Bringing Moneyball to the workplace

Algorithms are an increasingly important part of our daily lives, with their power seeming to rise by the day.  Over a decade ago Moneyball was published, and unveiled the potential for an analytical approach to talent management in a baseball setting.

It has since spread into a wide range of other sporting contexts, but can it ever apply with equal veracity in the workplace?  A new MIT spinout called Sociometric Solutions believes it can.

The system uses sensor ID badges and a variety of analytics tools to track a whole host of behaviour data on employees.  The aim is to provide the kind of insights that will eventually lead to increases in productivity.

“‘Moneyball’ is putting numbers on behavior and using that data to build a baseball team. But what if I could say, ‘Here’s how you need to talk to customers, here’s how people need to collaborate with each other, and here are the things that lead to outcomes such as turnover, sales, and job satisfaction,'” says Ben Waber, the co-founder of Sociometric. “Individuals can use that data to boost performance, and a company can use that to help set up an environment where everybody’s going to succeed.”

The system utilizes ID badges with built in snsors that track both the employees location, and the direction they’re facing.  Additional sensors can measure things such as whether someone is engaged in a conversation, whilst acceleramoeters track the energy levels of employees.  There are also built in microphones to monitor how employees talk, including their speed and tone of voice.

That’s a whole lot of data, which is available to both individuals, and their employers (albeit in anonymous form).  The hope is that when this data is combined with more explicit information from performance reviews, interviews and the like, it gives an organization great insight into how they can make their workplaces more productive.

The system has already been deployed in around 20 live organizations, plus around 60 more research based institutions, with a decent degree of success, not all of it expected.  For instance, one implementation of the service discovered that the more people an employee ate lunch with, the happier and more engaged they were at work.

This finding led to the implementation of longer tables in the staff cafeteria, to therefore enable people to eat together in larger numbers, with a resulting rise in productivity of 36%.

“It’s crazy that something as trivial as physical space, as the size of the lunch table, could affect productivity,” Waber says. “The CEO obviously wasn’t thinking about that, but those are the biggest drivers of how people communicate with one another.”

Pleasingly for those in the social business world, the initial installations of Sociometric has pointed to the importance of collaboration amongst employees.  For instance, collaboration between sales staff was found to be a better predictor of their revenue than their sales ability with customers.

“If someone figures out a really good way to pitch to customers, you talk to them and learn how to make that pitch, which makes things more efficient,” Waber says. “Even if you’re competing on performance metrics, if you know each other well enough, you’ll share. That’s exactly what we see.”

There are already a number of efforts to better optimize the workforce, but many of these attempts use surveys to better understand the networks we use at work.  The sensor based approach of Sociometric is therefore an interesting deviation from that, and certainly one to follow with interest.

Original post