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3 min read

What This Does #

Creates and manages custom data fields that your bot can collect and use throughout conversations.

When to Use This #

  • You need to collect specific business information
  • You want to personalize conversations with custom data
  • You need fields beyond the standard name/email/phone
  • You’re integrating with systems that require specific data fields

Step-by-Step Instructions #

  1. Access Attributes Management
    • Navigate to Configure
    • Find “ATTRIBUTES SETTING” section
    • Click on “All Attributes” to see existing fields
Manage All Attributes
  1. Review Default Attributes
    • See standard fields already available:
      • User Name (text)
      • Email Id (text)
      • Phone (text)
      • Address (text)
      • Company Name (text)
  2. Create New Custom Attributes
    • Click “New Attribute” button
    • Define the field name and type
    • Set description for internal reference
    • Configure validation rules if needed
Create New Attributes
  1. Manage Existing Attributes
    • View all attributes in the table
    • See creation date and creator
    • Edit or delete attributes as needed
    • Use pagination to browse multiple attributes

Types of Custom Attributes #

Text Fields:

  • Job title or role
  • Department or division
  • Current software/tools used
  • Specific needs or challenges

Boolean:

  • A checkbox on a form representing user agreement to terms and conditions (checked = true, unchecked = false)

Numeric Fields:

  • Number of employees
  • Current subscription level
  • Years in business
  • Monthly volume/usage

Date Fields:

  • Contract renewal date
  • Implementation deadline
  • Last service date
  • Next review scheduled

Industry-Specific Attribute Examples #

SaaS/Software Business:

  • Current software used
  • Number of users/licenses
  • Integration requirements
  • Technical expertise level
  • Implementation timeline

Healthcare:

  • Date of birth
  • Insurance provider
  • Preferred appointment time
  • Medical conditions/concerns
  • Emergency contact

Real Estate:

  • Property type interest
  • Budget range
  • Preferred location
  • Timeline to purchase/sell
  • Current housing situation

Professional Services:

  • Service type needed
  • Project scope
  • Previous experience with service
  • Preferred meeting times
  • Decision-making process

Using Attributes in Conversations #

Data Collection:

  • Attributes automatically appear in “Collect User Data” action
  • Choose which attributes to collect for each conversation
  • Set required vs. optional attributes
  • Collect progressively throughout conversation

Personalization:

  • Use collected attributes in responses
  • Customize recommendations based on attributes
  • Provide relevant examples and case studies
  • Tailor conversation flow to user characteristics

Integration:

  • Send attribute data to CRM systems
  • Include in email templates
  • Use for analytics and reporting
  • Pass to external APIs

Best Practices for Attribute Management #

Naming Conventions:

  • Use clear, descriptive names
  • Be consistent across similar attributes
  • Avoid abbreviations or code names
  • Make names intuitive for team members

Data Quality:

  • Validate data formats where possible
  • Set appropriate field types
  • Include help text or examples
  • Review and clean data regularly

Privacy Compliance:

  • Only collect necessary information
  • Ensure compliance with privacy regulations
  • Provide clear data usage explanations
  • Allow users to update or delete information

Managing Attribute Lifecycle #

Planning New Attributes:

  • Identify business need for data
  • Define how data will be used
  • Choose appropriate field type
  • Plan integration with existing systems

Implementation:

  • Create attribute with clear description
  • Test data collection process
  • Update bot configuration to use attribute
  • Train team on new data field

Maintenance:

  • Regularly review attribute usage
  • Remove unused or outdated attributes
  • Update descriptions as needed
  • Monitor data quality and completeness

Tips for Success #

  • Start with essential attributes only
  • Add complexity gradually
  • Test attribute collection thoroughly
  • Keep descriptions clear and helpful
  • Regularly audit and clean up unused attributes
  • Ensure all team members understand attribute purposes

Common Mistakes to Avoid #

  • Creating too many attributes at once
  • Using unclear or confusing names
  • Not setting appropriate field types
  • Forgetting to remove outdated attributes
  • Not documenting attribute purposes
  • Collecting unnecessary personal information