Metric Configuration
Metrics are the outcomes you care about tracking. Configure metrics at the org level so every template can use them as optimization targets and performance indicators.
Available Metrics
Section titled “Available Metrics”JustAI supports the following standard metrics, sourced from your ESP’s webhook events:
| Metric | Description | Common Use |
|---|---|---|
| Open rate | Percentage of recipients who opened the message | Subject line and preheader testing |
| Click rate | Percentage of recipients who clicked a link | CTA and content relevance testing |
| Conversion rate | Percentage of recipients who completed a target action | Revenue and signup optimization |
| Unsubscribe rate | Percentage of recipients who unsubscribed | Content quality and frequency monitoring |
| Revenue | Total or average revenue attributed to the message | Direct business impact measurement |
Your org may have additional custom metrics configured based on your ESP’s event data.
Primary Optimization Metric
Section titled “Primary Optimization Metric”Each template selects one metric as its key metric — the outcome that JustAI’s ranking algorithms optimize for. When you create a new template, you choose the key metric during setup.
The key metric determines:
- How the multi-armed bandit allocates traffic between variants
- Which variant Auto-Tune identifies as the winner
- What lift percentage is shown on the template dashboard
How Metrics Flow from Your ESP
Section titled “How Metrics Flow from Your ESP”Metric data follows this path:
- Your ESP sends a message using content from JustAI
- The recipient takes an action (opens, clicks, converts, etc.)
- Your ESP fires a webhook event to JustAI with the event details
- JustAI attributes the event to the specific variant that was served
- The metric appears in the template dashboard and analytics
Accurate metric tracking depends on your ESP integration being properly configured. See your integration guide for webhook setup details.
Analytics Window Parameters
Section titled “Analytics Window Parameters”Two parameters control the time window for metric calculations:
lookback_days (default: 14)
Section titled “lookback_days (default: 14)”How many days of historical data to include when calculating metric rates and variant performance. A 14-day lookback means analytics reflect the most recent two weeks of data.
When to adjust:
- Increase for low-volume templates that need more data to show meaningful patterns
- Decrease for high-volume templates where you want metrics to reflect recent performance more quickly
offset_days (default: 3)
Section titled “offset_days (default: 3)”How many recent days to exclude from analytics calculations. This accounts for the delay between a message being sent and downstream events (like conversions) being recorded.
When to adjust:
- Increase if your conversion events typically lag by more than 3 days (e.g. purchase decisions that take a week)
- Decrease if your events are near-real-time (e.g. click tracking)
Example
Section titled “Example”With lookback_days = 14 and offset_days = 3, on March 15:
| Parameter | Value |
|---|---|
| Window start | March 1 (15 - 14 - 3 + 3 = day 17 ago) |
| Window end | March 12 (3 days ago) |
| Excluded | March 13–15 (still accumulating events) |
How Metrics Appear in Dashboards
Section titled “How Metrics Appear in Dashboards”Once configured, metrics show up across the platform:
- Template Overview — Topline key metric with lift vs. control
- Variant Performance Table — Per-variant breakdown of all configured metrics
- Segment Analysis — Metric performance by attribute segments
- Flows — Aggregated metrics across multiple templates in a journey
Related Resources
Section titled “Related Resources”- Analytics — How to read and interpret metric data in dashboards
- Template Configuration — Set the key metric and stat sig thresholds per template
- Core Concepts: Optimization Metrics — How metrics fit into the broader platform