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Attributes are custom fields with predefined options for more granular conversation tagging.

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What are Attributes?

Unlike categories (one per conversation), attributes are additional fields that can capture multiple dimensions: A conversation can have values for multiple attributes.

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Creating Attributes

1

Navigate to Attributes

Go to Tag → Attributes in your dashboard.
2

Click Create Attribute

Click Create Attribute.
3

Name the Attribute

Choose a clear, descriptive name:
  • Good: “Priority”, “Product Area”, “Customer Tier”
  • Avoid: “Field 1”, “Attribute”
4

Add Options

Define the possible values:For Priority: High, Medium, Low
5

Choose Selection Type

  • Single-select: Only one option can be chosen
  • Multi-select: Multiple options can be chosen
6

Save

Click Save to create the attribute.

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Single vs Multi-Select

Single-select examples:
  • Priority (can only be one level)
  • Sentiment (positive, neutral, or negative)
  • Customer Tier (one subscription level)
Multi-select examples:
  • Product Areas (issue might span multiple)
  • Tags (multiple descriptors)
  • Features Mentioned (could discuss several)

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How Auto-Extraction Works

The AI extracts attribute values based on:
  1. Conversation content — What was discussed
  2. Customer information — Available metadata
  3. Context clues — Tone, urgency, specifics mentioned
For example, if a customer says “This is urgent, I need this fixed today!”, the AI would likely set Priority to “High”.

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Common Attributes

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Priority

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Sentiment

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Issue Type

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Product Area

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Customer Tier

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Using Attributes in Analytics

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Breakdown by Attribute

See distribution across attribute values:
  • 45% Low Priority, 35% Medium, 20% High
  • Dashboard: 60%, API: 30%, Mobile: 10%

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Filter by Attribute

View only High Priority conversations, or only API-related issues.

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Cross-Analysis

Combine category and attribute analysis:
  • “High Priority Technical Issues”
  • “Enterprise Billing Questions”

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Assigning Attributes to Agents

1

Open Agent Configuration

Go to Build → Agents and click on an agent.
2

Go to Classification Tab

Select the Classification tab.
3

Select Attributes

Check which attributes this agent should extract.
4

Save

Save the agent configuration.

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Best Practices

  • Start with essentials — Priority and Product Area are common starting points
  • Keep options clear — Each option should be distinct
  • Use descriptions — Help the AI extract accurately
  • Review extraction — Check that attributes are being applied correctly
  • Don’t over-attribute — 3-5 attributes is usually sufficient

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Next Steps

Categories

Set up high-level categories

Resolution Rules

Define resolution criteria