> ## Documentation Index
> Fetch the complete documentation index at: https://docs.verbex.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Post Call Analysis

> Learn how to analyze conversations between AI assistants and users after they happen

<Note>
  **What you'll learn**

  * What Post Call Analysis is and why it's important

  * How to set up different types of Post Call Analysis fields

  * How to use Post Call Analysis to extract structured data from conversations
</Note>

# What is Post Call Analysis?

Post Call Analysis helps you understand what happened in conversations between your AI assistant and users. Think of it like a smart report card that automatically grades and categorizes each conversation.

For example, you can:

* Track if users were satisfied with the conversation

* Extract structured data from conversations

* Categorize conversations into specific groups

* Summarize the conversation between the AI Agent and the user

* Measure how well your AI assistant is performing

<Note>
  **How it works**
  After each call ends, Post Call Analysis automatically starts working in the background. It:

  * Analyzes the entire conversation

  * Extracts necessary information using the fields you set up

  * Stores the results for you to review

  The best part? You can customize what information to extract by simply writing clear descriptions for each field you set up.
</Note>

# Types of Post Call Analysis Fields

You can collect different types of information using four field types:

## 1. Text Fields 📝

* **What they are**

  : Fields where you can write any text

* **Best for**

  : Storing detailed notes, comments, or feedback, summarizing the conversation

* **Example**

  : "What did the customer say about our service?"

## 2. Yes/No Fields ✓/✗

* **What they are**

  : Fields with only two possible answers (Yes/No, True/False)

* **Best for**

  : Simple questions with clear answers, extracting boolean values

* **Example**

  : "Did the customer's problem get solved?"

## 3. Choice Fields 📋

* **What they are**

  : Fields with a list of options to choose from

* **Best for**

  : Categorizing conversations into specific groups

* **Example**

  : "Which product did the customer ask about?"

## 4. Number Fields 🔢

* **What they are**

  : Fields that only accept numbers

* **Best for**

  : Ratings, scores, or counting things, extracting numerical values

* **Example**

  : "Rate the conversation from 1 to 5"

# How to Set Up Post Call Analysis Fields

Let's walk through how to set up each type of field:

## Step 1: Open the Analysis Settings

1. Go to your dashboard

2. Click on "Post Call Analysis"

3. Click the "Add Field" button

<img height="321" width="426" src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXedLPnUMahmElb4wYdE_MQtvdyj2x890bl8opKNBk2DrhvIIdsxGbxZAI2EmzUKNmKuxY_RuVamKk9-zYz2azMnRxRa5luJ0C-rporQJTyIuI9DMm1kZxNNIvPAhWYyKwcQtsIS?key=pHx6wP9dauST4uLstZFocSQW" />

## Step 2: Choose Your Field Type

Pick the type of field that matches what you want to track:

### Text Field Example

<img height="332" width="417" src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXdJDz5HiTbkNrUdnvOndfOlHoBt71xm2BPQIdt9N4U_2mSGmoOz2CdBMR9ZUzt8ILQdFx7tw2ENUdNLN533SEfMshO0MLlaL042UuQao4LQxc5FWlpV6uBP3YmgCUIwzBr7NrXn5g?key=pHx6wP9dauST4uLstZFocSQW" />

### Yes/No Field Example

<img height="295" width="358" src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXdHYW1RCelR8dm3UfMnmS7T5HW9dV5EzzddP4kR7E4JDT6CXa4P-jZet9dW5xJIRDlYgazNCBUTpgvm6l76UtR0kLVoHSrZIcAR6FSI-J8c5HtU7WAyh8PljeAFLuIfB89rt0n61g?key=pHx6wP9dauST4uLstZFocSQW" />

### Choice Field Example

<img height="318" width="411" src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXeUNaFqM7gyAtz42sBTf7ok21kWYIOh6eqrR5Nqs9NpBflonSwaat4yFtijANYWW-k9cCtNES6yHEl9MlSCawROBYMglYiH9FejCZfCziqQWCx_LUPwsRT1PSxtKhtBS_97AkEqzw?key=pHx6wP9dauST4uLstZFocSQW" />

### Number Field Example

<img height="357" width="439" src="https://lh7-rt.googleusercontent.com/docsz/AD_4nXeKYw9-1sf5A_8IRcTAoskI7v-uHx7PHP8tvo0Qj9NM-fvgo5mBwM8tHzRxyhE-7OlsOIpccQKKzk-VkQiCR5HgxkYvqdJKV3_LbJICcirfNqeTNpX1k-s0hetSUHms5kJAOwbBOQ?key=pHx6wP9dauST4uLstZFocSQW" />

## Step 3: Configure Your Field

For each field:

1. Give it a clear name (like "customer\_satisfaction")

2. Write a clear description that acts like a prompt

   * Be specific about what you want to extract

   * Use clear instructions

   * Include format requirements if any

   * Add examples if helpful

### Writing Good Descriptions

Your description tells the AI what to look for and how to format the response. Here are examples for each field type:

#### Text Field Description Examples

✅ Good: "Summarize the main complaint or issue the customer reported. Include the specific product/service mentioned and any error messages if applicable."

