Get hourly email traffic trends
Performance Metrics
Hourly Email Traffic Trends
Returns hourly email volume (received and sent) between the given start and end dates. Each day is broken down into 24 hourly buckets, showing how many emails were received and sent during each hour.
- If no date range is provided, the system automatically adjusts it to a week from today. - Each entry in the result corresponds to an hour of the day (
00:00:00→23:00:00). - Useful for visualizing hourly trends in dashboards.
GET
Get hourly email traffic trends
Returns hourly email volume (received and sent) between the given start and end dates. Each day is broken down into 24 hourly buckets, showing how many emails were received and sent during each hour.
Request
Headers
| Name | Type | Required | Description |
|---|---|---|---|
| Authorization | string | Yes | Bearer token |
Path Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| startDate | string | Yes | Start date for the range (YYYY-MM-DD) |
| endDate | string | Yes | End date for the range (YYYY-MM-DD) |
Parameter Format
- Format:
YYYY-MM-DD(ISO 8601 date) - Timezone: UTC
- Default: Last 7 days if not provided
Response
200 OK - Hourly email trends between given dates
401 Unauthorized
500 Internal Server Error
Example
Data Structure
Labels Array
| Field | Type | Description |
|---|---|---|
| labels | array | Hour labels from 00:00:00 to 23:00:00 |
Series Object
| Field | Type | Description |
|---|---|---|
| received | array | Series showing received emails per hour per day |
| sent | array | Series showing sent emails per hour per day |
Data Series Structure
Each object in received/sent arrays contains:| Field | Type | Description |
|---|---|---|
| name | string | Date of the data point (YYYY-MM-DD) |
| data | array | 24-hour email counts |
Data Alignment
series.received[i].data[j]corresponds tolabels[j]for received emails on dateseries.received[i].nameseries.sent[i].data[j]corresponds tolabels[j]for sent emails on dateseries.sent[i].name- All data arrays have exactly 24 elements (one for each hour)
Use Cases
- Peak Hours Analysis: Identify busiest email hours
- Staff Scheduling: Optimize agent scheduling based on volume
- Resource Planning: Allocate resources during peak times
- Performance Monitoring: Track hourly performance patterns
- Heat Map Visualization: Create hourly heat maps
Analysis Examples
Peak Hour Identification
Daily Pattern Analysis
Business Hours vs Non-Business Hours
Visualization Examples
Heat Map
Multi-Day Line Chart
Best Practices
- Date Range: Use reasonable ranges (recommended: max 30 days for hourly detail)
- Time Zone: Convert UTC times to local time zones for display
- Aggregation: Consider aggregating multiple days for pattern analysis
- Visualization: Use heat maps for better pattern visualization
- Business Hours: Focus analysis on business hours for operational insights
Performance Considerations
- Data Volume: Hourly data generates 24x more points than daily
- Processing Time: Larger date ranges take longer to process
- Memory Usage: Consider client-side memory for large datasets
- Caching: Cache results for dashboard performance
Related Endpoints
- Use
/email-meter/stats/emails-trendfor daily trends - Use
/email-meter/stats/email-countsfor aggregate statistics - Use
/email-meter/stats/response-timesfor hourly response analysis
Notes
- All times are in UTC - convert to local time for operational analysis
- Zero-value hours are included for complete timeline
- Data includes all email types (incoming, outgoing, internal)
- Weekends and holidays are included in hourly breakdown
- Automated emails are included unless filtered specifically
Authorizations
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Path Parameters
Start date for the range (YYYY-MM-DD)
End date for the range (YYYY-MM-DD)