I deal heavily with workplace sensor technology. This is the tech where a sensor is placed under a desk or in a room that can tell you if space is occupied or not. Basically, it reports a 1 if it detects new occupancy and a 0 if that occupant leaves. Pretty straightforward.
From a primary data standpoint, we can use this data to understand the utilization of the office. Were we 70%, 80%, 90% occupied on average? What was our occupancy late morning? Mid-afternoon? What days of the week do we see our peaks occurring? It’s pretty cool to see some of these trends.
From a secondary data standpoint, it can report the average occupancy of the office across the day accounting for the time a given desk sat empty. If you measure an office between 7a and 7p, you may never get higher than 50% occupancy because the tails of your measurement period are extremely low occupancy. If you measure between 10a and 3p, the lunch period takes on outsized significance. If you measure between 9a and 4p, the trend changes to something else. Picking the right measurement period is tricky. Even more tricky is understanding what’s good or bad with a particular measure.
I’ve recently seen a number of requests to measure conference room utilization by counting the number of people in a room at any given time. Naturally, a 10 person conference room occupied by only 2 people is under-utilized. Unless those two people are the head of sales and a big client he’s working with. Then it’s perfectly utilized. But what about a 6 person room only occupied by 2 people? Is that under-utilized? Even if it is, is it 33% below utilization target or 67%? Identifying how to define good and bad performance is extremely difficult.
Primary data is binary – good or bad – difficult to argue with. Secondary data is open to interpretation. Focus on the primary data first. Evolve to include secondary data over time as you learn what it means to your business.
This utilization metric seems very off base. First of all, is higher utilization even good? If you’re at 100% utilization, then there are a great many people who can’t use the conference room when they want it.
The problem is a queueing problem where there is a cost to each server (conference room) and a cost to having to wait in line (can’t use the conference room when you want to).
That second cost is the hard one to figure out.
It may just be better to have a goal like “95% of the time, you can get a conference room in any given 3 hour window.” But either way, I think conference utilization isn’t what you actually care about.
EXACTLY! Defining what a good utilization is for conference rooms (in this example) is an age-old question with no clear answer. We know that 100% booked conference rooms (assuming that the bookings are valid and not just people gaming the system to hold the rooms) is bad. We know that conference rooms that sit empty day-in, day-out are bad. What we don’t know is where is the line in the middle that defines good. It’s a fixed resource that is difficult to add or remove after the go-live which means you have to plan for excess capacity on day one no matter what. Reporting a metric around this is a futile effort because you will ultimately spend all your time justifying or explaining the target or benchmark without actually using it to drive behavior or change.