System Attributes

System attributes are automatically generated by the chatbot platform and provide essential information about user interactions and system performance. They play a crucial role in managing and optimizing the chatbot experience.

What are System Attributes?

System attributes are predefined fields created by the platform. They are collected from the channels connected to the chatbot, such as Facebook, WhatsApp, Instagram, and store data related to user interactions and system operations. This data includes information like user names, conversation history, and metrics automatically generated by the system.

Examples of System Attributes

• {{first_name}}: Stores the user's first name as text.
• {{last_name}}: Stores the user's last name as text.
• {{profile_pic}}: Stores the URL of the user's profile picture.
• {{locale}}: Stores the user's language preference as text.
• {{timezone}}: Stores the user's timezone as a number.
• {{gender}}: Stores the user's gender as text.
• {{signup_date}}: Stores the date and time when the user signed up.
• {{last_interaction}}: Stores the date and time of the user's last interaction with the bot.
• {{last_visited_flow}}: Stores the name of the last flow the user interacted with.
• {{last_message}}: Stores the content of the user's last message as text.
• {{last_clicked_button}}: Stores the label of the last button the user clicked.
• {{phone_number}}: Stores the user's phone number as text.
• {{ChannelId}}: Stores the ID of the channel through which the user is interacting.
• {{UserId}}: Stores the user's unique identifier as text.
• {{HandoverOn}}: Stores whether the conversation has been handed over to a human agent.
• {{email}}: Stores the user's email address as text.
• {{last_message_id}}: Stores the ID of the last message sent by the user.
• {{last_message_sentiment}}: Stores the sentiment of the user's last message (e.g., positive, neutral, negative).

How Do System Attributes Help?

Let’s look at some practical examples:

• Greeting Users by Name: The bot can say, "Hi Sarah!" instead of just "Hi!" if it knows the user’s name.
• Speak Your Preferred Language: If the user prefers Arabic, the bot can automatically greet them in Arabic. (But for this to work, you have to set a condition to check the user’s language and then create a flow to greet them in Arabic.)
• Recommend Products Based on What You Like: If the user last visited a section about skincare, the bot can show them offers on skincare products. (To make this happen, you need to set a condition based on the last visited flow and direct them to the skincare offers.)

• Segmenting Users for Targeted Messages: You can segment users based on attributes like gender, last visited flow, or interaction history. This allows you to send them more relevant content, offers, or direct them to specific flows. For example, if users are interested in skincare and are female, you can target them with personalized offers for skincare products.

  
  
  

How They Work

System Attributes are updated automatically based on user activity and bot performance. No manual input is required from the bot creator.

Examples

1. Greeting Users by Name:

• Attribute Used: {{first_name}}
• How It Helps: Makes the conversation feel more personal.

2. Recommending Personalized Sucare Products to Female Users:

• Filters to Set Up:
  • {{last_visited_flow}} == "Suncare"
  • {{gender}} == "female"
• Target Flow: A flow that shows offers for suncare products.

Learn More

To see how to use system attributes effectively, check out this article on Conditions Go To Flow. It provides practical examples and additional details.