When most people consider a day at work, the first thing that comes to mind is their meetings. When do I have meetings today? Which of these meetings is going to be useful, and which will be long and boring? Will I have time to eat between meetings today? Despite the fact that meetings dominate most work days, there still aren’t many resources for understanding or streamlining calendars. The few that do exist often fail to consider context—such as company meeting culture, meeting prioritization, or user’s personal preferences—in their suggestions, making them less useful or even unhelpful. At Cultivate, we capture unique calendar data and are able to dig into those pieces of context more deeply than other tools can. Calendaring is a problem we hope to improve in the near future, and the first step is learning about calendar trends and patterns. With that in mind, we wanted to share some of the most interesting insights we’ve uncovered in the past few years.
We started by creating a standard calendar across ten weeks for each user. That allowed us to understand what an average week looks like for any given person and calculate weekly measures like hours in meetings, focus time, predictability and meetings by day of the week. We removed meetings with fewer than 2 participants (usually appointments or individual tasks), meetings greater than 3 hours long (often out of office notices or one time full-day events), and meetings a user either declined or did not respond to. This resulted in a heat map calendar that mimicked a user’s standard week.
Below you can see a sample calendar for someone who has pretty reliable meetings on Mondays from 11am-12:30pm and some early meetings on Tuesday and Fridays. The rest of their meetings seem to vary week to week.
Conversely, this user has many weekly meetings that seem to recur with a couple of hours free over lunchtime. Their weekly schedule is more predictable than the example above and has more dedicated focus time as well.
Once we had individual calendar predictions, we were able to calculate metrics at a company-wide level. We were interested generally in how long people spend in meetings each week, how predictable their schedules were from week to week, and how many focus hours (blocks of two hours or greater without meetings) people had per week. We were also curious how those metrics varied by company. Below you can see the results of that analysis.
In general it looks like people spend about a quarter of their time on a weekly basis in meetings. However, at Company 2, that number rises to nearly half! These companies obviously have different cultures around meeting use.
We can also look at meetings by day of the week. Company 2 still has vastly more meetings than the rest of the companies in this analysis, but you can see that all four of our companies hold most of their meetings in the middle of the week, leaving Monday and Friday for catching up from the weekend and preparing for the weekend.
The next interesting metric is focus time. How concentrated are meetings throughout the day? Can people focus on work between meetings, or are they constantly in and out of meetings and preparing for the next? We know company 2 holds the most meetings, so we might expect them to have the least focus time, but that’s not what we found. Instead it seems like they are able to at least stack their meetings in such a way that they have similar focus time to our other companies. But Company 4 does the best job of that, allowing an average of 15 uninterrupted hours per week.
Finally we wanted to see how predictable a user’s week is from company to company. That looks at whether meetings are usually more recurring or more ad hoc. We can calculate predictability by using the average work week, which gives us a probability of meetings at any given time. For instance, if a certain time block has a probability of 0.8, that means the user has a meeting at that time in 8 of the 10 weeks we analyzed. Conversely, if a user has a lot of blocks with 0.2 or 0.3 probabilities, they only have meetings 2 or 3 of those weeks at those times, which means their schedule is less predictable. We can average these probabilities to create a predictability measure.
Predictability allows people to schedule their work around their meetings more easily and should lead to greater productivity. This tracks pretty closely with meeting hours in general, but it’s good to know that despite having twice as many meetings, Company 2 is able to at least keep weeks very predictable for their employees.
These metrics are a great starting point to analyze company meeting culture. We have also analyzed how each of these variables impacts digital behavior, which can give us greater insight into how meeting hygiene impacts productivity and digital culture on a company by company basis. Understanding meeting culture and impact on behavior, such as cognitive load, is helping Cultivate create tools to improve the employee experience.
Rachel is the Senior People Scientist at Cultivate. As a psychologist, she’s always been interested in people: how we think, grow, evolve, and interact. She is excited to help Cultivate users interpret their behaviors through a scientific, research-based lens.