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Ticket-Based Knowledge Search

This guide explains how to implement and optimize ticket-based knowledge search, which automatically suggests relevant knowledge articles to support agents based on ticket content.

Ticket-based search is an AI-powered feature that:

  • Automatically analyzes incoming support tickets
  • Identifies key topics, issues, and intent
  • Searches the knowledge base for relevant articles
  • Presents suggestions to agents in real-time
  • Enables quick resolution using existing knowledge

Prerequisites​

Before configuring ticket-based search, ensure you have:

  • A well-populated knowledge base
  • Integration with your ticketing system
  • Admin access to Knowledge Management
  • Support agent permissions

Integration Configuration​

Connect your ticketing system:

  1. Navigate to Admin > Integrations > Ticketing Systems
  2. Select your ticketing platform (ServiceNow, Zendesk, Salesforce, etc.)
  3. Configure the integration:
    • API credentials
    • Webhook endpoints
    • Field mappings
    • Sync frequency
  4. Test the connection
  5. Enable ticket-based search

Search Configuration​

Customize the search behavior:

  1. Go to Admin > Search > Ticket-Based Search
  2. Configure search settings:
    • Relevance threshold (minimum confidence score)
    • Maximum number of suggestions
    • Search scope (categories to include/exclude)
    • Weighting factors (title, description, customer responses)
  3. Set up result filtering:
    • By audience (match ticket requester attributes)
    • By product (match ticket product field)
    • By content type (articles, guides, FAQs)
  4. Save your configuration

Agent Experience​

Viewing Suggested Articles​

Agents will see suggested articles in their ticketing interface:

  1. When viewing a ticket, the Knowledge panel appears automatically
  2. Suggested articles are ranked by relevance
  3. Each suggestion includes:
    • Article title
    • Relevance score
    • Brief excerpt
    • Last updated date
  4. Agents can click to view the full article

Searching from Tickets​

Agents can also perform manual searches:

  1. In the Knowledge panel, click Search
  2. The search is pre-populated with ticket context
  3. Agents can modify the search terms
  4. Results are filtered based on ticket context
  5. Agents can sort and filter results

Using Knowledge in Responses​

Incorporate knowledge into ticket responses:

  1. View a suggested article
  2. Click Use in Response to:
    • Insert the full article
    • Insert a specific section
    • Insert with a link to the article
  3. Customize the inserted content
  4. Send the response to the customer

Providing Feedback​

Improve search quality through feedback:

  1. For each suggestion, agents can:
    • Mark as helpful
    • Mark as not relevant
    • Provide specific feedback
  2. This feedback trains the AI to improve future suggestions

Advanced Features​

Automatic Article Creation​

Generate new knowledge from tickets:

  1. Enable Knowledge Capture in settings
  2. The system identifies tickets without matching articles
  3. After resolution, agents are prompted to create an article
  4. The system pre-populates content based on the ticket
  5. Agents review, edit, and publish the new article

Proactive Suggestions​

Get suggestions before customers submit tickets:

  1. Enable Proactive Knowledge in settings
  2. When customers start typing in web forms or chat:
    • The system analyzes the partial content
    • Suggests relevant articles in real-time
    • Offers self-service options
  3. If customers resolve their issue, no ticket is created

Multilingual Support​

Handle tickets in multiple languages:

  1. Go to Admin > Search > Language Settings
  2. Enable multilingual ticket analysis
  3. Configure language detection
  4. Set up cross-language search (find English articles for Spanish tickets)
  5. Enable automatic translation of suggestions

Analytics and Optimization​

Performance Metrics​

Track the effectiveness of ticket-based search:

  1. Navigate to Analytics > Ticket Search
  2. View key metrics:
    • Suggestion accuracy rate
    • Agent utilization rate (% of suggestions used)
    • Time saved per ticket
    • Knowledge gaps identified
    • Articles created from tickets
  3. Filter by time period, agent team, or ticket category

Content Optimization​

Improve content based on ticket search data:

  1. Go to Analytics > Content Optimization
  2. Review the Ticket Impact report
  3. Identify:
    • High-performing articles in ticket resolution
    • Articles that need improvement
    • Missing content based on ticket topics
  4. Use the Optimize button for AI-suggested improvements

A/B Testing​

Test different search configurations:

  1. Navigate to Admin > Search > A/B Testing
  2. Create a new test:
    • Define test variants (different relevance algorithms)
    • Set test duration and scope
    • Define success metrics
  3. Run the test
  4. Review results and implement the winning configuration

Integration with Agent Workflows​

Ticket Categorization​

Use knowledge to improve ticket routing:

  1. Enable Smart Categorization in settings
  2. The system suggests categories based on knowledge matches
  3. Tickets are automatically tagged and routed
  4. Agents can verify and adjust categorization

Knowledge-Driven Automation​

Automate responses using knowledge:

  1. Go to Admin > Automation > Knowledge Rules
  2. Create rules based on knowledge matches:
    • If high-confidence match found, suggest auto-response
    • For specific article matches, trigger workflows
    • When no matches found, escalate to specialists
  3. Set confidence thresholds for each automation
  4. Monitor and refine automation performance

Best Practices​

Content Optimization​

  • Keep articles concise: Agents need quick answers
  • Use clear titles: Make the problem and solution obvious
  • Include troubleshooting steps: Provide actionable information
  • Update regularly: Ensure content reflects current products and policies
  • Add agent-only sections: Include internal notes and escalation paths

Search Tuning​

  • Start with higher relevance thresholds: Begin with quality over quantity
  • Analyze search logs: Identify patterns in successful suggestions
  • Adjust weightings: Tune the importance of different ticket fields
  • Create specialized indexes: For different product lines or customer segments
  • Regular retraining: Update the AI model with new feedback data

Agent Training​

  • Provide search training: Ensure agents know how to use and refine suggestions
  • Encourage feedback: Create a culture of continuous improvement
  • Recognize knowledge contributors: Reward agents who create valuable articles
  • Share success stories: Highlight cases where knowledge improved resolution

Troubleshooting​

Common Issues​

IssueSolution
Irrelevant suggestionsIncrease relevance threshold and provide feedback
No suggestions appearingCheck integration status and search scope
Slow suggestion loadingOptimize indexing and check API rate limits
Duplicate suggestionsReview and merge similar articles

Next Steps​