The more data you have at your disposal, the better informed your decision will be.
That’s what the age of big data promises, anyway. Thanks to data, companies are able to target their customers more effectively. Data helps content providers and publishers advertise related content that individual users are most inclined to like. We all know what data means for e-commerce: odds are you’ve bought at least one of the items Amazon recommended to you as you perused the site.
Indeed, data can be used to influence all of our decisions. In the work sphere, data can be used to decide which markets to target next or which products to develop. Believe it or not, it can also be used to streamline the interview process — leaving companies much more likely to hire the best person for the job.
And that’s nothing to take lightly. After all, organizations are only as strong as the employees that power them. The wrong hiring decisions can lead to decreased morale and high turnover, which can both make a serious dent in the bottom line.
The last thing any company wants to do is have to keep on hiring new employees because the wrong hiring decisions are made continually. Beyond the costs, it takes as long as months before new hires become fully productive. You can’t expect your rookies to provide superior customer service or contribute as much as your seasoned vets do right away.
The good news is that by implementing a data-driven interview process, organizations are much likelier to make the right hiring decisions the first time around. Thanks to data, companies can benefit from hiring talented employees who positively contribute to their culture and have no immediate plans of looking for another job.
Traditionally, hiring managers would look at a pool of résumés, narrow it down to a reasonable number of candidates, conduct phone interviews, and then decide whom to invite in for a face-to-face interview. There might be a few rounds of interviews, with only the best candidates moving forward. More often than not, a “gut decision” would ultimately be made, and the candidate who was perceived to be the best would be extended a job offer.
While that approach can certainly result in the right people getting hired most of the time, it doesn’t result in the best candidates being hired every time.
More than a decade ago, Mark Roberge was asked by one of the founders of HubSpot whether he could build a successful sales team for the then start-up. Despite the fact he had never done that before, Roberge said yes; he’d gotten the company its first 50 customers, anyway. Eager to get started and aware that if he didn’t move fast, someone else could beat HubSpot in the inbound marketing space, Roberge knew he had to scale rapidly.
If every hiring choice is a gut decision, invariably some candidates won’t work out. Roberge couldn’t afford to have anyone jump ship. So, tapping into his MIT engineering background, he created the metrics he believed were requisites for succeeding as a member of the HubSpot sales team.
Creating those metrics involved three phases:
01. Building the sales model
Roberge meticulously analyzed how he did his job and built a model he could compare each candidate against. Would they be able to give as many presentations? Were they comfortable with talking to as many prospects each day as he did? Could they close as many deals?
02. Hiring character traits
Next, Roberge figured out which specific character traits a successful member of the HubSpot sales team would have. Great candidates would be curious. They’d have a work ethic that’s second to none.
Roberge ultimately settled on 10 specific traits and graded each candidate on a scale of 1 to 10 on each of them. After some hiring decisions were made, he went back to his scale to see which traits were the biggest indicators of success in the position. He then refined his metrics even further. Ultimately, this approach enabled him to create a system that could accurately predict each candidate’s odds of success.
03. Choosing the right leaders
As the team continued to grow, Roberge knew that he needed skilled leaders to develop it even further. No, he didn’t just promote the folks who had the best sales stats. Using the same data-driven approach described above, Roberge settled on promoting leaders who scored high among many of the criteria he used to hire team members in the first place. That way, they would be able to help team members develop all of the skills Roberge identified as being necessary for success at HubSpot — not just a handful of them.
It turns out Roberge’s system worked pretty well. In 2013, the sales team was staffed by 450 people who generated $90 million.
More than a decade ago, Google sought to attract top talent by placing billboards with complicated math problems in areas where engineers were likely to be (e.g., Harvard Square and Silicon Valley). If you could solve the math problem, you’d arrive at a URL that would have another difficult puzzle. Get that right, and Google would ask you for your résumé.
Sounds cool, right?
After analyzing the people they hired this way and seeing there wasn’t any correlation between solving the complex puzzles and success at Google, the company decided to nix the program altogether.
By using a data-driven interview process, Google has also found out that:
If HubSpot and Google can use data to make better hiring decisions, there’s no reason your organization needs to continue to rely on gut instincts. Let these successful companies’ stories inspire you to create your own data-driven interview metrics — putting you in a much better position to hire the right people the first time around.