In a cafe or restaurant, whether seating is tight or plentiful tends to get judged by feel. Answering exactly which hour, and which table size, fills up first takes actual usage data stacked up over time.
The daily trend and hourly occupancy are read separately. A bar that spikes in one hour only is where the table mix deserves another look. The product UI shown is the Korean build.
What it shows
Seat occupancy analyzes seat count and usage inside the store and reports occupancy by period and by hour. You can view a single hour as a snapshot or an average across many days.
If occupancy stays above 90 percent during a specific window, that's lost revenue from a seating shortage. Check your table configuration first.
When to use it
Use it as a quantitative basis for deciding the ideal seat count and the mix of two-seat versus four-seat tables. It replaces a gut call with actual usage data.
A worked example
Here's an example. Say a cafe's seat occupancy stays above 90 percent every day from noon to 1pm. That's a case for converting two-seat tables into a shared four-seat table, or redesigning the order flow to speed up turnover. Measure occupancy again for the same window after the change, and you can confirm the effect directly.
Which features to read it with
One feature alone rarely gets you to the cause. The pattern that repeats is: overlay a second feature to narrow the cause, act, then re-measure with a third.
- Measure — Overlay the hours seats fill with the hourly heatmap so you read it alongside store-wide congestion.
- Act — Change the table mix or the order flow.
- Verify — Use custom report to pull the same hours before and after the change into one comparison.
Knowing when your seats fill up most is how you prepare for turnover. For congestion across the whole store, see the real-time dashboard. Check out seat occupancy in saai insight.
