There are no better tools to use for office optimization than workplace analytics. Measuring how your office resources are used can help companies reduce office space and change current layouts to cater to employee needs. However, data needs to be interpreted which requires knowledge about what type of data that can be extracted, and what conclusions that can be drawn from it. In this blog post, we will present the key metrics, the hardware needed to produce that data and some example cases of how it can be used.
In workplace analytics current form, there are three types of data that can be used to measure office space.
Booking data refers to the statistics that can be extracted from the bookings of your office resources. One thing that you will probably want to do when you measure your office is to see how often your resources are booked. This data is easy to obtain if you already have a booking system installed at your office. From the booking data, you can find out some key insights.
- How often your resources are booked.
- Which resources are the most popular (based on number of bookings, percentage of bookings, time per booking)
- When resources are booked the most (periods, day of week, time)
- In what areas resources are booked the most.
Presence data is an important complement to Booking data to gain full insights on how your office space is used. Without presence data there are some important metrics that will be unavailable. For example, resources can be booked but never used. To create optimal office utilization, we need to know how often booked resources are actually used, and how often resources are used without being booked. If you have desks and rooms that are non-bookable in your office, presence data will be the only way to understand the utilization.
To start measuring presence data, you need presence sensors for rooms and desks. You can read more about sensors here. When adding presence sensors to your workplace analytics you will be able to measure:
- Actual usage (no. of minutes a resource is used, percentage of day/week)
- Percentual usage compared to full capacity.
- Bookings vs. usage rates – number of minutes booked vs. number of minutes used.
- How many resources that are booked but not used (count, percentage, etc).
The descriptive data can be used to understand what about the rooms and desks that creates variety in bookings. Descriptive data is important to understand the complete picture of how office resources can be optimized. Some examples of descriptive data can be:
- Location of the resource (Building, Floor, zone)
- Equipment present (Ergonomic desk, phone, Screen, etc)
- Time and date
- Opening hours for office
- Resource Name
Combining the Data
All information needed to optimize your office space can be obtained by combining these data sources. Some examples of questions you can have answered include:
- Are you reaching a specific utilization goal? Select a goal and measure booking data to see if the goal is achieved.
- Why are we/are we not reaching our utilization goal? Check why office resources are booked the way they are by using presence data and the descriptive data to investigate.
- What areas should we reduce? Look at the booking and presence data and decide what office resources that can be reduced. Look at the percentage of usage for either complete buildings, floors or zones and determine.
- Should we increase the presence of any office resources? Check if there are any resources that you need more of. Look at the bookings and utilization of rooms and desks and add some descriptive variables. Analyze which resources are overbooked and add more of those resources.
How to use workplace analytics – some example cases
The Unused Desks
When analyzing the bookings of the desks in your office, you find out that the desks in the open area zone in the office are booked 80% of the workday, you alert the managers, and they seem pleased. However, you start passing by the open work area and the results from the bookings does not seem to match reality, they are always empty when you pass by.
You decide to check the sensor data, you can see that the desks are actually used about 20% of the booking time. For some reason, people seem to book, but not actually sit at the desks, why? Starting the investigation, you also decide to analyze the other desk area, the quiet zone. You can see that the desks in the quiet area that are used and booked 90% of the time, a staggering difference, why? When analyzing the presence duration time for both desk areas, the average presence duration was 5 hours for quiet work area and 1 hour for the open area, indicating that the desks in the open area have faster movement.
When investigating further, you find out that more employees are interested in a quiet work area when they book desks, and that the desks in the open area are used as touch down desks before meetings. This could be concluded by comparing the desk bookings and desk presence data with the meeting room bookings. Based on this, and because desks still need to be available, you change half of the desks in the open work area to hot desks, and the rest will become a new quiet area with longer bookings. After this, the total utilization increases to 70% for all desks.
The Rooms that no one booked
You have decided to take a closer look at the room bookings in your office. When you look at the data, you can to your surprise see that the meeting rooms on the 4th floor are never booked. You get a little worried, why isn’t anyone booking these rooms? You check the equipment, and the rooms have the same equipment as the rooms on the 3rd floor which have a much higher booking rate, so it cannot be lack of equipment. There are no error reporting’s either, so it is not an issue of quality. To be certain, you decide to also check the presence data and find out that the rooms are actually used 40% of the time! But why isn’t anyone booking?
When checking the data more thoroughly, you can see that the average time present adds
up to roughly 10-15 minutes, this gives you an indication that the meeting rooms are not used for actual meetings. When you check the map, you can see that the meeting rooms are in close proximity to the sales team’s zone. You make the assessment that the sales team is probably using the rooms to take quick phone calls and do not bother booking. You quickly realize that this is not a problem with usage, but a problem with the booking system.
With this information, you decide to create ad-hoc rooms with busy lights that indicates occupancy instead of booking. You also decide to take away the tables in the room and instead add armchairs so that the sellers can take their calls more comfortably.
Investigating before investing
The company is looking at upgrading the equipment for the desks at the office. Right now, all desks have one screen, but you are thinking about adding two to promote employees to come into work more often. You are also looking at adding some ergonomic desks to make them more comfortable. However, this investment will cost the company a lot of money, and you want to make sure that employees will actually use them more than the single screen areas.
To test this, you decide to add two screens on randomly selected desks throughout the building. You also alert the employees that these desks are now available. After two weeks, you analyze the data. You can see that the desks that had dual screens and ergonomic desks added actually increased the bookings with 30%. You could also see that employees tended to sit longer at their desks compared to when employees did not have ergonomic desks. Based on these results, you conclude that a total upgrade in equipment would be worth the money in both office usage, employee satisfaction and work efficiency.