Popular Articles Report

From data to decisions: A deep dive into ticketing system reports

Content management teams can use various metrics to assess the effectiveness of knowledge base articles, improve content quality, and ensure articles are regularly updated. Predefined article reports allow organizations to identify the most popular and frequently suggested articles and evaluate the usefulness of the content for both users and support teams. 

Analyzing metrics such as the most-liked and most-disliked articles provides additional insights into the relevance and quality of the information provided. This approach not only enhances user self-service capabilities but also reduces the volume of incoming support tickets.

What are article-based reports?

Article-based reports use the Knowledge Base module as their data source and display metrics such as views, feedback, and ticket usage. They help content owners, support leads, and managers understand which articles are helping ticket deflection and where documentation needs improvement.

You can access article-based reports from the Analytics/Reports section by selecting the Articles module or utilizing the predefined Article Reports. Additionally, the Knowledge Base Dashboard provides a visual representation of article metrics, including overall traffic and engagement trends.

These article-based reports show you how useful and engaging each knowledge base article is. You can use this information to improve self-service content based on honest customer feedback and usage.

This report showcases the articles that have garnered the most likes from readers in your Help Centre or knowledge base. It clearly identifies the content customers find most helpful or satisfactory, allowing you to pinpoint exemplary articles, adopt their format, and promote them more effectively, such as by featuring them in FAQs or onboarding processes.

Popular Articles 

The popular articles feature typically ranks content based on engagement metrics, such as views and likes, to highlight the articles that are most frequently visited and appreciated by users in the Help Centre. This allows for the effective promotion of high-impact content to end users, such as showcasing a "Popular Articles" section on the portal's homepage. Additionally, it provides your team with insights into which topics are currently in demand, enabling you to tailor new content or automation (such as Zia suggestions and related articles) to address those areas better.

Key article-based report metrics include:

• Popular or most-viewed articles over a selected period
• Articles most frequently suggested or used in ticket responses (via KB suggestions)
• Article feedback, like likes/dislikes, ratings, or usefulness
• Article status and ownership (published/draft, author, category, last updated date)

How the reports are typically used

1. Identify high-impact articles that reduce ticket volume and replicate their structure or style.
2. Find gaps (topics with low article coverage but high ticket volume) and prioritize new documentation for them.
3. Monitor content hygiene by tracking stale articles, low-performing content, and ownership for periodic reviews.
   

Conclusion

To boost content quality and engagement, use "most liked" articles as a model for structure and language, and apply this to new or underperforming article pieces. Identify "most disliked" items to create a backlog for improvement, and after making edits, track metrics (likes, dislikes, views) to assess impact. Finally, leverage "popular articles" to decide what to feature in the Help Center, convert into videos or FAQs, and integrate into ticket templates and email responses.

 

Please stay tuned for more in the Desk Reports series.

 

Regards,

 

Kavya Rao

The Zoho Desk Team

Also read:

Time-based reports

• Resolution Time Report
• Response Time Report
• SLA Ticket Report

Agent-based reports

• Agent Availability Report
• Agent Performance Report

Ticket-based reports

• Lifecycle Reports
• Incoming Threads Report
• Ticket Stages Report
  

• Topic Participants

• Kavya Rao Addepalli

  

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