Dashboard: Bookings

Content:

Basic requirement for the dashboard

• Advanced analytics subscription (contact Sales if you wish a demo)

• Booking system for the workspaces needing to be analysed:

  • Room: customer booking system

  • Desk: myROOMZ

  • Parking space: myROOMZ

Where does the data come from

The data for this Dashboard comes from the bookings of the workspace. We analyse only occupancy data on the previously specified working days between the specified working hours (These can be changed in the building details). For Meeting rooms, the data is gathered from the connected booking system, and for Desk / Parking spaces it is from the myROOMZ application.

Categorization vs. Number of hours

As a workspace manager, you are looking for patterns, outliers and key differentiators between the workspaces. When comparing them, the hours of utilization would appear to be the right metric, but when averaged over several days, important differentiation can be lost.

Let’s take a look to an example:

Here, each meeting room has an average occupancy of 2 hours. But as you can see, there is a clear difference. While meeting room 1 has been occupied for all of Monday, it was not used for the rest of the week.

Based on several years of experience, ROOMZ has developed a machine learning-based algorithm, allowing the simple categorisation of occupancy and bookings. The workspace will show as one of the following; Very Rarely, Rarely, Frequently or Very Frequently.

Note: unlike the basic analytics, working hours no-longer needs to be taken into consideration for this algorithm.

Organization view

If you have set your Filters, you will see a report similar to the one below. You are able to click on every bar and the report will change to show you only the selected data:

Open

1. A short overview about the workspaces in the report.

   1. In this report we see a lot of rarely booked workspaces, this could be improved.

2. In the weekly profile you are able to so how different the workspace are booked during the week. (The data is aggregated for all day's over the selected period, so several weeks or months of data could be aggregated for each day of the week. )

   1. In this example, we see clearly that the office is not well-used towards the end of the week.

3. In the monthly profile you can view the bookings over the months. (If you have selected more than one year, the data will be aggregated for some of the months, in the example above, Jan, Feb and March are an aggregation of two years data.)

4. In the selected time period you are able to see the data over the selected time period.

5. In the buildings, you can compare the selected buildings or floors by opening the data with the +.

   1. This is an easy way to see how the spaces are used differently in various buildings, or on different floors.

6. If you have set tags, you will see data relating to them reported here too.

   1. This is a great way to analyse how different teams use their workspaces.

You are able to expand every part of the Report separately by opening the details with a right mouse click (select show as a table):

Open

You will now see a larger view with the exact numbers for each section:

Floor view

On the top bar, if you change the view to Floor, some parts of the report will change:

1. On most booked, you see the 3 most and the 3 least booked workspaces.

2. If you have more than one floor in the selection, you are able to switch the floor with the navigation. Also, you can zoom in/out.

3. Using this view of the floorspace, you will easily see all the workspaces and how they are being used.