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Taxonomy Design

Design and structure your ticket classification taxonomy for effective automated tagging and categorization.

Taxonomy Design

Learn how to design an effective taxonomy for automated ticket tagging and classification.

Overview

A well-designed taxonomy is the foundation of successful automated ticket tagging. Your taxonomy should reflect your organization’s structure, processes, and ticket handling requirements.

Taxonomy Principles

Keep It Simple

Start with a manageable number of categories:

  • Initial rollout: 5-10 main categories
  • Mature system: 15-25 categories maximum
  • Too many categories: Reduces classification accuracy

Make It Distinct

Categories should be clearly differentiated:

  • Avoid overlapping definitions
  • Use clear, descriptive names
  • Define explicit scope for each category
  • Document edge cases and boundary conditions

Align with Business Processes

Your taxonomy should match how your organization works:

  • Reflect existing team structures
  • Map to support queues or departments
  • Consider SLA requirements
  • Account for escalation paths

Taxonomy Structure

Hierarchical Taxonomy

Organize categories in a tree structure:

IT Support
├── Hardware
│   ├── Desktop Issues
│   ├── Laptop Issues
│   └── Peripherals
├── Software
│   ├── Application Errors
│   ├── License Requests
│   └── Installation Support
└── Network
    ├── Connectivity Issues
    ├── VPN Access
    └── WiFi Problems

Advantages:

  • Clear organization
  • Supports multi-level classification
  • Easy to understand and maintain

Disadvantages:

  • More complex to implement
  • Requires careful training data preparation
  • May need multi-stage classification

Flat Taxonomy

Single-level categories:

- Hardware Issues
- Software Errors
- Network Problems
- Access Requests
- Password Resets
- Account Management
- Email Issues
- Printer Support

Advantages:

  • Simple to implement
  • Easier to train models
  • Faster classification

Disadvantages:

  • Limited granularity
  • May become unwieldy with many categories

Defining Categories

Category Definition Template

For each category, document:

## Category Name: [Name]

**Description**: [Brief description of what belongs in this category]

**Scope**: [What is included]

**Examples**:

- Example ticket 1
- Example ticket 2
- Example ticket 3

**Exclusions**: [What is NOT included]

**Keywords**: [Common terms associated with this category]

**Priority**: [Typical priority level]

**Target Queue/Team**: [Where these tickets should be routed]

Example Category Definition

## Category Name: Password Reset

**Description**: Requests to reset forgotten passwords or unlock locked accounts

**Scope**:

- Forgotten password requests
- Account lockouts due to failed login attempts
- Password expiration issues

**Examples**:

- "I forgot my password and can't log in"
- "My account is locked after too many failed attempts"
- "I need to reset my password for the customer portal"

**Exclusions**:

- New account creation (→ Account Management)
- Permission/access level changes (→ Access Requests)
- Password policy questions (→ General IT Support)

**Keywords**: password, reset, locked, unlock, forgot, login, access

**Priority**: Medium (affects productivity)

**Target Queue/Team**: IT Help Desk

Testing Your Taxonomy

Manual Classification Test

Before automating, test your taxonomy manually:

  1. Sample Selection: Select 100-200 recent tickets
  2. Multiple Reviewers: Have 2-3 people classify the same tickets
  3. Measure Agreement: Calculate inter-rater agreement
  4. Target Agreement: Aim for >80% agreement between reviewers

Common Issues

Low Agreement (<60%):

  • Categories may overlap too much
  • Definitions may be unclear
  • Need better examples and documentation

Medium Agreement (60-80%):

  • Some edge cases need clarification
  • May need to merge similar categories
  • Refinement of category boundaries needed

High Agreement (>80%):

  • Taxonomy is ready for automation
  • Proceed with model training

Iteration and Refinement

Start Small

Begin with core categories:

Phase 1 (Week 1-2):
- 5-7 most common ticket types
- Cover 60-70% of ticket volume

Phase 2 (Week 3-4):
- Add 5-7 more categories
- Target 80-90% coverage

Phase 3 (Month 2+):
- Fine-tune and add edge cases
- Achieve >90% coverage

Monitor and Adjust

After deployment, continuously monitor:

  • Classification accuracy: By category
  • Confidence scores: Identify uncertain classifications
  • Misclassifications: Look for patterns
  • New ticket types: Identify gaps in taxonomy

When to Split Categories

Consider splitting when:

  • Category represents >20% of all tickets
  • Clear subcategories exist
  • Different handling processes needed
  • SLA requirements differ

When to Merge Categories

Consider merging when:

  • Category has <2% of tickets
  • Categories are frequently confused
  • Similar handling processes
  • Same team handles both

Best Practices

DO ✅

  • Start with business-driven categories
  • Document each category clearly
  • Test manually before automating
  • Iterate based on feedback
  • Monitor performance continuously

DON’T ❌

  • Create too many categories initially
  • Use vague or overlapping definitions
  • Skip manual validation
  • Set and forget - taxonomies evolve
  • Ignore low-confidence predictions

Tools and Resources

Documentation Template

Use a shared document to define your taxonomy:

docs/
├── taxonomy/
│   ├── overview.md
│   ├── categories/
│   │   ├── hardware.md
│   │   ├── software.md
│   │   └── ...
│   └── changelog.md

Review Schedule

  • Weekly: Review misclassifications
  • Monthly: Analyze category distribution
  • Quarterly: Major taxonomy review
  • Annually: Complete taxonomy redesign consideration

Next Steps

After designing your taxonomy:

  1. Prepare Training Data: Gather and label historical tickets
  2. Configure Model: Set up classification model with your categories
  3. Test Classification: Validate on hold-out data
  4. Monitor Performance: Track accuracy and adjust as needed