Data Collection

Extract structured information from conversations such as contact details and business data.

Data collection automatically extracts structured information from conversation transcripts using LLM-powered analysis. This enables you to capture valuable data points without manual processing, improving operational efficiency and data accuracy.

Overview

Data collection analyzes conversation transcripts to identify and extract specific information you define. The extracted data is structured according to your specifications and made available for downstream processing and analysis.

Supported Data Types

Data collection supports four data types to handle various information formats:

String: Text-based information (names, emails, addresses)
Boolean: True/false values (agreement status, eligibility)
Integer: Whole numbers (quantity, age, ratings)
Number: Decimal numbers (prices, percentages, measurements)

Configuration

Access data collection settings

In the Analysis tab of your agent settings, navigate to the Data collection section.

Add data collection items

Click Add item to create a new data extraction rule.

Configure each item with:

Identifier: Unique name for the data field (e.g., email, customer_rating)
Data type: Select from string, boolean, integer, or number
Description: Detailed instructions on how to extract the data from the transcript

The description field is passed to the LLM and should be as specific as possible about what to extract and how to format it.

Data collection items are limited to 40 per agent for Trial and Enterprise plans, and 25 per agent for other plans.

Review extracted data

Extracted data appears in your conversation history, allowing you to review what information was captured from each interaction.

Data collection results in conversation history

Best Practices

Writing effective extraction prompts

Be explicit about the expected format (e.g., “email address in the format user@domain.com”)
Specify what to do when information is missing or unclear
Include examples of valid and invalid data
Mention any validation requirements

Common data collection examples

Contact Information:

email: “Extract the customer’s email address in standard format (user@domain.com)”
phone_number: “Extract the customer’s phone number including area code”
full_name: “Extract the customer’s complete name as provided”

Business Data:

issue_category: “Classify the customer’s issue into one of: technical, billing, account, or general”
satisfaction_rating: “Extract any numerical satisfaction rating given by the customer (1-10 scale)”
order_number: “Extract any order or reference number mentioned by the customer”

Behavioral Data:

was_angry: “Determine if the customer expressed anger or frustration during the call”
requested_callback: “Determine if the customer requested a callback or follow-up”

Handling missing or unclear data

When the requested data cannot be found or is ambiguous in the transcript, the extraction will return null or empty values. Consider:

Using conditional logic in your applications to handle missing data
Creating fallback criteria for incomplete extractions
Training agents to consistently gather required information

Data Type Guidelines

String

Boolean

Integer

Number

Use for text-based information that doesn’t fit other types.

Examples:

Customer names
Email addresses
Product categories
Issue descriptions

Best practices:

Specify expected format when relevant
Include validation requirements
Consider standardization needs

Use Cases

Lead Qualification

Extract contact information, qualification criteria, and interest levels from sales conversations.

Customer Intelligence

Gather structured data about customer preferences, feedback, and behavior patterns for strategic insights.

Support Analytics

Capture issue categories, resolution details, and satisfaction scores for operational improvements.

Compliance Documentation

Extract required disclosures, consents, and regulatory information for audit trails.

Troubleshooting

Data extraction returning empty values

Verify the data exists in the conversation transcript
Check if your extraction prompt is specific enough
Ensure the data type matches the expected format
Consider if the information was communicated clearly during the conversation

Inconsistent data formats

Review extraction prompts for format specifications
Add validation requirements to prompts
Consider post-processing for data standardization
Test with various conversation scenarios

Performance considerations

Each data collection rule adds processing time
Complex extraction logic may take longer to evaluate
Monitor extraction accuracy vs. speed requirements
Optimize prompts for efficiency when possible

Extracted data is available through Post-call Webhooks for integration with CRM systems, databases, and analytics platforms.