# Data collection 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. https://www.youtube-nocookie.com/embed/v6_oVI0xy00?rel=0 ## 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 :::::steps :::step{title="Access data collection settings"} In the **Analysis** tab of your agent settings, navigate to the **Data collection** section. Setting up data collection ::: ::::step{title="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 :::callout{intent="info"} The description field is passed to the LLM and should be as specific as possible about what to extract and how to format it. ::: :::callout{intent="note"} Data collection items are limited to 40 per agent for Trial and Enterprise plans, and 25 per agent for other plans. ::: :::: :::step{title="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 :::accordion{title="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 ::: :::accordion{title="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” ::: :::accordion{title="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 ::::tabs :::tab{title="String"} 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 ::: :::tab{title="Boolean"} 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 ::: :::tab{title="Integer"} 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 ::: :::tab{title="Number"} 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 ::::card-grid :::card{title="Lead Qualification"} Extract contact information, qualification criteria, and interest levels from sales conversations. ::: :::card{title="Customer Intelligence"} Gather structured data about customer preferences, feedback, and behavior patterns for strategic insights. ::: :::card{title="Support Analytics"} Capture issue categories, resolution details, and satisfaction scores for operational improvements. ::: :::card{title="Compliance Documentation"} Extract required disclosures, consents, and regulatory information for audit trails. ::: :::: ## Troubleshooting :::accordion{title="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 ::: :::accordion{title="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 ::: :::accordion{title="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 ::: :::callout{intent="info"} Extracted data is available through [Post-call Webhooks](/guides/elevenagents-workflows-post-call-webhooks) for integration with CRM systems, databases, and analytics platforms. ::: ## Related pages - [Administration](./administration-index.md) - [API reference](./api-reference-index.md) - [Changelog](./changelog-index.md) - [ElevenAgents](./elevenagents-index.md) - [ElevenAPI](./elevenapi-index.md) - [ElevenCreative](./elevencreative-index.md) - [ElevenLabs Documentation Docs](../index.md) - [General Troubleshooting FAQ](./troubleshooting-index.md) - [General Website FAQ](./website-index.md) - [Help Center](./help-center-2-index.md) # Agent Instructions Cite this page’s canonical URL and keep its documentation version. 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