cotera

Calendly Scheduling Analytics

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

Meeting analyticsTime managementScheduling optimizationCancellation tracking

The Challenge

Calendly handles your scheduling, but it does not tell you much about your patterns. How many meetings are you actually taking? Which event types get booked the most? What is your cancellation rate? Are your availability windows actually getting used? Without this data, you are flying blind on how you spend your time.

What This Prompt Does

Pull Event History

Fetches 30 days of meetings, both active and canceled

Analyze Patterns

Finds busiest days, peak hours, and trends

Check Availability

Compares your availability windows to actual bookings

Recommend Changes

Suggests schedule optimizations based on the data

The Prompt

The Prompt

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 UserName it "Calendly/Get Current User" and call it with @Calendly/Get Current User to get the user URI
  2. Use @Calendly/List Event TypesName it "Calendly/List Event Types" and call it with @Calendly/List Event Types to get all event types with durations and active status
  3. Use @Calendly/List EventsName it "Calendly/List Events" and call it with @Calendly/List Events for the past 30 days (both active and canceled) -- paginate to get all results
  4. Use @Calendly/List Availability SchedulesName it "Calendly/List Availability Schedules" and call it with @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.

Example Usage

Try asking:

  • →"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."