Easy

Calendly No-Show Tracker

Stop guessing which meetings get skipped. Track no-shows and cancellations across your Calendly events and find patterns you can fix.

Works with:CalendlyCalendly

Free to start

1,000 credits included

No credit card required

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Setup time

~5 min

Time saved

1-2 hrs/week

Difficulty

Easy

Tools

1 connected

How it works

1

Pull Event History

Fetches all events including canceled and no-showed

2

Classify Outcomes

Separates completed, canceled, and no-show events

3

Find Patterns

Analyzes no-show rates by day, time, and event type

4

Recommend Fixes

Suggests schedule changes to reduce wasted slots

Try asking

Show me my no-show rate for the past month by event type
Which days and times have the most cancellations on my Calendly?
Track my Calendly no-shows and tell me what patterns you see

View the agent prompt

See the full instructions this agent runs on — copy, edit, or customize it

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The Prompt

Task

Track no-shows and cancellations across my Calendly events for a given date range. Identify patterns by day of the week, time of day, and event type so I can reduce wasted meeting slots.

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 Events With InviteesName it "Calendly/List Events With Invitees" and call it with @Calendly/List Events With Invitees for the specified date range, including canceled events -- paginate to get all results
  3. For events that were canceled, use @Calendly/Get EventName it "Calendly/Get Event" and call it with @Calendly/Get Event to pull cancellation reasons and details

What to Analyze

  • Total events scheduled vs. completed vs. canceled vs. no-showed
  • No-show rate as a percentage
  • Cancellation rate as a percentage
  • Breakdown by event type: which meeting types get skipped most?
  • Day-of-week patterns: are certain days worse for no-shows?
  • Time-of-day patterns: are early morning or late afternoon slots more likely to be missed?
  • Cancellation reasons: what reasons do people give when they cancel?
  • Lead time: how far in advance do cancellations happen?

Output

Overview: Total events, completion rate, no-show rate, cancellation rate.

By Event Type: Name, total scheduled, completed, canceled, no-showed, no-show rate.

Day-of-Week Patterns:

  • No-show rate by day (Monday through Friday)
  • Cancellation rate by day
  • Highlight the worst and best days

Time-of-Day Patterns:

  • No-show rate by hour block (morning, midday, afternoon, evening)
  • Which time slots have the highest completion rate

Cancellation Analysis:

  • Most common cancellation reasons
  • Average lead time before cancellation
  • Last-minute cancellations (under 24 hours) vs. advance cancellations

Recommendations: 3-4 actionable suggestions to reduce no-shows and cancellations based on the data.

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