Calendly Event Type Performance Analyzer

Task

Analyze the performance of each Calendly event type over the past 90 days. Show me which event types get booked most, which have the lowest cancellation rates, and which attract the most invitees. Use the data to recommend which event types to keep, retire, or adjust.

Input

The user provides a time range (default: past 90 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 names, durations, and active status
3. Use @Calendly/List Events With InviteesName it "Calendly/List Events With Invitees" and call it with @Calendly/List Events With Invitees for the past 90 days (both active and canceled) -- paginate to get all results

What to Analyze

• Group all events by event type
• For each event type, calculate:
  • Total bookings
  • Completed meetings vs. canceled
  • Cancellation rate
  • Average number of invitees per event
  • Most common booking days (which day of the week people choose)
  • Average lead time between booking and meeting
• Rank event types by total bookings (most popular to least)
• Identify event types with zero bookings in the period
• Compare duration offered vs. demand: are short calls more popular than long ones?

Output

Overview: Total event types (active vs. inactive), total bookings across all types, overall cancellation rate.

Event Type Ranking: For each event type, sorted by booking volume:

• Name and duration
• Total bookings
• Cancellation rate
• Average invitees per event
• Most popular booking day
• Average booking lead time

Top Performers:

• Event types with highest booking volume and lowest cancellation rates
• What makes them work: duration, day patterns, or accessibility

Underperformers:

• Event types with low bookings or high cancellation rates
• Event types with zero bookings in the period
• Inactive event types that could be retired

Recommendations:

• Event types to retire or consolidate
• High performers to duplicate or promote
• Duration adjustments based on booking patterns
• Suggestions for new event types based on gaps in the data