We’re introducing new Drill-In Capabilities and Tickets Table Column Sets in Qvasa to make ticket analysis faster and more efficient.

New Drill-In Features
The Drill-in experience has been improved. The inspect tickets lightbox now has the following features:

• Select and order desired table columns
• Open an inspect lightbox to explore up to 500 tickets at a time without pagination
• Sort columns directly in the inspect lightbox
• Download sorted CSV from the lightbox

Prebuilt Column Sets
Admins can now pre-configure ticket table column sets to streamline analysis. Column sets can be:

• Created and managed on the Tickets Table Configurations page.
• Applied at the account level, dashboard level, or individual widget level
• Add ticket attributes as tickets table columns
• Add all AI workflow based attributes as tickets table columns

These updates provide faster insights, easier data exploration, and more flexibility when digging into underlying ticket data.

For more information refer to these knowledge base articles: Ticket-and-Agent-Drill-in-Capabilities-with-Configurable-Column-Sets and Configurable-Ticket-Table-Column-Sets.

We’re introducing Inferred CSAT, an AI workflow that automatically analyzes 100% of support tickets upon closure to estimate how customers might have rated their satisfaction with the service they received. This feature provides a new way to measure customer sentiment without relying on surveys.

How Inferred CSAT Works

The workflow reviews support interactions and determines the likely customer satisfaction level based on the following categories:

1. Satisfied
   The customer’s needs were met, and they appear content with the resolution.
2. Neutral
   The customer was indifferent or their satisfaction is unclear.
3. Dissatisfied
   The customer’s needs were not met, and they seem unhappy with the resolution.
4. Unknown
   There isn’t enough conversation data to assess satisfaction. This ensures accuracy by preventing guesses.

Measuring Inferred CSAT

Inferred CSAT calculates satisfaction as:
Satisfied / (Dissatisfied + Satisfied)

Addressing Dissatisfaction

When a ticket is categorized as Dissatisfied, the workflow runs an additional AI process to identify reasons for the dissatisfaction. These reasons are grouped and tracked over time, enabling organizations to visualize trends and address recurring issues.

Key Benefits

• Comprehensive Coverage: Measure customer satisfaction on every ticket.
• Actionable Insights: Understand what’s causing dissatisfaction and use those insights to improve service.
• Accurate Data: Ensure reliability by categorizing unclear cases as Unknown, avoiding speculative results.

Inferred CSAT helps businesses better understand and improve customer satisfaction by providing detailed, actionable data on every ticket. Contact us to learn more and activate this workflow in your system.

We're thrilled to introduce the Product Contact Reasons workflow—a powerful AI-driven workflow designed to save you time and deliver organized actionable insights without needing to read support conversations. Whether you're:

• Sharing top product issues with your product team,
• Optimizing your bot or knowledge base by identifying common areas of customer confusion, or
• Streamlining workflows by addressing repetitive agent interventions,

this workflow cuts through the noise to surface the information you need, tailored to your unique goals.

How It Works

The Product Contact Reasons workflow uses AI to analyze ticket conversations upon closure, automatically categorizing them into one of four key categories:

1. Product Deficiencies
   Pinpoint bugs or product limitations that require escalation or prioritization. Use these insights to guide fixes and feature improvements.
2. Product Confusions
   Highlight instances where customers needed additional documentation or support. Empower your team to enhance self-service options and improve customer education.
3. Agent Interventions
   Identify cases where agents performed actions unavailable to customers. Discover opportunities to automate repetitive tasks and optimize workflows.
4. Uncategorized
   Tickets that do not fit into the other categories.

After categorization, the workflow activates a generative AI process to uncover specific details about the underlying issue—whether it's a deficiency, confusion, or intervention. Qvasa's generative workflows can reuse existing tags or create new ones, surfacing novel trends and insights from ticket content.

Why It Matters

This out-of-the-box workflow transforms raw support data into meaningful analytics, empowering you to:

• Share targeted, actionable reports with different teams.
• Identify and address trends before they become widespread issues.
• Save time by automating the categorization and tagging process.

Getting Started

To maximize accuracy, users can seed the system with known deficiencies, confusions, and interventions. Over time, this makes the AI smarter, delivering even greater value to your analytics.

Please contact the Qvasa team if you are interested in enabling this feature for your account.