Post-call metrics let you pull specific insights from conversations after they end. Define what you want to know — satisfaction scores, call outcomes, issue categories — and Atoms analyzes each call to fill in the answers.
Location: Left Sidebar → Post Call Metrics
How It Works
- You define metrics — What questions do you want answered about each call?
- Call ends — Conversation completes normally
- AI analyzes — Atoms reviews the transcript against your metrics
- Data populated — Your metrics get filled in automatically
- Access anywhere — View in logs, receive via webhook, export
Creating a New Metric
Click the Add Metrics + button to open the configuration panel. You’ll see two options:
Disposition Metrics
Templates
Build a custom metric from scratch. Fill in the Identifier, Data Type, and Prompt — see details below.Use Add Another + to create multiple metrics at once.Don’t forget to hit Save in the Disposition tab once you’re done.
Choose from pre-built metrics for common use cases. Just select the ones you want — no manual configuration needed.Don’t forget to hit Save in the Disposition tab once you’re done.
Configuring a Metric
Each metric needs three things:
| Field | Required | Description |
|---|
| Identifier | Yes | Unique name for this metric. Lowercase, numbers, underscores only. |
| Data Type | Yes | What kind of value: String, Number, or Boolean |
| Prompt | Yes | The question you want answered about the call |
Identifier
This is the key used to reference the metric in exports, webhooks, and the API.
customer_satisfaction
call_outcome
follow_up_needed
Naming rules: Lowercase letters, numbers, and underscores only. No spaces or special characters.
Data Type
| Type | Use for | Example values |
|---|
| String | Free text, categories | ”resolved”, “escalated”, “billing issue” |
| Boolean | Yes/no questions | true, false |
| Integer | Whole numbers, scores | 1, 5, 10 |
| Enum | Fixed set of options | One of: “low”, “medium”, “high” |
| Datetime | Dates and times | ”2024-01-15T10:30:00Z” |
Prompt
This is the question the AI answers by analyzing the transcript. Be specific.
Good prompts:
- “Did the agent acknowledge and respond to customer concerns effectively?”
- “Rate customer satisfaction from 1 to 5 based on tone and words used.”
- “What was the primary reason for this call? Options: billing, technical, account, other”
Vague prompts to avoid:
- “Was it good?”
- “Customer happy?”
Start with 3-5 metrics. Too many can slow analysis and clutter your data. Add more as you learn what insights matter most.
Example Metrics
Call Outcome
Satisfaction Score
Follow-Up Needed
Issue Category
| Field | Value |
|---|
| Identifier | call_outcome |
| Data Type | String |
| Prompt | ”What was the outcome of this call? Options: resolved, escalated, transferred, abandoned, callback_scheduled” |
| Field | Value |
|---|
| Identifier | satisfaction_score |
| Data Type | Integer |
| Prompt | ”Rate the customer’s apparent satisfaction from 1 to 5, based on their tone and language throughout the call.” |
| Field | Value |
|---|
| Identifier | follow_up_needed |
| Data Type | Boolean |
| Prompt | ”Does this call require any follow-up action from the team?” |
| Field | Value |
|---|
| Identifier | issue_category |
| Data Type | Enum |
| Prompt | ”What was the primary issue category? Options: billing, technical, account, product_info, complaint, other” |
