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.
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Overview
Section titled “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
Section titled “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
Section titled “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
- Identifier: Unique name for the data field (e.g.,
Review extracted data
Extracted data appears in your conversation history, allowing you to review what information was captured from each interaction.
Best Practices
Section titled “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
Section titled “Data Type Guidelines”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 for yes/no, true/false determinations.
Examples:
- Customer agreement status
- Eligibility verification
- Feature requests
- Complaint indicators
Best practices:
- Clearly define what constitutes true vs. false
- Handle ambiguous responses
- Consider default values for unclear cases
Use for whole number values.
Examples:
- Customer age
- Product quantities
- Rating scores
- Number of issues
Best practices:
- Specify valid ranges when applicable
- Handle non-numeric responses
- Consider rounding rules if needed
Use for decimal or floating-point values.
Examples:
- Monetary amounts
- Percentages
- Measurements
- Calculated scores
Best practices:
- Specify precision requirements
- Include currency or unit context
- Handle different number formats
Use Cases
Section titled “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
Section titled “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