Skip to main content
All CollectionsReports and Insights
Occupancy Intelligence Forecast Report
Occupancy Intelligence Forecast Report
K
Written by Kiren Dosanjh-Dixon
Updated over 4 months ago

Using your feedback, we have completely revamped the RICOH Spaces Insight tab to provide you with a handy panel of reports. Combining multiple metrics taken from the suite of reports, the Occupancy Intelligence Insights, delves into the anticipated occupancy trends of your locations and meeting rooms, gaining invaluable insights into the expected foot traffic, meeting room utilization, and the dynamic interplay between employees and visitors. Our refined metrics pave the way for strategic decision-making, optimizing resource allocation and enhancing the overall efficiency of your workspace management.

At the top of the report, you will see the available filters:

  • Location Filter - You can add one or more locations and the report will provide all the data relating to your choice. This is a core filter and as such how all other filters act will depend on the selection here.

  • Day date - Choose a predefined filter such as "last 90 days" or choose your custom start and end date.

  • Expected Status - Filter by users' potential or expected status.

Note: Whenever you change the filters you must click on the reload icon at the top right of the report to refresh.

Key Metrics of the Services Report

Expected People overview

Based on room bookings, desk bookings & visitor management data.

  • Building Limit

This is your maximum capacity for the selected location/s.

  • Average Excepted People Count

This metric represents the average count of expected people at the location/s selected. Expected people have accepted meetings

  • Peak Excepted People Count

The Peak Expected People Count indicates the highest anticipated number of individuals at the location(s).

  • Total Excepted People Count

Total Expected People Count is the sum of expected people across all selected locations.

  • Occupancy Intelligence

Occupancy Intelligence is a comprehensive metric that involves the count of expected bookings and visitors for the filtered location/s.

  • Excepted People Count by Day of the Week

This metric categorises the expected people count based on each day of the week. In this example, Tuesday has the highest total expected count of people.

Meeting Room Intelligence

This section helps you answer questions such as:

how many meetings are planned in the period, and how much time is available vs booked?

What's the longest and shortest meeting, and what's the longest period a room is utilised back-to-back?

What rooms are not being utilised over the period?

How many bookings are usually made on the day? Therefore, what can we expect to see in terms of bookings that are not pre-planned?

  • Meeting Room Occupancy %

It represents the utilisation rate of a meeting room, calculated as the percentage of time the room is occupied compared to its total available time. This metric offers insights into the efficiency of meeting room usage, helping optimize scheduling and resource allocation.

  • Meeting Room - Time Booked vs Available Minutes

This metric compares the total time a meeting room is booked for meetings against its available minutes. It provides a detailed breakdown of how much of the meeting room's time is scheduled for meetings, offering a clear view of utilisation efficiency.

  • Meeting Room Usage

Meeting Room Usage is a comprehensive metric encompassing various aspects of meeting room utilization such as maximum consecutive meeting minutes, minimum consecutive meeting minutes, non cancelled booking count.

  • Planned vs. On the Day - Meetings

This graph categorises scheduled meetings into two types: those that were planned in advance and those booked on the day along with their cancellation/non-cancellation rate. This metric helps distinguish between different meeting types, providing insights into the organization's meeting culture and allowing for tailored strategies to accommodate both types efficiently.

In this example, we can see bookings made on the day are less cancelled than the ones planned in advance.

  • Forecast Expected People

    This graph uses previous data to forecast the count of people in the building. In this example, we can see that the forecast predicted more people than the expected count of people.

Did this answer your question?