Filtering and Categories

Filtering System Overview

Feedric's filtering system helps you organize and find specific signals using categories, tags, and various filter criteria. This guide covers all the filtering options available to help you manage your signals effectively.

Category System

AI-Generated Categories

Our AI automatically correlates signals into relevant categories based on content analysis:

Common Categories

• Bug Reports: Technical issues and errors
• Feature Requests: New functionality suggestions
• User Experience: Interface and usability signals
• Performance: Speed and optimization issues
• Security: Safety and privacy concerns
• Support: Help and assistance requests

Custom Categories

• Product-Specific: Tailored to your product
• Team-Based: Organized by department
• Priority-Based: High/Medium/Low priority
• Status-Based: New/In Progress/Resolved
• Source-Based: Slack channel or integration

Filter Types and Options

Time-Based Filters

• Date Range: Custom start and end dates
• Quick Filters: Last 24 hours, week, month
• Relative Dates: Before/after specific dates
• Business Hours: Filter by working hours
• Peak Times: High activity periods

Content Filters

• Categories: Filter by AI or custom categories
• Tags: User-assigned or auto-generated tags
• Keywords: Specific words or phrases
• Length: Short, medium, or long signals
• Language: Specific languages or regions

Source Filters

• Bucket: Specific buckets or all buckets
• Slack Channel: Individual or multiple channels
• User: Signals from specific users
• Team: Department or team members
• Integration: Source platform or service

Quality Filters

• Sentiment: Positive, negative, or neutral
• Priority: High, medium, or low priority
• Status: New, in progress, or resolved
• Verified: Confirmed or unverified signals
• Spam: Exclude or include spam content

Advanced Filtering Techniques

Combining Multiple Filters

Use multiple filters together to create highly specific searches:

Example 1: Critical Issues

• Category: "Bug Reports"
• Priority: "High"
• Status: "New"
• Date: "Last 7 days"

Example 2: User Experience Signals

• Category: "User Experience"
• Sentiment: "Negative"
• Source: "Mobile App" bucket
• Length: "Medium to Long"

Filter Presets

Save commonly used filter combinations as presets for quick access:

• Daily Review: New signals from last 24 hours
• Weekly Summary: All signals from last week
• Critical Issues: High priority unresolved items
• Team Focus: Signals assigned to specific team
• Customer Complaints: Negative sentiment signals

Creating and Managing Categories

Custom Category Setup

Step 1: Define Categories

• Identify common themes in your signals
• Create clear, descriptive category names
• Consider your team's workflow and needs
• Plan for future growth and changes

Step 2: Configure AI Training

• Provide examples for each category
• Train the AI with sample signals
• Review and correct AI correlations
• Continuously improve accuracy

Category Management Best Practices

Organization

• Keep categories mutually exclusive
• Use consistent naming conventions
• Avoid too many categories
• Group related categories together

Maintenance

• Regularly review category usage
• Merge similar categories when needed
• Archive unused categories
• Update category definitions

Tagging System

Automatic Tagging

• Keyword Tags: Based on important words in signals
• Sentiment Tags: Positive, negative, neutral
• Priority Tags: High, medium, low based on content
• Context Tags: Mobile, web, API, etc.
• User Tags: New user, power user, etc.

Manual Tagging

• Custom Tags: Create your own tag system
• Team Tags: Assign to specific team members
• Project Tags: Link to specific projects
• Status Tags: Track progress and resolution
• Follow-up Tags: Mark for follow-up actions

Filtering Best Practices

✅ Recommended Practices

• Start with broad filters, then narrow down
• Use saved filter presets for common searches
• Combine time and content filters effectively
• Regularly review and update filter criteria
• Train team on consistent tagging practices
• Document your filtering strategies

❌ Common Mistakes

• Using too many filters at once
• Not saving useful filter combinations
• Ignoring filter performance impact
• Inconsistent tagging across team
• Not reviewing filter effectiveness
• Over-complicating category systems

Advanced Filtering Features

Smart Filtering

• Auto-suggestions: AI suggests relevant filters
• Filter combinations: Smart recommendations for filter pairs
• Trend detection: Automatically suggest trending filters
• Performance optimization: Efficient filter processing

Filter Analytics

• Usage tracking: Monitor which filters are used most
• Effectiveness metrics: Measure filter performance
• Team insights: See how different teams use filters
• Optimization suggestions: Improve filter configurations

💡 Pro Tip

Create a standard set of filter presets for your team to ensure consistency. This makes it easier for everyone to find the same types of signals and maintain consistent analysis across your organization.