Event teams invest heavily in driving registrations, but registration count is not the same as attendance. The gap between the two has real consequences for catering, room setup, staffing, and exhibitor expectations, yet most teams plan against registration numbers rather than realistic attendance.

In this Event Data Lab report, we analyzed check-in data across 1,070+ live events to establish baseline no-show benchmarks and understand how they vary by event size and pricing model.

Executive Summary

• The median no-show rate across all events is ~20%. Roughly one in five expected attendees does not check in.
• Free events experience a median no-show rate of ~28%, compared to ~17% for paid events. The gap is consistent across event sizes but most pronounced at smaller events.
• No-show rates are highest at small events (10-49 attendees, ~32% median no-show) and stabilize around 19% for events with 150+ attendees.

Dataset Overview

Dataset overview

• 1,070+ live events with check-in data
• Check-in rate defined as check-ins divided by total expected attendees (including speakers and non-registrant participants)
• Events with very low attendance and test or internal events were excluded
• Events with check-in rates exceeding 100% were capped at 100% (8 events, likely reflecting minor data timing artifacts)
• One internal sample event was excluded
• Data aggregated and anonymized across live events

Metric definition

No-show rate is defined as the proportion of expected attendees who did not check in.

No-show rate = 1 - (Check-ins / Total Expected Attendees)

This metric captures the gap between expected attendance and actual onsite presence. It does not distinguish between attendees who cancelled in advance and those who simply did not appear.

What the Data Shows

Overall No-Show Benchmarks

Across all events with check-in data, the median no-show rate is approximately 20%. The mean is higher at 29%, indicating that a subset of events experience severe no-show problems that pull the average up.

The distribution is wide:

• 21% of events have no-show rates at or below 10%
• 49% of events have no-show rates at or below 20%
• 17% of events lose more than half their expected attendance

Free vs Paid Events

The free/paid distinction is the strongest predictor of no-show rates in this dataset.

No-show rates by pricing model

• Free events: ~28% median no-show (N=389)
• Paid events: ~17% median no-show (N=683)

The 10 percentage point gap is consistent but not uniform. At the 25th percentile (worse-performing events), the gap widens: free events hit 47% no-show compared to 32% for paid events.

Among free events, 22% lose more than half their expected attendance. For paid events, the figure is 14%.

No-Show Rates by Event Size

Smaller events experience materially higher no-show rates.

No-show rates by total expected attendees

• 10-49 attendees: ~32% median no-show
• 50-149 attendees: ~23% median no-show
• 150-499 attendees: ~19% median no-show
• 500-999 attendees: ~18% median no-show
• 1,000+ attendees: ~19% median no-show

The decline is steepest between very small events and mid-size events. Once events reach 150+ expected attendees, no-show rates stabilize around 19% regardless of total size.

The Interaction: Size and Pricing

The free/paid gap is most dramatic at smaller event sizes.

• Small free events (10-49 attendees): ~37% median no-show
• Small paid events (10-49 attendees): ~17% median no-show
• Large free events (500+): ~24% median no-show
• Large paid events (500+): ~18% median no-show

Paid events show remarkably consistent no-show rates (~15-18%) regardless of size. Free events are where most of the variability lives: small free events lose more than a third of their expected attendance, while larger free events perform closer to paid events.

Key insight: The median event loses roughly one in five expected attendees to no-shows. Free events and small events are disproportionately affected. Paid events show stable, predictable no-show rates around 18% regardless of size, while free event no-show rates vary widely and are significantly worse at smaller scales.

Practical Implications for Event Teams

• Plan operational logistics (catering, seating, staffing, materials) for 80% of expected attendance at paid events and 70% at free events. These are more realistic planning numbers than registration counts.
• Small free events should plan for up to 40% no-shows. Overbooking or waitlist strategies may be appropriate for events in this category.
• Paid events can rely on more predictable attendance regardless of size. The financial commitment acts as a consistent filter.
• Teams tracking event performance should distinguish between registration metrics and attendance metrics. A high registration count with a high no-show rate may indicate a marketing-to-commitment gap rather than a successful event.

Download the Full Report

Download the full Event Data Lab report

Get the complete dataset, extended benchmarks by size and pricing, and detailed methodology notes.

This report is part of the Event Data Lab, an ongoing research initiative analyzing real-world event performance across registration, onsite operations, engagement, and ROI.