Recruiting Is a Science Not an Art — How Intuition Hurts Recruiting Results

The future of recruiting is scientific, data-driven, and businesslike. The roadblock to that transition is our current “art not science” approach where intuitive recruiters act like artists who want 100 percent freedom over how they work.

Now you might assume that everything in recruiting is going fine, but you would be wrong. A 2019 survey of CEOs ranked their No. 1 internal business challenge as attracting and retaining top talent. A similar survey revealed that 80 percent of CEOs worried about the availability of key skills. Part of their unhappiness may be because research indicates that 46 percent of all new hires fail within 18 months and the “Cost of Mis-Hire Study” revealed that the costs of a mis-hire are 24 times salary. Surveyed executives also revealed that 10 to 25 percent of new hires leave within the first six months.

The primary factor that prevents companies from shifting to the scientific approach is resistance from intuitive recruiters and HR professionals. The quickest way to instantly identify intuitive recruiters is by their arguments against the scientific approach, which inevitably include hundreds of words but no data.

In the “science versus art” continuum, it should be no surprise to recruiting leaders that historically corporate recruiting has been managed exclusively as if recruiting was an art. Evidence of this artistic or intuitive approach includes the fact that you don’t need a degree or certification in recruiting to become a recruiter. And unlike scientific professions, there are no universally accepted success metrics. Even though there are valid recruiting principles, they are not universally applied ones. Scientific professions have underlying theoretical frameworks; however, a search of Amazon won’t find any book entitled “The Theory of Recruiting.”

The current intuitive recruiting approach has led to several years of dramatic talent shortages and an almost universal failure to meet diversity goals. And because my research has revealed that shifting to the scientific approach can improve recruiting results by up to 50 percent, in my view now is the time to catch up to the leading firms by beginning to make the transition.

Many Fields Have Made the Transition From Art to Science

Historically, there were many fields that were previously treated as an art, including medicine, astronomy, weather, customer relations, marketing, and professional sports. But practitioners started using what is now known as “the scientific approach,” which relies on cause and effect and root-cause identification. In each of these fields, the intuitive approach became undefendable.

In the business world, the best recent example of becoming scientific is supply chain. Once it became data-driven, it transitioned from the overhead functions of inventory, transportation, and purchasing to a profit center. Unfortunately, most corporate recruiting is still much too similar to astrology and alchemy, because of our resistance to data-driven decision-making and hypothesis testing.

The Art Approach to Recruiting — Unfettered Freedom to Use Intuition

With the exceptions of Google, Amazon, Nestlé Purina, and Sodexo, corporate recruiting is managed as an art. In fact, the goal of Amazon’s HR is to “Become the most scientific HR organization in the world.”

The hallmark of recruiting as an art is an unrestricted freedom for a recruiter to use whatever approach they choose. Intuitive corporate recruiters operate in a minimal structure, low individual-accountability environment. Individual recruiters are free to come up with and keep secret their own ad-hoc mix of recruiting and assessment approaches. Recruiters are free to make life-changing recruiting decisions based on intuition, their experience, and vague criteria like fit, attitude, and engagement. Recruiting and recruiting leaders prefer this approach because their only real accountability is in the area of recruiting costs. This approach is defended by those that “hate math,” have never worked on the business side, and refuse to take accountability for recruiting results because they don’t own all aspects of the process.

Recruiting Science — Using Data to Identify “What Works”

In direct contrast, the relatively rare scientific approach is defined with the use of a businesslike and therefore quantified approach, where decisions are made primarily using data. As mentioned previously, this approach uses the scientific approach to identify causes and effects and to experiment in order to test hypotheses.

Perhaps the best primary indicator of the scientific approach in recruiting is the fact that the quality of hire is always measured. That metric is the benchmark standard for determining which tools and approaches have the highest success rate in predicting new-hire job performance. Even though it is required by law, only those addicted to a scientific approach have any statistical proof that their individual sourcing and screening approaches accurately predict success in this job at their firm.

Another example of a more precise scientific recruiting process is Google’s use of an interview-question generator. This “guardrail approach” only allows interviewers to ask validated and job-related interview questions. And finally, because it is a businesslike approach, the business impacts of recruiting are also tracked, quantified, and reported to executives.

The Many Benefits of Scientific Recruiting

When recruiting operates as a data-driven scientific approach, you get consistently superior results and business impacts that can’t be achieved when it’s managed like an intuitive art.

If you need more justification, here are some of the many benefits that you can expect when you adopt a scientific recruiting approach.

