Support Ticket Auto-Categorization System Designer
Design an AI-powered support ticket categorization and routing system that automatically tags, prioritizes, and routes incoming tickets to the right team based on content analysis.
Prompt Template
Help me design an automated support ticket categorization and routing system for my team. **Product/service type:** [e.g., SaaS platform, e-commerce, mobile app] **Current ticket volume:** [tickets per day/week] **Support channels:** [e.g., email, chat, in-app, social media] **Current categories/tags (if any):** [list existing categories] **Support team structure:** [e.g., Tier 1 general, Tier 2 technical, Tier 3 engineering] **Helpdesk tool:** [e.g., Zendesk, Intercom, Freshdesk, Help Scout] **Top 5 most common ticket types:** [list them] **Current pain points:** [e.g., misrouted tickets, slow first response, inconsistent tagging] Please provide: 1. **Category taxonomy** — a hierarchical tagging system (primary category → subcategory → issue type) with 15-25 total leaf categories 2. **Priority scoring matrix** — rules for auto-assigning priority (P1-P4) based on keywords, customer tier, sentiment, and issue type 3. **Routing rules** — which team/agent gets each category, with escalation triggers 4. **AI classification prompt** — a ready-to-use prompt template that takes a ticket subject + body and outputs category, priority, sentiment, and suggested first response 5. **Edge case handling** — how to handle ambiguous tickets, multi-issue tickets, and tickets in other languages 6. **Success metrics** — KPIs to track classification accuracy, routing efficiency, and time savings 7. **Implementation roadmap** — phased rollout plan with human-in-the-loop validation
Example Output
🏷️ Ticket Auto-Categorization System Design
Product: CloudSync SaaS Platform
1. Category Taxonomy
2. Priority Scoring Matrix
| Signal | P1 (Critical) | P2 (High) | P3 (Medium) | P4 (Low) |
|--------|--------------|-----------|-------------|----------|
| Keywords | "data loss", "security breach", "can't access" | "error", "broken", "not working" | "how to", "question", "help" | "suggestion", "nice to have" |
| Customer tier | Enterprise | Business | Pro | Free |
| Sentiment | Very negative | Negative | Neutral | Positive |
| Auto-escalate | Yes → Tier 3 | After 2hr | After 8hr | After 24hr |
3. AI Classification Prompt
4. Success Metrics
| KPI | Target | Measurement |
|-----|--------|-------------|
| Classification accuracy | >90% | Weekly audit of 50 random tickets |
| Routing accuracy | >85% | % tickets not re-routed after assignment |
| First response time | -30% | Compare pre/post automation |
| Agent handle time | -15% | With pre-filled context from AI |
Tips for Best Results
- 💡Start with human-in-the-loop: have agents confirm or correct AI classifications for the first 2 weeks to build training data
- 💡Set a confidence threshold (e.g., 0.8) — below that, flag for manual review instead of auto-routing
- 💡Multi-issue tickets are common; design your system to apply multiple tags, but route based on the highest-priority issue
- 💡Review and update your taxonomy quarterly as product features and common issues evolve
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