Fiona Smith Columnist

Fiona writes on workplace issues, including management, psychology, workplace design, human resources and recruitment. She is a former Work Space editor at The Australian Financial Review and has also covered property, technology, architecture and general news.

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Driven by data: moneyball recruitment takes away the guesswork

Published 05 June 2013 07:44, Updated 06 June 2013 13:54

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Driven by data: moneyball recruitment takes away the guesswork

Sears monitors social media and uses topics trending among people they want to hire, such as TV’s Family Guy, in its ad campaigns.

Most of what we know about hiring great people is wrong.

But don’t despair. If you discover what really does make the difference between your best employees and the rest, you may be able to fish from a pool of undiscovered talent, more productive than before, cheaper, and easier to hire and keep.

This is the promise of “moneyball” – the analysis and synthesis of all the data that employers (mostly) already have on their employees.

The data-driven moneyball strategy could be the next big thing to hit recruitment and talent management since the advent of online hiring. Quite simply, it aims to take the guesswork, gut feel and prejudice out of hiring and promotion decisions.

For instance, did you know that casual workers who fill out online job applications with third-party browsers like Firefox or Chrome perform better and change jobs less often than people who use Internet Explorer?

US predictive analytics company Evolv analysed data from up to 100,000 employees to discover that fact.

And why should the choice of browser matter? The vice-president of global sourcing and talent strategy at Randstad, Glen Cathey, speculates that people who have taken the trouble to download a browser that has not been pre-installed by the computer manufacturer have made a conscious decision that they prefer the performance of something else.

“It indicates a higher IQ and that they are better performers,” he says. “It is a lot about exercising independent judgment.”

Here’s something else you probably didn’t know: people with a criminal background are better call centre employees than cleanskins, according to Evolv.

Think about how grateful you would be if a company was willing to give you a chance, despite your blemished record. That kind of makes sense, doesn’t it?

“They have more to prove. They need the job,” says Cathey.

And the data also shows that job-hoppers are no more likely to quickly quit than people who have a history of staying put. That is another much-maligned group of job-seekers that could be worth another look.

Data-based conclusions like these cast a new light on hiring requirements. The things we used to think were important, like a clean record and job stability, can fade into irrelevance once you start looking for evidence to prove that they really matter.

Job myths exploded

And what about the biggest requirement in hiring: previous experience? It is still the “big daddy” in all job interviews. Hiring managers generally believe that people who have done the job before are a better bet.

Cathey says that for hourly (casual) workers, people with no previous experience in a similar job had the same probability of survival over 180 days as experienced workers. So, for those kinds of jobs, having done the job before makes no difference.

“For hourly workers, it is not necessary to pay top dollar for experienced labour,” he says.

According to Evolv’s white paper Workforce Wives Tales , “All the conventional wisdom about how to better recruit, retain and manage an hourly workforce can and should be tested. That may sound like a pipe dream, but it’s not.”

What is moneyball?

The strategy of moneyball was popularised by a book from journalist Michael Lewis, Moneyball: The Art of Winning an Unfair Game, and a movie based on it, which detailed the journey of the Oakland Athletics baseball team, which was gutted when far richer clubs stole its best players. The club’s general manager Billy Beane was sold on the idea of looking solely at statistics to find talented players that the other clubs overlooked.

Many of these players had significant weaknesses, but if they were put in the right position and operated as a team, they could do what they did best, while not being called upon to be all-rounders. These players were described in the movie as being “like an island of broken toys”.

Thanks to moneyball, the club achieved the longest unbroken string of wins in the history of the game and, with a salary budget of $41 million, outdid clubs who were spending three times as much.

The lesson here for employers is that an ideal employee could be someone they would typically reject.

“Many employers are discriminating against people . . . they may be avoiding better performers. We are going to have to make more data-based decisions,” says Cathey.

“I’m not saying you have to hire people you don’t like, based on numbers, but I think [hiring] is going to be a whole picture put together, more like a scorecard.”

And the really good news for employers is: if you stop fighting over the same small pool of people and fish elsewhere, you can save a lot of money.