Increased productivity — the primary goal of recruiting is to increase team productivity through superior hiring. In order to attain that goal, recruiting must exclusively use the recruiting approaches and criteria that accurately predict on-the-job success. Obviously, you can’t correlate recruiting factors with on-the-job productivity unless you first measure the on-the-job productivity of new hires. Many call that quality of hire, but it is no more than measuring the performance level of new hires. The science of measuring quality (Six Sigma) has been established for decades. But the artists within recruiting continue to offer numerous excuses as to why it is too difficult to accomplish. Measuring quality hire is the essential factor that allows recruiting to continually hire more productive individuals.

Rapid learning and best-practice sharing — in science, typically results are shared openly among colleagues. So, a key feature of the scientific approach is the continuous sharing of both positive and negative recruiting results. This transparency and continuous sharing allow everyone to learn from the successes and failures of others. Under the art approach, best practices are kept by individuals and there is no systematic way to share them. This results in errors being repeated and unfortunately the best practices likely leave when a key recruiter departs.

Increased consistency — artists take pride in “winging it,” based on the premise that creativity leads to better results. However, there is no evidence that creative approaches are more effective in recruiting, and clearly, inconsistency leads to potential legal problems. Under the scientific approach, once both best and weak practices have been identified, individual recruiters are expected to use only the standard proven approaches. With everyone consistently using only the most effective approaches, recruiting results improve and errors decrease. Under the artistic approach, intuitive recruiters can continually use a variety of approaches, many of which have not been proven to be effective. And with no centralized reporting, it’s impossible for leaders to know which approaches are being used.

More recruiter accountability — gathering and reporting data makes accountability much easier. Measuring the outputs of individual recruiters against a standard allows you to quickly identify problems. With individual accountability, it is possible to recognize and reward great recruiting results and to fix weak recruiters and hiring managers. The lack of data under the intuitive approach allows recruiters to use personal relationships with managers, rather than results, to support their job security.

Failure analysis — when success is clearly defined and measured, it is much easier to spot major errors. A systematic process for spotting errors allows you to routinely conduct failure analysis in order to find the root causes to a problem and prevent future occurrences. In most corporate recruiting functions where intuition dominates, there is simply no formal system for requiring failure analysis, so little learning occurs after a major mistake.

Speed — in today’s highly competitive job market, hiring fast is essential if you expect to land in-demand candidates with multiple offers. Unfortunately, without data, it’s hard to identify the slowest aspects of recruiting. In addition, under the intuitive approach, many recruiters argue that slow hiring produces superior results, even though there is data to the contrary during low unemployment.

You can actually know things — under the intuitive approach, you have little data. That means when you’re asked by an executive about the results of a particular approach, your only answers can be “I believe” or “I think.” In direct contrast, under the scientific approach, you can answer questions with some confidence, saying “I know” or “the data proves.”

Data makes the use of AI and technology possible — machine learning is a little more than an extremely fast continuous improvement tool. The key foundation of AI and machine learning is the use of large quantities of data. This data allows the algorithm to continually identify the obvious and less obvious factors that contribute to recruiting successes and failures. However, little data is gathered or reported under the intuitive approach. The use of machine learning might improve recruiting results by as much as 50 percent. The lack of data and the failure to measure quality of hire under the intuitive approach literally makes the addition of most technology and the use of machine learning impossible.

Why Intuitive Recruiters and Artists Resist Change

I find that intuitive recruiters are the ones most resistant to change. Part of it is job security, but most of it is because stopping or delaying change allows individuals to keep doing what they’re doing, unchanged by facts or shifts in the talent marketplace. Part of the resistance comes from wanting to hold onto the past. Intuitive recruiters insist that they’ve always done it this way. However, they probably have always done it that way because there was no accountability in the form of hard data showing them that their approach might no longer work. In fact, you’ll likely find that individual intuitive recruiters have never written down their methods and their approach. That makes it extremely hard for others to learn from any of their intuitive approaches that might work.

If you want to identify the “problem recruiters” who you need to fix or get rid of, simply look for naysayers when it comes to quality of hire, machine learning, adding recruiting technology, or resistance to reporting their recruiting steps and their individual results. I have found that less than half of intuitive recruiters can successfully adapt to the scientific or technology-driven approaches, so in many cases, they will have to be replaced.

Final Thoughts

At least to me, it seems a bit silly that there is even time for an argument about whether recruiting must become data-driven and scientific.Almost every other major business function (other than HR) is already shifted to the data-driven model. That shift occurs not because the function wants to change (they seldom do), but because the need to dramatically improve business results demands that the function change. Look at how supply chain, customer service (CRM), and marketing have shifted to a data-driven and hypothesis testing scientific approach. Their approach has changed dramatically. Their status, reputation, and their results have all significantly improved as a result of that transition. Imagine a time when, like supply chain, recruiting could be considered a profit center.