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Overview

Post-call variables are the data that you want to extract from the call.
Define what data to extract, and AI automatically extracts it from every call.

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Post-Call Variables

Post-call variables are information fields that AI extracts from the conversation after the call ends. This data can be used in post-call functions and analytics.

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Variable Types

Purpose: Extract free-form text responsesUse Cases:
  • Call summaries
  • Customer feedback
  • Address or location information
  • Notes or comments
Example:
Name: detailed_call_summary
Description: Detailed summary of the call covering main points discussed, 
             customer concerns, and action items
AI Behavior: Extracts and returns free-text response based on your description

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Creating Post-Call Variables

1

Click Add Variable

Click the “Add Variable” dropdown button
2

Select Variable Type

Choose the appropriate type: Text, Selector, Boolean, or Number
3

Configure Variable

Name: Unique identifier (use underscores, e.g., customer_interest)Description: Clear instruction for AI on what to extract
The description is critical - it tells the AI exactly what information to look for and how to extract it.
4

Save

Click Submit to add the variable

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Best Practices for Descriptions

Bad: “Get customer info”Good: “Extract the customer’s full name as mentioned during the conversation. If multiple names are mentioned, use the one they introduce themselves with.”
Always list all possible values:
Description: What is the customer's preferred contact method? 
Options: email, phone, whatsapp, sms
Help AI understand edge cases:
Description: Did the customer book an appointment? Return true only if a 
specific date and time was confirmed. Return false if they said "maybe" 
or "I'll think about it."
Frame as yes/no questions:
  • ✅ “Did the customer express interest in the product?”
  • ✅ “Was the call issue resolved?”
  • ❌ “Customer interest” (too vague)

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Using Post-Call Variables

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In Post-Call Functions

Access extracted variables in your post-call functions: Basic Mode: 
{
  "customer_name": "{{customer_name}}",
  "interest_level": "{{interest_level}}",
  "appointment_booked": "{{appointment_booked}}"
}
Advanced Mode (JavaScript): 
async function processCall(context) {
    // Access post-call variables
    const customerName = context.post_call.customer_name;
    const interestLevel = context.post_call.interest_level;
    const appointmentBooked = context.post_call.appointment_booked;
    
    // Use in your logic
    if (appointmentBooked) {
        // Send confirmation email
        await axios.post('https://api.example.com/send-email', {
            to: customerName,
            type: 'appointment_confirmation'
        });
    }
}

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Post-Call Model

Choose which AI model processes your post-call analysis.
  • o4-mini (Default) - Fast and cost-effective
  • gpt-4.1-mini - Balanced performance
  • gpt-4.1 - Higher accuracy
  • gpt-5-mini - Latest model
  • gpt-4o - OpenAI’s flagship model
For most use cases, o4-mini provides excellent accuracy at the best price.

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Conversion Reason

Define what constitutes a “conversion” for your use case. Purpose: Tell the AI what successful outcome looks like so it can mark calls as converted. Examples: E-commerce: 
Customer places an order or commits to purchasing a product. 
Mark as converted if they provide payment information or confirm 
the order.
Appointment Booking: 
Customer books a confirmed appointment with a specific date and time. 
Do not mark as converted for tentative or "maybe" responses.
Lead Qualification: 
Customer expresses strong interest and agrees to a follow-up call with 
the sales team. They must provide their contact information and confirm 
a callback time.
Be specific about what qualifies as a conversion vs. what doesn’t. This ensures accurate conversion tracking.

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Best Practices

Begin with 3-5 core post-call variables. Add more as you identify needs from actual call data.
  • Use snake_case for names
  • Be descriptive: customer_interest_level not interest
  • Avoid abbreviations unless universally clear
After running calls:
  1. Check if variables are being extracted correctly
  2. Refine descriptions if AI misunderstands
  3. Add new variables as patterns emerge
  4. Remove unused variables
Use the Test Call feature to verify:
  • Variables are extracted as expected
  • Descriptions are clear enough for AI
  • Data types match expectations

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Examples by Industry

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E-commerce

Post-Call Variables: 
order_amount (Number): Total order amount in rupees
product_category (Selector): Which product category did customer order from? 
                              Options: electronics, clothing, home, beauty
delivery_preference (Selector): Preferred delivery option? Options: standard, 
                                express, same_day

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Healthcare/Medical

Post-Call Variables: 
appointment_type (Selector): Type of appointment? Options: consultation, 
                              follow_up, lab_test, emergency
urgency_level (Selector): How urgent is the appointment? Options: routine, 
                          urgent, emergency
insurance_verified (Boolean): Was insurance information verified?

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Real Estate

Post-Call Variables: 
property_type (Selector): Type of property interested in? Options: apartment, 
                          villa, plot, commercial
budget_range (Text): Budget range mentioned by customer
location_preference (Text): Preferred locations or areas mentioned
viewing_scheduled (Boolean): Was a property viewing scheduled?

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Troubleshooting

Possible Causes:
  • Description is too vague
  • Information wasn’t discussed in call
  • Variable type doesn’t match data
Solutions:
  • Make description more specific
  • Add examples of what to look for
  • Check call transcript to verify info was mentioned
Issue: Too strict criteriaSolution: Clarify what counts as true:
Bad: "Did customer book appointment?"
Good: "Did customer book appointment? Return true if a specific date 
       and time was confirmed, even if tentative."
Issue: Options not clearly definedSolution: Provide examples for each option:
Description: Customer's experience level? 
Options: 
- beginner (first time using, needs guidance)
- intermediate (some experience, needs occasional help)
- expert (very familiar, minimal assistance needed)

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Getting Help

Test Your Configuration

Use Test Call to verify variables work as expected

Contact Support

Our team can help optimize your post-call variable configuration for your specific use case