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Overview

The History section in Oration AI provides a comprehensive view of all your past conversations. Here, you can review call details, listen to recordings, analyze transcripts, inspect dynamic variables, and run post-call analysis to extract actionable insights.

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1. Conversation History Overview

History Overview The master view displays a list of all calls made by your agents. For each conversation, you can see:
  • Agent Name: The agent who handled the call.
  • Created At: The date and time the call was initiated.
  • Talk Time: Duration of the conversation.
  • Customer Phone Number: The number of the customer involved in the call.
  • End Reason: Why the call ended (e.g., user hangup, completed).
  • Conversation Type: The channel used (e.g., telephony).
  • Actions: Play the call recording or copy the conversation ID for reference.
You can filter, sort, and change the view to quickly find the conversations you’re interested in.

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2. Call Transcript and Details

Call Transcript and Details Clicking on a conversation opens a detailed view, where you can:
  • See Talk Time, End Reason, Status, and Phone Number: At the top, key call metrics and metadata are displayed.
  • Transcript Tab: Review the full transcript of the conversation, with clear separation between agent and customer messages, including timestamps.
  • Playback Controls: Listen to the call recording directly within the interface.
This helps you audit conversations, understand customer interactions, and ensure quality.

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3. Dynamic Variables

Dynamic Variables The Dynamic Variables tab shows all the placeholders and values used during the conversation. These variables can be referenced in your agent’s prompts and responses to personalize the experience. Examples:
  • customerName: The name of the customer.
  • amount: Financial details relevant to the call.
Dynamic variables make your agents more context-aware and responsive.

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4. Post Call Analysis

Post Call Analysis The Post Call Analysis feature lets you extract structured insights from every conversation using AI.
  • Is Enabled: Toggle to automatically run analysis after every call.
  • Prompt: Write a custom prompt to instruct the AI on what to analyze in the transcript, or click Generate Prompt for AI assistance.
  • Schema: Define the tags or events you want to capture from the transcript. For each event, specify:
    • Type: The data type (e.g., String).
    • Event Name: The label for the event (e.g., Callback).
    • Description: Describe the event, including scenarios, possible values, criteria, and examples.
Example:
To detect if a customer requested a callback, set:
  • Type: String
  • Event Name: Callback
  • Description: “Indicates if the customer asked for a callback. Values: ‘yes’, ‘no’. Criteria: Look for phrases like ‘please call me back’, ‘can you call later’, etc. Example: ‘I missed your call, can you call me again?’”
This structured analysis helps you track key outcomes, monitor agent performance, and identify trends for continuous improvement.

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Best Practices

  • Regularly review transcripts and recordings to ensure quality and compliance.
  • Leverage dynamic variables for personalized and context-rich agent responses.
  • Use post call analysis to automate insight extraction and drive data-driven decisions.
  • Define clear schemas(event) for events you want to track, and update them as your business needs evolve.
Need more help? Reach out to our team at [email protected]