cotera

Calendly Availability Optimizer

Stop offering time nobody books. Compare your Calendly availability against actual booking patterns and optimize your schedule with data.

Availability optimizationBooking patternsTime managementSchedule analysis

The Challenge

You set up Calendly availability once and rarely revisit it. But your actual booking patterns shift — maybe Fridays are dead but Tuesday mornings are overbooked. You are offering availability that nobody wants while blocking time that people would book. Without data, you are guessing.

What This Prompt Does

Map Availability

Pulls your configured availability windows and rules

Analyze Bookings

Cross-references 60 days of events against available slots

Find Dead Zones

Identifies time slots that rarely or never get booked

Suggest Changes

Recommends specific availability adjustments based on data

The Prompt

The Prompt

Task

Compare my configured Calendly availability against my actual booking patterns over the past 60 days. Find underutilized time slots, identify peak booking times, and suggest schedule changes to maximize bookings.

Input

The user provides a lookback period (default: past 60 days). No other input needed.

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 Availability SchedulesName it "Calendly/List Availability Schedules" and call it with @Calendly/List Availability Schedules to get all configured availability windows and rules
  3. Use @Calendly/List EventsName it "Calendly/List Events" and call it with @Calendly/List Events for the past 60 days (status "active") -- paginate to get all results
  4. Use @Calendly/List Event TypesName it "Calendly/List Event Types" and call it with @Calendly/List Event Types to get all event types with their durations and scheduling rules

What to Analyze

  • Map configured availability windows to actual booking frequency per slot
  • Calculate utilization rate per time block (e.g., 9-10am Monday = 80% booked, 4-5pm Friday = 5% booked)
  • Identify peak booking hours: when do people actually book?
  • Identify dead zones: available time that rarely or never gets booked
  • Compare event type scheduling rules to actual demand
  • Look for mismatches between when you offer availability and when people want to meet
  • Check if buffer times or minimum notice periods are too restrictive

Output

Availability Overview: Total hours offered per week, total hours booked per week, overall utilization rate.

Peak Booking Times:

  • Top 5 time slots by booking frequency
  • Day-of-week demand distribution
  • Hour-of-day demand distribution

Dead Zones:

  • Time slots with less than 10% utilization
  • Days with consistently low bookings
  • Available hours that have never been booked in the period

Event Type Analysis:

  • Which event types are most popular
  • Whether scheduling windows for each type match actual demand
  • Duration utilization: are people booking 15-min calls when you also offer 30-min?

Recommendations:

  • Specific time slots to add or remove from availability
  • Days to open up or cut back
  • Event type changes: consolidate, retire, or adjust durations
  • Buffer or notice period adjustments based on booking patterns

Example Usage

Try asking:

  • →"Compare my Calendly availability to my actual bookings over the past 2 months"
  • →"Which of my available time slots never get booked? Show me the dead zones."
  • →"Optimize my Calendly schedule based on when people actually want to meet"