The Check-ins report helps you to get a better understanding if your employees are actually coming into the office. A booking shows their intent, whereas check-in gives you the confirmation. Please go over our check-in possibilities in the articles Manual check-in or Automatic check-in.
Check-in enabled?
This report is only visible when check-in is enabled for your environment!
Applies to
The check-ins report can be seen by the following administrator roles:
- Subscription Owner
- Subscription Administrator
- Building Administrator
- Booking Administrator
- Analytics Viewer
Applicable filters
| Filters | Selection type? | Description |
| Building | Multi-select | Filter on a specific or multiple building to drill down on it further. |
| Floor | Multi-select | Similar to building but on floor level. Note that if you have a building selected already you will filter on that floor specifically. |
| Area | Multi-select | Similar to the Floor filter, but on area level. |
| Week length | Single-select | Determines whether you compare a full week (seven days) or only on a workweek (five days). Note that this filter is not applicable to all graphs! |
| Period | Single-select | A filter to determine the period in which you want to evaluate your data. Filters vary from since the beginning of the year all the way back to 2021. |
Where is it most busy?
Booked workspaces versus check-ins per location
This graph compares the number of workspace bookings with the actual check-ins for those workspace bookings. It's important to note that if someone checks in without a booking, the person is prompted to make a booking as well. Also, as a check-in happens on a building level, when a person has a booking and checks-in it is assumed the user go to their initial booked location. The line plot shows the total workspaces' capacity per location.
When is it most busy?
Booked workspaces compared to check-ins over time
The bars in the graph show the absolute number of bookings per date whereas the line shows the absolute number of check-ins per date. By plotting the line over the bars you can make easy comparisons between bookings and check-in events. The week length depends on the filter Week length, it will include all seven days (including Saturday and Sunday) if the filter is set to 7 days. The workweek will be shown with a Week length set to 5 days.
Filter for correct visualization
If you have selected the Area view in the graph, it displays all the areas of all buildings. By filtering (on the right side) on a building or floor you can help in exactly visualizing and comparing the areas as you want.
Booked workspaces compared to check-ins per day of the week.
Similar to the graph explained above, is the comparison per day of the week. In contrast, this graph shows the average number of booked workspaces per day of the week compared to average number of check-ins. Again, the days that are shown are dependent on the filter Week length.
Check-ins and check-outs per time of the day
With the Check-in feature you are also able to send a check-out event. Check-outs occur when you leave the office for the day or go out for lunch (depending on the setup this could be a manual or automatic action). The graph shows you the average number of check-ins compared to the average number of check-outs per hour of the day. The number of check-ins and check-outs are counted within the hour, meaning that if you check-in or out at 9.29AM and if you check-in or out at 9.41AM it will count a total of two check-ins or check-outs. This is visualized at 9.00AM in the graph. An average is calculated based on active filters.
Check-ins per time of day
In comparison to the graph Check-ins and check-outs per time of day, this graph displays the cumulative number of people who have checked-in and checked-out. Each check-in is counted as +1 and each check-out is counted as -1. The number represents the average over the selected time period. In short, if at 8.00AM 95 check-ins are counted and at 9.00AM it shows 155 check-ins, that means 60 people have checked-in during this hour. The aggregation is identical to the graph Check-ins and check-outs per time of the day.
