Implement Automated Weekly Support Trend Analysis Using Rovo and Confluence

[Testing]

Summary

Implement Automated Weekly Support Trend Analysis Using Rovo and Confluence

Description

Objective

Implement an automated weekly trend-analysis capability for the Jira project using Atlassian Rovo, with the analysis published to the Customer Support Confluence space.

The solution should analyze recently created  support tickets, identify recurring customer issues and trends, highlight issues that may require Engineering escalation, and publish an executive-level weekly summary in Confluence.

Requirements

1. Jira Ticket Analysis

• Analyze tickets created within the previous 15 days.
• Use relevant ticket information such as:
  • Summary
  • Status
  • Issue Type
  • Priority
  • Labels
  • Components
  • Created Date
  • Resolution
• Avoid using the Description field unless specifically required, to prevent excessive data/context consumption.

2. Trend Identification

Rovo should:

• Group similar tickets into meaningful problem types/trends.
• Primarily use ticket summaries to identify recurring issues.
• Identify the Top 5 problem types by ticket volume.
• Provide the number of tickets associated with each problem type.
• Provide the relevant Jira ticket keys.
• Provide 2–3 representative ticket summaries for each problem type.
• Include a priority breakdown where applicable.

3. Engineering Escalation Assessment

For each of the Top 5 problem types, determine whether Engineering escalation is recommended.

The assessment should consider:

• Frequency/volume of the issue.
• Recurrence of the same technical problem.
• Customer/business impact.
• Production impact.
• Blocker or critical symptoms mentioned in the ticket summary.
• Whether the issue appears to be a routine support request versus a systemic technical problem.

Important: Jira Priority should not be treated as the sole indicator for escalation because priority data may not always accurately represent the actual severity of an issue. Potentially under-triaged blockers should be explicitly highlighted.

4. Confluence Reporting

Publish the weekly analysis to the Customer Support Confluence space.

The report should contain:

• Weekly reporting date.
• Top 5 problem types.
• Ticket count for each problem type.
• Engineering escalation recommendation.
• Relevant Jira ticket keys.
• Representative ticket summaries.
• Key observations/trends.
• Recommendations for Engineering.
• Any potentially under-triaged blocker or high-impact issue.
• Link to the source Jira search/JQL.

5. Automation

The solution should be designed to run automatically on a weekly schedule, minimizing manual intervention.

The proposed flow is:

Jira Tickets → Rovo Analysis → Identify Top 5 Trends → Assess Engineering Escalation → Generate Summary → Publish to Confluence

Expected Outcome

A repeatable automated weekly report that provides Support and Engineering teams with visibility into:

• The most common customer issues.
• Emerging or recurring technical trends.
• High-volume problem areas.
• Issues requiring Engineering attention.
• Potential blockers that may be incorrectly prioritized in Jira.

The solution should use Rovo and native Atlassian capabilities wherever possible, avoiding unnecessary external scripts or data-processing components.

[Giuseppe Miccoli]

Hi there!

This is an excellent initiative!

Automating your weekly support trend  using Atlassian Rovo and Confluence will drastically reduce manual overhead.

Atlassian Rovo within Jira Service Management and AI computational are great tools!