Use Case - Advanced Status Labels for Confluence
Filter Jira Issues by Customer Sentiment with AI Insights
Use real-time emotion-based filters in JQL to prioritize support, prevent escalations, and improve customer outcomes at scale.
Use Case
Filter Jira Issues by Customer Sentiment with AI Insights
Challenge:
Enterprise support teams often struggle to quickly identify and prioritize customer issues based on emotional context within Jira Service Management. Traditional filtering focuses on status or priority but doesn’t provide insights into how customers feel, which is critical for timely and effective intervention.
How the Functionality Solves This:
AI Insights introduces a custom “Customer Sentiment” field that integrates seamlessly with Jira’s powerful JQL search engine. Teams can create precise queries to filter issues by customer sentiment—such as all work-items with frustrated customers, or all work-items with excited customers assigned to specific agents—across projects or the entire organization. These filters enable managers and agents to focus on cases requiring urgent attention or escalation, proactively engage dissatisfied customers, or recognize and amplify positive experiences. Saving these queries as reusable filters provides a consistent way to monitor sentiment trends over time and ensures no critical issue slips through the cracks.
How It’s Better Than Default JSM Functionality:
While Jira Service Management offers robust issue filtering by status, priority, or assignee, it lacks native insight into customer emotions within those issues. AI Insights enhances this by making real-time sentiment data directly accessible via JQL, empowering enterprise teams to filter and act based on nuanced customer feelings—not just technical attributes. This capability transforms customer sentiment into a practical, searchable attribute, driving smarter prioritization, faster response, and ultimately better service outcomes.
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