RecruitingTrends LinkedIn Group @recruitingtrends on Twitter RecruitingTrends Facebook RSS

Thought Leadership

Taking the Guesswork Out of Recruiting

Personality assessment data and analytics can help you find job candidates who most closely meet your organization's needs.  

Tuesday, February 14, 2017
Write To The Editor Reprints

Whenever I bring up the subject of data analytics' role with managers, I'm commonly met with one of two responses: "There's no way we can afford a product suite" or "We already have best practices around our recruiting efforts."

Thing is, if you're consistently not finding the ideal candidates, or employees aren't lasting more than a few months on the job, then you obviously don't need an expert to tell you something is wrong. If nothing else, the status quo isn't working.

To demystify analytics, it's math -- and not the hardcore linear-algebra kind. It's using your common sense to understand data, something everyone can do with a little bit of training and the right tool. And the math adds up; at its core, analytics is simply quantifying your best practices. Also, it's not expensive, and it does not require a dedicated staff to administer.

On top of that, the HR field has been looking for precision in recruiting for a long time, and this is just the latest evolution. Before Big Data played a significant role, there were a host of separate processes at play: reference checking, personality assessments, skills assessments and interviewing, to name a few. Problem was, people were coming to their own conclusions about these components separately. On top of that, no one was aggregating the data to create a predictive model that looked at the different factors holistically.

With algorithms that take mere seconds to compute, analytics can examine a variety of data points and determine the candidates who most closely meet your desired characteristics, even spitting out percentage fit-scores to a specific role. As you become more familiar with the tool, you can build models that help you understand the makeup of an existing workgroup or pinpoint your high-potentials.

Additionally, analytics models are flexible and can be updated to respond to the demands of the changing business landscape. So, if your company suddenly needs its sales team to focus more on new customers and less on upselling, a model can be quickly adjusted to reflect the most relevant requirements.

Analytics also allows businesses to go beyond surface-level information or gut instinct to not only understand what makes a person tick, but how to improve their workplace output in various departments. Viewing a candidate's responses to her personality assessment through analytics lets you see her true potential and career motivations, as well as where she would be ideally positioned. With a few clicks, you can also plot a department's employees -- along X and Y axes -- and determine if you have the right balance between the four general team roles: champion, creator, facilitator and implementer. From experience, I can say that unbalanced workgroups tend to fall short of goals, but with analytics, you can see a visual representation of your existing teams so you can make informed recruiting decisions to ensure departments are well-rounded.

Also, analytics provides instant results, and when properly structured, you can get answers to complex business questions as quickly as you can formulate them.

Recently, I was working with a client that had an underperforming salesforce. They were bringing in people with great references, even past sales success, so what was going wrong?

While the client didn't particularly see analytics as a way to answer its question, I took it upon myself to look at the personality-assessment data of its salesforce. By examining individuals' unique constellation of consistent behaviors, I was able to determine that those who were performing well in the role had better-developed cognitive and big-picture thinking ability than those who weren't performing well.

The initial insights gained by looking at analytics were eye-opening; seeing the differences in personality-assessment data between the top performers and the underperformers led the business to question its assumptions about what works from a sales perspective in its industry.  All of a sudden, decision makers were asking questions like:

"Are we defining the job correctly?"

"Are we responding to the marketplace?"

"Do we understand the needs of our customers, and are we speaking to them in a language that will position us as subject-matter experts?"

Soon, the business was using the data and even asking what it indicated about the type of training individual sales team members need. Pre-Big Data, a company might have decided to throw generic sales training at the problem, thinking to itself: "We've got great people. Maybe they need to learn prospecting?"

But by using analytics, today, the company can target the root cause of the problem and develop an approach to specifically address the issue. For example, analytics can show you where a group has a common shared weakness (e.g., detail focus). This allows you to arrange for group training to help all of the team members at once. You can also see which team members have a unique challenge, and provide them with individualized coaching to supplement other learning. Now, the business is receiving greater return on its investment, and it has more targeted parameters around which it can recruit going forward.

And, with new hiring and development parameters in place, you'll notice that your recruiting efforts will become decidedly more precise. Honestly -- and it's tough for many of us to admit this -- businesses sometimes got lucky in the past. In other words, the right candidate just happened to stumble upon your company, find your ad in an online listing or have a good day when interviewing.

Businesses freely reaped the benefits of "happy accidents," and why wouldn't they? The problem is that luck eventually runs out, and you experience a regression toward the mean. Also, sometimes a good candidate is unlucky and doesn't make it into the fold.

Data-driven decision making is the exact opposite; it's deliberate and sustainable. You know your ideal candidate. From there, you can be precise about writing job descriptions, placing ads and knowing whom to target. After fleshing out the hiring process by combing through a candidate's references, you have all the data at your fingertips and can calculate who gives your business the best chance of success.

This way, if a candidate doesn't interview well, you can look at her personality-assessment data and see her potential. So, you chalk up her poor first impression to external factors and you bring her back to interview again. Slowly, you'll notice fewer and fewer instances of random chance, as you take control of the recruiting process instead of letting it guide you.

Dr. David Solot is senior vice president of client services at Caliper Corp.

Copyright 2018© LRP Publications