# Success evaluation Success evaluation allows you to define custom goals and success metrics for your conversations. Each criterion is evaluated against the conversation transcript and returns a result of `success`, `failure`, or `unknown`, along with a detailed rationale. https://www.youtube-nocookie.com/embed/hvuuRpvAlV0?rel=0 ## Overview Success evaluation uses LLM-powered analysis to assess conversation quality against your specific business objectives. This enables systematic performance measurement and quality assurance across all customer interactions. ### How it works Each evaluation criterion analyzes the conversation transcript using a custom prompt and returns: - **Result**: `success`, `failure`, or `unknown` - **Rationale**: Detailed explanation of why the result was chosen ### Types of evaluation criteria ::::tabs :::tab{title="Goal prompt criteria"} **Goal prompt criteria** pass the conversation transcript along with a custom prompt to an LLM to verify if a specific goal was met. This is the most flexible type of evaluation and can be used for complex business logic. **Examples:** - Customer satisfaction assessment - Issue resolution verification - Compliance checking - Custom business rule validation ::: :::: ## Configuration :::::steps :::step{title="Access agent settings"} Navigate to your agent’s dashboard and select the **Analysis** tab to configure evaluation criteria. Analysis settings ::: ::::step{title="Add evaluation criteria"} Click **Add criteria** to create a new evaluation criterion. Define your criterion with: - **Identifier**: A unique name for the criterion (e.g., `user_was_not_upset`) - **Description**: Detailed prompt describing what should be evaluated Setting up evaluation criteria :::callout{intent="note"} Evaluation criteria are limited to 30 per agent. ::: :::: :::step{title="View results"} After conversations complete, evaluation results appear in your conversation history dashboard. Each conversation shows the evaluation outcome and rationale for every configured criterion. Evaluation results in conversation history ::: ::::: ## Best Practices :::accordion{title="Writing effective evaluation prompts"} - Be specific about what constitutes success vs. failure - Include edge cases and examples in your prompt - Use clear, measurable criteria when possible - Test your prompts with various conversation scenarios ::: :::accordion{title="Common evaluation criteria"} - **Customer satisfaction**: “Mark as successful if the customer expresses satisfaction or their issue was resolved” - **Goal completion**: “Mark as successful if the customer completed the requested action (booking, purchase, etc.)” - **Compliance**: “Mark as successful if the agent followed all required compliance procedures” - **Issue resolution**: “Mark as successful if the customer’s technical issue was resolved during the call” ::: :::accordion{title="Handling ambiguous results"} The `unknown` result is returned when the LLM cannot determine success or failure from the transcript. This often happens with: - Incomplete conversations - Ambiguous customer responses - Missing information in the transcript Monitor `unknown` results to identify areas where your criteria prompts may need refinement. ::: ## Use Cases ::::card-grid :::card{title="Customer Support Quality"} Measure issue resolution rates, customer satisfaction, and support quality metrics to improve service delivery. ::: :::card{title="Sales Performance"} Track goal achievement, objection handling, and conversion rates across sales conversations. ::: :::card{title="Compliance Monitoring"} Ensure agents follow required procedures and capture necessary consent or disclosure confirmations. ::: :::card{title="Training & Development"} Identify coaching opportunities and measure improvement in agent performance over time. ::: :::: ## Troubleshooting :::accordion{title="Evaluation criteria returning unexpected results"} - Review your prompt for clarity and specificity - Test with sample conversations to validate logic - Consider edge cases in your evaluation criteria - Check if the transcript contains sufficient information for evaluation ::: :::accordion{title="High frequency of 'unknown' results"} - Ensure your prompts are specific about what information to look for - Consider if conversations contain enough context for evaluation - Review transcript quality and completeness - Adjust criteria to handle common edge cases ::: :::accordion{title="Performance considerations"} - Each evaluation criterion adds processing time to conversation analysis - Complex prompts may take longer to evaluate - Consider the trade-off between comprehensive analysis and response time - Monitor your usage to optimize for your specific needs ::: :::callout{intent="info"} Success evaluation results are available through [Post-call Webhooks](/guides/elevenagents-workflows-post-call-webhooks) for integration with external systems 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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