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

# Step 4: Post Call Metrics

> Detailed guide to configure and track post call performance metrics for your AI Agent.

## How to Set Up a Post Call Metric

Step-by-step guide:

### **Metric Name**

Enter a unique, descriptive metric name (e.g., "Customer Satisfaction", "Order Status", "Follow-up Required").\
This helps you quickly identify what data you're tracking for each call.

### **Data Type**

Choose from several data types to fit your tracking needs:

* **Text** – Free-form summaries or notes from the call.
* **Single Extract** – Extract one specific item of information (like the customer's answer or score).
* **Boolean (Yes/No)** – Track simple outcomes: was the order placed? Did the issue get resolved?
* **Timestamp** – Store the exact time something happened on the call.
* **Date** – Save calendar-based dates, such as appointment dates.
* **Numeric** – Store quantities, ratings, codes (for example, feedback scores).
* **Multi Extract** – Collect multiple pieces of data from the same call for richer reporting.

### **Description**

Document what each metric tracks and how it’s calculated.\
Clearly explain how the agent should use or interpret this field.

### Example: Custom Post Call Metric

```
Suppose you want to track how satisfied customers are after a call.

- Metric Name: Customer Satisfaction Score  
- Data Type: Numeric  
- Description: "Extract a rating from 0 to 10 based on how satisfied the customer sounds during the call. If the customer directly provides a rating, use that value. Otherwise, estimate based on conversation sentiment."
```

### 💡Tip

* Use meaningful metric names so everyone on your team understands what’s being tracked.
* Pick the most suitable data type to make reporting and analysis easy.
* Clearly describe each metric for consistent agent use and accurate data.