❌ Bad: "Get customer complaint"

<br />

✅ Good: "List the specific next steps agreed with the customer. Format as bullet points, starting with action verbs."

❌ Bad: "What are the next steps"

<br />

#### Yes/No Field Description Examples

✅ Good: "Did the customer explicitly express satisfaction with the resolution? Answer 'Yes' only if they clearly stated they were satisfied."

❌ Bad: "Customer satisfied?"

<br />

✅ Good: "Was a refund requested during the conversation? Check for any mention of 'refund', 'money back', or similar phrases."

❌ Bad: "Refund needed"

<br />

#### Choice Field Description Examples

✅ Good: "Identify the primary reason for the call. Choose the category that best matches the customer's first reported issue, not subsequent topics discussed."

❌ Bad: "What did they call about"

<br />

✅ Good: "Determine the customer's subscription tier mentioned during the call. If multiple tiers are discussed, select the one they currently have."

❌ Bad: "Customer tier"

<br />

#### Number Field Description Examples

✅ Good: "Extract the total amount the customer is willing to spend, mentioned in dollars. If a range is given, use the higher amount."

❌ Bad: "How much will they spend"

<br />

✅ Good: "Rate the customer's frustration level from 1-5 based on their tone and words used. 1=Very calm, 3=Moderate frustration, 5=Extremely frustrated."

❌ Bad: "Customer frustration level"

<Tip>
  **Description Writing Tips**

  * Start with an action verb (Extract, Identify, Determine, etc.)

  * Specify any conditions or criteria

  * Include format requirements if needed

  * Provide context for subjective measures

  * Define how to handle edge cases
</Tip>

# Real-World Example: Employee Satisfaction Survey

Let's walk through how to set up Post Call Analysis fields for an employee satisfaction survey. We'll create fields to extract answers for each survey question.

## Survey Questions

1. "How would you rate your overall job satisfaction on a scale of 1 to 5?"

2. "Would you recommend our company as a good place to work?"

3. "Which department do you currently work in?"

4. "What specific factors contribute to your satisfaction or dissatisfaction?"

## Step-by-Step Field Setup

### Step 1: Create Number Field for Satisfaction Rating

1. Click "Add Field"

2. Select "Number" as the field type

3. Configure the field:

   * **Name**

     :

     `satisfaction_rating`

   * **Description**

     : "Extract the employee's job satisfaction rating on a scale of 1 to 5, where 1 is very dissatisfied and 5 is very satisfied. Only accept whole numbers between 1-5."

4. Click "Save"

**Expected Result**: A number between 1-5 (Example: "4")

### Step 2: Add Yes/No Field for Company Recommendation

1. Click "Add Field"

2. Select "Yes/No" as the field type

3. Configure the field:

   * **Name**

     :

     `would_recommend_company`

   * **Description**

     : "Determine if the employee would recommend the company as a workplace. Look for explicit statements of willingness to recommend. Answer 'Yes' only if they clearly indicate they would recommend."

4. Click "Save"

**Expected Result**: "Yes" or "No"

### Step 3: Set Up Choice Field for Department

1. Click "Add Field"

2. Select "Choice" as the field type

3. Configure the field:

   * **Name**

     :

     `employee_department`

   * **Description**

     : "Identify which department the employee currently works in. Select from the available options. If multiple departments are mentioned, choose their current department."

   * **Add Choices**

     :

     * Type "Engineering" and click Add

     * Type "Finance" and click Add

     * Type "Operations" and click Add

     * Type "Product Management" and click Add

4. Click "Save"

**Expected Result**: One of the predefined departments (Example: "Product Management")

### Step 4: Create Text Field for Satisfaction Factors

1. Click "Add Field"

2. Select "Text" as the field type

3. Configure the field:

   * **Name**

     :

     `satisfaction_factors_feedback`

   * **Description**

     : "Extract and summarize the specific factors affecting the employee's job satisfaction or dissatisfaction. Include:

     * Mentioned positive factors

     * Any areas of concern
       Format as a clear paragraph with specific examples mentioned."

4. Click "Save"

**Expected Result**: A detailed paragraph (Example: "The employee expressed high job satisfaction, highlighting the interactive work environment and feeling valued. They mentioned no current factors contributing to dissatisfaction.")

<Note>
  **Verification Tips**

  * After setting up all fields, conduct a test call to verify each field extracts the expected information

  * Check if the descriptions are clear enough by reviewing the extracted results

  * Adjust field descriptions if the results aren't as expected
</Note>

<Note>
  **Pro Tips**

  * Keep field names lowercase with underscores for consistency

  * Make descriptions as specific as possible to get accurate results

  * Include example formats in descriptions when needed

  * Review and refine field configurations based on initial results
</Note>