Overview
The hTech AI Ticket Assistant includes a detailed Usage and Analytics system that helps you understand how the AI is performing across your WHMCS environment. Analytics provide visibility into automated replies, ticket categories, customer sentiment, model usage, and more. This guide explains the available analytics sections and how to interpret them.
Where to Find the Analytics Dashboard
You can access usage and analytics inside the module:
- Log into WHMCS admin
- Select Addons
- Click "hTech AI Ticket Assistant"
- Open the Dashboard or Analytics tab
All analytics are displayed in easy-to-read charts, tables, and summaries.
AI Usage Overview
This section shows how many AI calls your system has used in the current billing period. It helps you monitor consumption and avoid usage limits.
Metrics may include:
- Total number of AI requests this month
- Breakdown by department
- Usage trend over time
- Projected usage for the current period
If you are using Hosted Mode with monthly limits, this section is especially important.
AI Model Usage
If you use multiple models (Hosted vs BYO), the analytics display which models are used most frequently. You may see metrics such as:
- Number of requests per model
- Model success and failure rates
- Tokens processed (input vs output)
This can help you decide whether to adjust your default model for performance or cost reasons.
Top Tags Chart
The dashboard includes a chart that highlights the most common tags extracted from customer tickets. For example:
- billing
- login
- setup
- refund
- firestick
This provides insight into what topics customers contact you about most often. These trends can help improve routing, documentation, and staffing decisions.
Ticket Sentiment Overview
Sentiment analytics summarize the emotional tone of incoming customer messages. Categories include:
- Positive
- Neutral
- Negative
A high number of negative messages may indicate service issues or unclear instructions in your documentation.
Escalation and Risk Insights
The AI identifies tickets that may need human review or involve sensitive topics. Analytics show:
- Number of escalated tickets
- Common escalation reasons
- Department-level escalation patterns
This data helps managers prioritize staffing and identify areas where SOPs may need improvement.
Department Performance Metrics
Each department using the AI includes usage metrics such as:
- How many replies were automated
- How many suggestions were created
- AI call limits per ticket
- Replies prevented due to safety or sentiment
This helps you understand how automation impacts different areas of your support team.
Knowledge Engine Stats
If the Knowledge Engine is enabled, this section shows:
- Total suggestions generated
- Types of documentation improvements needed
- Common missing topics identified by the system
This helps you keep your Knowledgebase up-to-date and reduce repetitive support questions.
AI Logs Summary
The logs area provides a searchable history of all AI actions. Analytics may summarize:
- Number of successful replies
- Number of blocked replies
- Number of routed tickets
- Error rates and reasons
Review this area to ensure the AI is behaving as expected and diagnose any issues quickly.
Using Analytics to Improve Support
Here are practical ways to use the analytics:
- Identify trending issues and create new KB articles.
- Reduce support volume by improving documentation for common tags.
- Spot negative sentiment trends before they grow.
- Identify which departments benefit most from automation.
- Adjust SOPs based on complexity or escalation rates.
- Optimize your AI models for cost or speed.
Summary
The Usage and Analytics system provides a clear picture of how the hTech AI Ticket Assistant is operating. By reviewing these metrics regularly, you can improve customer satisfaction, reduce support workload, and make better operational decisions.
If you need help understanding your analytics or want recommendations for optimization, feel free to open a support ticket.