36,035 Tickets Issued, 26,705 Admissions — What a 74.1% Check-In Rate Really Tells You

The short version

From July 15 to October 13, 2026 — 90 days — TixPass handled 13 events. 21,585 completed orders, 36,035 tickets issued, 26,705 admissions confirmed. Converted to a check-in rate, that's 74.1%.

The first instinct when you see that figure is to read it as "25.9% didn't show." But the way the number actually gets used on the ground is closer to the opposite. A check-in rate isn't a grade stamped on an event that already ended — it's the baseline for how you assign people and hours at the next one.

Issuance and admission were never the same number

Issuance is the moment a ticket comes into existence. Admission is the moment that ticket clears the gate. Days or weeks sit between the two events. In that gap the weather turns, schedules collide, and the friend who was coming along drops out.

21,585 orders producing 36,035 tickets means a single order usually carries several seats. When buying happens in twos and fours, you have to look at tickets, not orders, to see how many bodies will be standing at the gate. Box office staffing is sized to people, not to receipts.

Which is why an operations report that says only "○○ sales" gives the field almost nothing to work with. What lines up in front of the window isn't an order form. It's a person.

What happens when you read 74.1% as a score

The moment a check-in rate becomes a grade, two distortions follow.

First, people want to move the number. Loosen the scanning, or skip the check entirely and just wave people through, and the "check-in rate" stays low while the actual visitors vanish from the record. The number gets neither better nor more accurate.

Second, you start comparing things that shouldn't be compared. These 13 events across 90 days are wildly different animals. A months-long exhibition and a one-night performance, a paid-ticket event and one mixing invitations with registrations — a check-in rate means something different in each. The overall 74.1% average is not a ranking tool for individual events; it's a background value showing the shape of the data as a whole.

In an organization that scolds teams for a low check-in rate, the check itself quietly disappears by the next quarter.

The 9,330-ticket gap isn't "loss" — it's headroom

Subtract 26,705 admissions from 36,035 tickets and 9,330 remain. Treat that purely as no-show loss and there's nothing you can do with it. From an operations standpoint, though, it's also load the venue didn't have to absorb.

When check-ins are recorded, you can slice that gap by time slot. The check-in rate for a morning session and for the slot two hours before closing are not the same. Weekends and weekdays differ too. Once you know that, there's no reason left to spread box office staff evenly.

• Time slots where check-in stays high → concentrate gate and scanning staff
• Time slots where check-in runs low → room to release more walk-up inventory
• Days where the gap widens sharply → days to move your reminder send earlier

That third one is the most practical. Whether a reminder lands before the event date changes the entire picture at the gate. You need the numbers before you can decide *when* to send.

The admission peak is already written in the issuance data

The question we hear most often on site is "when will the crowd hit?" The answer isn't in the day itself — it's sitting in the booking data first. Multiply which sessions had heavy issuance by that session's historical check-in rate and you get a range for how many people will actually stand at the gate.

At a continuously operating exhibition space like the Lee Ungno Museum, that range sharpens as it accumulates. Once you've stacked records for the same weekday and the same time slot, you build the staffing sheet from last week's numbers instead of gut feel. Ninety days is long enough to establish that baseline.

Short-run events like trade fairs work the other way: day one's check-in rate decides how you deploy on days two and three. If admissions clustered in the morning on day one, you move the opening crew earlier on day two. What this takes isn't a sophisticated forecasting model — it's being able to see yesterday's numbers this morning.

Why we're publishing this

A 74.1% check-in rate isn't a number you brag about. That's exactly why it falls out of most reports. Sales revenue and tickets issued survive; how many people actually walked in gets quietly omitted.

But the person designing operations doesn't need the issuance count — they need the number that cleared the gate. 26,705 has to exist in the record before you can set the next event's box office headcount and opening time on anything more than instinct. Hide the gap and you can't use the gap.

Frequently asked questions

Is a 74.1% check-in rate low?

The standard for high or low differs by event. This figure is an aggregate, anonymized total across 13 events run over the 90 days from July 15 to October 13, 2026. Exhibitions, performances, and trade fairs are mixed in, so it can't be converted into a verdict on any individual event — read it as a background value showing the shape of the overall data.

Are all tickets issued but never checked in simply no-shows?

You can't conclude that. The gap between 36,035 tickets issued and 26,705 admissions confirmed includes genuine no-shows, but also cases where the check-in step was skipped on site. So before interpreting the number, we first verify whether admissions are being recorded consistently at every gate.

How do you use the check-in rate for staffing?

Apply a session's historical check-in rate to its ticket count to get a range for how many people will actually stand at the gate. Use that range to concentrate scanning staff in specific time blocks, and open more walk-up inventory in the slots where check-in runs low. The same headcount covers a longer peak than it would with even distribution.

How much of the aggregate data do you publish?

The figures in this post are aggregate and anonymized. Sales and admission data for an individual client or a specific event belongs to that organizer and is never published without consent. Each organizer views their own event data directly in the operations dashboard.