Smart Automation
Feedbackview's intelligent automation system reduces manual work by automatically processing, analyzing, and routing feedback based on configurable rules and AI insights.
How Automation Works
Every feedback submission triggers an automated workflow that analyzes content, scores impact, and applies your configured automation rules to take appropriate actions without manual intervention.
Automation Types
Feedbackview offers two main automation types that can work independently or together to streamline your feedback workflow.
What it does:
- Analyzes feedback content and context
- Generates relevant, helpful responses
- Can auto-publish or save as drafts
- Maintains consistent tone and messaging
Best for:
High-volume feedback, common questions, and maintaining response consistency
What it does:
- Matches feedback to team member skills
- Considers current workload and capacity
- Routes by department and expertise
- Balances assignments across team
Best for:
Teams with specialized roles, workload distribution, and ensuring expertise matching
Rule Configuration
Create powerful automation rules with granular conditions that determine when and how automation should trigger based on feedback characteristics.
Define precisely when rules should activate:
- Feedback Types: Bugs, features, questions, or general feedback
- Impact Scores: Critical (5) down to minimal (1) priority levels
- Keywords: Specific terms like "urgent", "crash", or "payment"
- Status: Open, in progress, resolved, or closed items
Customize automation behavior:
- Enable/Disable: Toggle rules on and off as needed
- Context Awareness: Include project-specific context
Intelligence Behind Automation
Feedbackview's automation is powered by advanced AI analysis that understands context, intent, and priority to make intelligent decisions.
AI reads and understands feedback content, extracting key information about user intent, technical issues, and priority levels.
Identifies patterns in feedback themes, user behavior, and resolution paths to improve automation over time.
Considers project context, team expertise, and historical data to make contextually appropriate automation decisions.
Automation Performance
Automation accuracy for rule matching and execution
Average time from feedback submission to automated action
Reduction in manual feedback processing time
Average response time for automated AI responses