We recently built a model of top-performing sales employees for a client company. The results surprised both the client and us. Why? Because their top performers had trouble paying attention to repetitive activities, tended to have wandering minds, and were somewhat impulsive.
Now think about this for a minute. Who would admit, in a job interview, that they don’t pay attention, tend to daydream, and are somewhat impulsive? No one. But what if those traits actually predispose you to be better at sales? At least in the case of this client, this was true. Conversely, other roles at this company revealed very different profiles; for example, technical roles required a high level of attention to detail. The takeaway is that different roles have very different success profiles.
pymetrics firmly believes that there are many pathways to career success, and our career assessment and hiring algorithms reflect this. The real challenge is finding your personal fit.
Many career assessments take this ‘non-directional’ approach, meaning they provide feedback on a person’s unique traits and how these traits map onto careers. For example, let’s take the Myers-Briggs. I’m an ENFP, with great people skills, and therefore my top career fits are social scientist, teacher, detective, politician, and journalist. My cofounder is an ISTP, making her a practical problem solver, and her top career fits are firefighter, policeman, mechanic or engineer. Career assessment tools are all about finding your fit.
However, it is a different story when companies use algorithms for hiring. These algorithms (called “pre-hire assessments” or “predictive hiring technology”) are often used as part of the application process for a job. Many times, they take a directional approach, proposing to assess whether someone is generically employable overall for any role in any company. It’s like the SATs but for job admissions: there is only one “employability” formula, and a higher score is always better.
This leads to a disconnect between the non-directional career assessments and directional pre-hire assessments. It is unfortunate for two main reasons.
First, it’s sending mixed messages to career seekers. On the one hand, career counselors universally insist that everyone has a place in the working world. On the other hand, pre-hire assessments determine that some people are more generically “employable” than others.
Second, and more importantly, the notion that some people are universally more employable than others is simply wrong. Typical tests ranking people directionally are not predictive of job success. Google famously showed that standardized test scores and GPAs, two well-known directional ranking systems, are essentially useless in predicting career success.
Therefore, what we need is an approach to predictive hiring that is more similar to career assessment. While measuring overall employability is ineffective, measuring your fit for specific roles or companies can be very powerful. Let’s return to our model of high-performing sales people: being inattentive doesn’t make you unemployable. In fact, it makes you a great fit for sales. However, it may not make you a great fit for other roles, like accounting. A hiring tool, not just a career assessment tool, needs to make that distinction.
This idea of different strengths mapping to different roles is exciting because it means all people have the potential to be superstars, given a good match between their inherent strengths and their job environment. It also means that all people have roles they are not suited for. My pymetrics profile says I’m a great fit for entrepreneurship (yay) but a terrible fit for consulting (amen to that).
It is not just candidates who are short-changed by the notion of overall “employability;” it’s employers also. Employers struggle to find the right candidates and they lament huge talent shortages. The modern world needs people with a wide variety of talents, and employers should be trying to match people’s talents to roles rather than chasing after the universally “highly employable” candidate.
This becomes very salient when considering that people labeled with disability can make outstanding employees in particular occupations. For example, autistic individuals have a strong predisposition for making great quality assurance engineers and people with dyslexia are much more likely than non-dyslexics to be entrepreneurs.
So, let’s set aside the notion of overall employability and work towards a hiring world where everyone is recognized for their unique strengths and directed towards the opportunities where they will best shine. It’s a winning proposition for job seekers and companies alike!