Companies that employ productivity-management software have an extraordinarily in-depth view of how their teams, and individual staffers, work. Who has done what, and when--and who isn't working at all.
When Hive Software, a New York City-based startup behind a productivity tracker and workflow-management tool, took a close look at its own 25-person team's productivity patterns recently, it found a dip in tasks completed and messaging among team members in the lead-up to lunch.
"We went out and bought dried mango for the office," said CEO John Furneaux. "Maybe they were just running on low fuel."
Hive is just one of a growing number of tools that allow businesses to monitor their staff's work in minute detail. The company's competitors in this sector include Trello, a visual team-collaboration tool created by Fog Creek Software, and Asana, a project-management system developed by Facebook co-founder Dustin Moskovitz and Justin Rosenstein.
The pre-lunch lag Hive found isn't particularly surprising. But the startup recently shared with Inc. a sampling of what else it discovered from examining 250,000 completed tasks by employees of the companies that use its software. The most interesting:
Productivity spikes midweek.
Wednesday is the most productive day of the week; Friday is the least, with only 16 percent of work completed in 2017. Summer hours didn't seem to be a factor, however--the number didn't waver much as seasons changed.
Women are both more prolific and more productive than men.
With about a 50-50 split of men and women using its software, Hive experimented with breaking down messages sent and tasks completed by gender. It found that women messaged more than their male counterparts, particularly as the day went on. Messaging had a "low positive correlation" to the number of actions completed--meaning female workers appeared to complete more tasks when also actively communicating frequently.
Women also were found to be wildly more productive than men, in general. On average women assigned 20 percent more tasks than men, and completed 28 percent more. Broken down by day, women worked an average of 31 percent more. On Thursdays, the gap spiked to a whopping 37 percent.
Kanye West is wildly disruptive.
Hive noticed a dramatic effect from distractions caused by pop culture. The productivity software tracked users' activity during a significant five-hour period on Wednesday, April 25, beginning when the rapper sent this tweet:
You don't have to agree with trump but the mob can't make me not love him. We are both dragon energy. He is my brother. I love everyone. I don't agree with everything anyone does. That's what makes us individuals. And we have the right to independent thought.-- KANYE WEST (@kanyewest) April 25, 2018
That kicked off a lengthy tweetstorm. It was a Wednesday afternoon, which Hive had found to be by far the most productive time of the week for businesses. But that afternoon, as West's wide-ranging, hyper-political, sometimes defensive tweets continued, productivity dropped 55 percent. Not until an hour after he finished tweeting did productivity return to normal.
Furneaux said the finding surprised him: "55 percent? That's nuts, and shows the insane influence of pop culture in 2018." But in terms of showing how managers can use the information Hive or other productivity tools can give them, it doesn't illuminate much. What savage of a manager would attempt to stop workers from spectating a tweetstorm?
Still, what's clear is that in the very near future, if they do not already, managers may have an extremely detailed view of how, and precisely when, employees are being productive. Might it be able to anticipate when they'll slack off? Or the periods they'll become overworked?
Hive Analytics is working on it. It already knows about problem Fridays--and claims to be the first player in the task- and productivity-management space that has a predictive analytics engine. The ability to predict which teams and individuals will have too much on their plate in coming months works in part by examining planned tasks. It can then alert management to reallocate some of that work.
The challenge for managers in the future, armed with so much more information--from what precisely Sam accomplished that day, to what his workload might be in two weeks--is deciding the proper way to react. How granular can our productivity analytics be before employees start to feel Big Brother is watching them doing their work, or criticizing them for taking a minute to read their favorite celebrities' tweets? Perhaps we all should just loosen up. After all: Even the best predictive analytics can't yet forecast the next epic tweetstorm.