Get actionable insights into how your office spaces are used — Office Analytics helps you understand booking behavior, space utilization, and no-show trends. Office Analytics is available on the Business and Enterprise plans.
1. Overview
Office Analytics provides you with visibility into how resources such as desks, meeting rooms, and parking spaces are used across locations. Insights help optimize space planning, detect no-show patterns, and make data-driven decisions.
💡 You'll now find Analytics in its own Intelligence area in the app switcher (alongside App and Admin). The Office, Workforce, and Advanced Analytics dashboards all live there. Your access permissions haven't changed — only the location.
- Users in the office: Shows how many people have set their status to 'in the office'.
- Bookings: Shows how many resources were booked at that time.
- No-shows: Missed check-ins.
KPIs like average desk utilization or users in the office without bookings give you detailed insights. Definitions are included directly in the dashboard.
💡 Analytics data is refreshed once per day (during the morning) and isn't updated in real time. Reports are designed primarily for retrospective insights. Data is displayed up to 30 days into the future.
💡 Office Analytics doesn't account for set holidays. On public holidays, data may appear missing because employees typically don't schedule or book on those days.
2. KPI definitions and use cases
Note: All booking and utilization KPIs apply to the selected resource type and are calculated for the selected time frame.
| KPI | Definition | Use Case | Example Calculation |
|---|---|---|---|
| Daily average users in the office | The average number of users scheduled to work in the office during the selected time frame, whether or not they booked a resource. | Understand how many employees are present in the office over a given period. | 50 users scheduled over 5 days → 50 ÷ 5 = 10 average users per day. |
| Daily average bookings created | The average number of bookings made per day for the selected resource type. | Identify trends in booking activity to optimize resources. | 30 bookings over 3 days → 30 ÷ 3 = 10 average bookings per day. |
| Daily average no-shows | The average number of bookings automatically canceled due to missing check-ins. | Monitor adherence to check-in policies and spot patterns in missed bookings. | 5 no-shows over 5 days → 5 ÷ 5 = 1 no-show per day. |
| Users in the office with bookings | The number of users working in the office with at least one booking. | Compare how many people attend with vs. without booking resources. | 15 users with bookings out of 50 total in office. |
| Users in the office without bookings | The number of users present in the office without any bookings. | Understand resource-independent office visits. | 35 users without bookings out of 50 total in office. |
| Daily average resource utilization | Percentage of bookable time actually booked for each day. Bookable time = the office's opening hours. | Evaluate how efficiently desks or rooms are being used. | 8 desks used for 6h, out of 10 desks for 10h: (8×6)/(10×10) = 48%. |
| Daily average resource booking frequency | How often a resource is booked at least once per day in the selected period. | Check if desks or rooms are used regularly or only occasionally. | Resources booked on 3 out of 5 days → Frequency = 60%. |
3. Roles and access
Only certain roles can access the Analytics dashboard:
- Global Admins: Full access to all data.
- Office Admins: Access limited to their assigned offices.
4. Export and save data
To save your analytics:
- Click Export report in the top right to download an Excel file of the bookings in the selected period.
- Alternatively, right-click the dashboard and select Print to save a PDF version.
This is useful for sharing reports, archiving snapshots, or integrating into presentations.
Check-in status values in the export
When exporting Office Analytics, you'll get both a Booking Status and a Checkin Status column. The Checkin Status column indicates whether a check-in was required for the booking and (if required) whether it happened. The export shows the technical values below; the plain-language name follows in parentheses. The export contains the value stored for each booking, which can differ from what the app shows: for example, a canceled booking shows Check-in not available in the app but notCheckedIn in the export.
-
checkInNotAvailable(Check-in not available): Check-in wasn't in play for this booking, for example because check-in is turned off for the resource type or office. -
notCheckedIn(Not checked in): Check-in was required, but the user did not check in. This includes bookings that were canceled before a check-in and bookings that ended without one. -
checkedIn(Checked in): The user checked in. The value stays even if the booking is canceled afterwards. -
readyForCheckIn(Ready for check-in): Check-in is turned on and the user has not checked in yet. A booking that ended without a check-in can keep this value.
💡 Tip: The Checkin Status column is not a no-show count: canceled bookings and bookings that ended without a check-in both show notCheckedIn, and an ended booking can also show readyForCheckIn. Use Booking Status to tell canceled from completed bookings, and the Daily average no-shows KPI to track missed check-ins.
5. FAQs
Closed spaces are treated as inactive and are excluded entirely from utilization and booking frequency metrics. They do not count toward the total available resources during the period they are closed.
If a space was closed for part of the selected time frame (e.g. 14 days out of a 30-day month), the analytics are pro-rated: the space is only included in calculations for the days it was open and bookable. The 14 closed days are excluded from the denominator, so your utilization and booking frequency percentages reflect actual availability.
Yes. It counts everyone who planned to work in the office, whether or not they booked a desk or other resource. If the number looks lower than you'd expect, remember it's a daily average across every day the office is open — so open days with few people in (for example, weekends when the office is open) pull the average down.
The two pull from different sources, so they can differ for the same past date:
- Office Analytics reads from a daily snapshot, which is why the page shows a "Last updated" timestamp. It reflects who was recorded as in the office on that date, as of when the snapshot was taken.
- The scheduling export reads live data, which changes as users are added or removed.
When employees leave and their historical records are removed, the live export shows fewer people than the frozen snapshot for the same past date. For historical reporting (for example, a board deck), Office Analytics is the more stable source, because the export erodes over time as users are removed. Check the "Last updated" timestamp on the page to see which snapshot you're viewing.
They answer different questions and aren't directly comparable. Office Analytics is a planning and utilization view based on daily averages for a resource type and time frame. Workforce Analytics expresses attendance as a share of users over the selected period. A daily-average headcount and a percentage-of-users figure won't line up 1:1, even when the numbers happen to look close.
Resource utilization is based on time: it divides the booked time by the bookable time, which is the office's opening hours. A 3-hour booking on a day with 10 opening hours therefore counts as about 30% for that desk. There is no option to count every booking as a full day in utilization.
Depending on what you want to measure, use one of these instead:
- Was the resource used at all? Open the Resource booking frequency tab (Daily average resource booking frequency). It counts a resource as booked if it has at least one booking that day, regardless of duration.
- How many days did people spend in the office? Use Workforce Analytics, which counts scheduled days per user. When you select Show half day schedules as 0.5 day, half-day schedules count as half a day.