What it does
Analyses the week’s Zendesk tickets using AI to identify emerging trends, common issues, and priority problems, generating an executive summary report for support leadership.
Why I recommend it
Individual tickets are trees, but managers need to see the forest. AI trend analysis surfaces patterns that manual review misses, enabling proactive problem-solving before issues escalate.
Expected benefits
- Early detection of product issues
- Data-driven support prioritisation
- Better resource allocation
- Proactive customer communication
How it works
Weekly trigger -> fetch all resolved tickets from past 7 days -> AI analyses for themes, common issues, sentiment patterns -> generate summary with top issues, volume trends, resolution time changes -> email to support leadership with actionable insights.
Quick start
Manually review last week’s tickets and group by common themes. Track time spent. Ask ChatGPT to analyse a sample of ticket summaries and generate trend report. Once format works, automate the weekly analysis.
Level-up version
Compare to previous weeks to identify growing vs declining issues. Include customer sentiment scores. Link to product roadmap items. Predict ticket volume for next week. Auto-create Jira tickets for product issues exceeding threshold.
Tools you can use
Support: Zendesk, Freshdesk, Help Scout, Intercom
AI: Claude API, ChatGPT API for analysis
Reporting: Google Docs, Slack for distribution
Analytics: Zendesk Explore for baseline data
Automation: Zapier, Make, n8n
Also works with
Support platforms: Front, Gorgias, HubSpot Service Hub
BI tools: Tableau, Looker for deeper analysis
Product: Jira, Linear for bug tracking
Technical implementation solution
- No-code: Zapier weekly schedule -> Zendesk API fetch last 7 days tickets -> export to Google Sheets -> send summary to ChatGPT for trend analysis -> email formatted report.
- API-based: Weekly cron -> Zendesk API query tickets by date -> extract subjects and descriptions -> Claude API analyse for trends with structured output -> generate markdown report -> email via SendGrid + post to Slack.
Where it gets tricky
Distinguishing signal from noise in ticket data, ensuring AI accurately identifies meaningful trends vs random variation, presenting insights actionably, and tracking whether identified trends actually get addressed.
