaction trends within Jira Service Management. Without clear visibility into evolving customer sentiment, it’s difficult to measure team performance, identify emerging issues, or validate the impact of service improvements effectively.
How the Functionality Solves This:
AI Insights empowers organizations to build tailored customer satisfaction dashboards within Jira using existing dashboard and gadget frameworks. By leveraging AI Insights’ customer sentiment data, teams can add gadgets such as Filter Results, Filter Counts, and Issue Statistics to visualize key CSAT metrics. These dashboards aggregate sentiment scores from saved filters, allowing managers and agents to monitor satisfied, frustrated, or neutral customer interactions across projects in real-time. With configurable columns and multiple gadgets, the dashboard provides a holistic view—helping teams quickly identify patterns, prioritize interventions, and track the effectiveness of support strategies.
How It’s Better Than Default JSM Functionality:
Default Jira Service Management dashboards do not natively integrate customer emotional data or sentiment tracking, limiting insights to ticket counts and statuses. AI Insights extends Jira’s dashboard capabilities by embedding sophisticated, AI-driven sentiment analysis into familiar gadget components. This seamless integration means enterprises can gain deep visibility into customer feelings alongside operational metrics, without adopting additional tools or leaving Jira. The result is a powerful, consolidated view of CSAT that drives data-driven decisions, faster issue resolution, and improved customer experiences at scale.
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