# Calendly Scheduling Analytics

> Get a clear picture of your meeting load. Track volume, cancellations, popular event types, and peak hours from your Calendly data.

Source: https://cotera.co/solutions/ops/calendly-scheduling-analytics

---

- **Team:** Operations
- **Tools:** Calendly
- **Difficulty:** easy
- **Setup time:** 5 min
- **Time saved:** 1-2 hrs/month

## How it works
1. **Pull Event History** — Fetches 30 days of meetings, both active and canceled
2. **Analyze Patterns** — Finds busiest days, peak hours, and trends
3. **Check Availability** — Compares your availability windows to actual bookings
4. **Recommend Changes** — Suggests schedule optimizations based on the data

## Example requests
- Analyze my Calendly meetings from the past month
- What is my meeting cancellation rate? Which event types get booked most?
- Are my availability windows optimized? Show me the data.

## Prompt

```markdown
## Task

Analyze my Calendly scheduling data for the past 30 days. Give me a breakdown of meeting volume, cancellation rates, most popular event types, busiest days, and whether my availability schedule matches my actual booking patterns.

## Input

The user provides a time range (default: past 30 days).

## Context

### Data to Pull

1. Use @Calendly/Get Current User to get the user URI
2. Use @Calendly/List Event Types to get all event types with durations and active status
3. Use @Calendly/List Events for the past 30 days (both active and canceled) -- paginate to get all results
4. Use @Calendly/List Availability Schedules to see configured availability windows

### What to Analyze

- Total meetings held vs. canceled
- Cancellation rate as a percentage
- Breakdown by event type (which meeting types are most popular?)
- Busiest day of the week
- Average meetings per day
- Peak booking hours
- Whether availability windows are well-utilized or have dead spots
- Comparison of active event types vs. those that never get booked

## Output

**Overview:** Total meetings, cancellation rate, average per day.

**By Event Type:** Name, count, percentage of total, average duration.

**Scheduling Patterns:**
- Busiest days of the week
- Peak hours for bookings
- Trends (are meetings increasing or decreasing week over week?)

**Availability Analysis:**
- Which availability windows get the most bookings
- Suggested adjustments to availability based on booking patterns
- Event types that could be consolidated or retired

**Recommendations:** 2-3 actionable suggestions to optimize scheduling.
```

## Related reading
- [Meeting Scheduling Software: The Feature That Actually Moved Our Numbers](https://cotera.co/articles/meeting-scheduling-software-comparison.md)
- [How to Automate Your Calendly Workflow with AI Agents](https://cotera.co/articles/calendly-workflow-automation.md)
- [Turn Your Calendly Meetings Into Sales Intelligence](https://cotera.co/articles/calendly-sales-intelligence.md)