“If you could identify the overlooked people, you are no longer fighting the war for talent over the same people.

“Companies can technically pay less because there is a premium to pay when you are all after the same people,” says Cathey.

“In Silicon Valley, everyone wants to recruit from the same five companies.”

A whole new ballgame

For many people, recruiting is an art, rather than a science. They hire people they feel comfortable with, which is why we have a monoculture in so many organisations.

The idea that people can be reduced to an algorithm, a series of numbers, is a challenging concept to many.

“A lot of people don’t like to be quantified, but the reality is, we can be. It is not any different to athletes,” Cathey says.

Retailer’s secret sauce

One company running with the moneyball approach is Sears Holdings Corporation,which has more than 2500 stores in the US and Canada.

Sears’ senior manager of strategic sourcing, Donna Quintal, shared the podium with Cathey at the Sourcecon recruiting conference in the US in February to talk about the retailer’s data-based approach to talent strategy.

“The team there has been heavily leveraging big data and analytics,” Cathey says. “They have been quietly doing it for two years.”

The retailer’s commitment extends to electing to its board one of the architects of the Oakland Athletics’ moneyball strategy, Paul DePodesta.Announcing the appointment last Christmas, Sears chairman Edward Lampert said in a statement: “Mr. DePodesta’s ability to scrutinise data and use it to assess talent and drive execution makes him ideally suited to join our board.”

Sears job applicants are put through an interview process and their answers are compared with those given by the existing Sears workforce. All the human resources data throughout their career gets added to their Sears profile.

“They found their best employees did not come from their previous talent pool,” says Cathey.

UK recruitment industry consultant Bill Boorman says in a blog that the retailer has profiles on more than 400,000 employees.

“Every interview is a source of competitor information that goes into the system, hired or not,” he writes.

“Recruiters are trained to gather data in the interview (they jokingly compare this to being interviewed by the CIA). When I think about how much market information recruiters could collect to help influence sourcing and hiring decisions, the potential is frightening,” he says admiringly.

Get to the right candidates in the right way

When employers use information from their own talent and performance databases, the strategy is called talent analytics. When they scour enormous amounts of publicly available information from social media and other sources, it is “big data”.

Talent analytics tell them what their best workers look like, but big data can give them information on what games they play, what kind of music they like and how many friends they have.

Big data can also find people who fit the bill. This is important because the best people for a role are usually not the ones you see. They are probably already working somewhere else and are unlikely to see a job advertisement and are unlikely to apply. So you have to go out and get them.

Sources of data can include:

  • Curriculum vitaes.
  • Employee performance and retention data.
  • Demographic data.
  • Social media: LinkedIn, Facebook, Twitter, Google+, YouTube, Pinterest.
  • Mobile check-ins and updates.
  • Recommendations, awards and endorsements.
  • Blog posts and comments.
  • Press releases and announcements.

Using this information, employers can contact people directly, or craft advertisements to be posted where they are most likely to see them.

Boorman says Sears tracks social media activity to see what topics are trending. Among the people they want to hire, TV show Family Guy and rapper Eminem are “massive trends”. Sears then uses this information for its advertising campaigns.

Big data can help employers better assess their talent, out-recruit the opposition and look for talent in new places.

Quality not quantity

When sourcing people for recruitment, one of the major concerns is how to tell if they are any good. San Francisco-based IT company Gild looks at software sharing sites like GitHub and Google Code and watches how often a person’s code is accepted for open source projects, plus how often other developers borrow from their code.

Through the developers’ GitHub profiles, Gild can find the contact details of those people who are writing the highest-quality code.

“What makes the best software engineer shouldn’t be left to the instinct of hiring managers,” says Cathey.

“Some of the best coders don’t come from Google or Facebook. Some may not even be at work. They may be in high school. Imagine, you could start recruiting people before they even go to university.”

If football talent scouts start enlisting kids from the age of eight or nine, you could get companies assessing the potential of students at 13 or 14 years of age, he says. “I think, at some point in the future, we will start recruiting younger and younger.”

Glen Cathey is also the author of the popular Boolean Black Belt-Sourcing blog and is the 24th most connected person on LinkedIn.

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