Who do you hire?
It sounds like the most fundamental of tenets for an organization. But as you know, sometimes the simplest questions can be the hardest to answer.
Often, it’s because there isn’t a clear vision that guides hiring decisions. There’s little formal understanding of who should be hired.
For many organizations, particularly those with high-volume hiring needs, the focus is on the frontline threat of understaffing. Drive more candidates to apply, and do so faster, the thinking goes, and you’ll always stay a few steps ahead. With this approach, job offers are heavily weighted on whose applications are seen the earliest. There’s no automated way to prioritize applicants beyond that time stamp.
The problem here isn’t hard to identify: When a person applies is actually a meaningless factor as it pertains to predicting job performance.
In the fight against understaffing, you might say that the bucket still has a hole and is leaking water. The core turnover problem remains and the need to refill the bucket never goes away. It’s a fundamentally inefficient system: Engage a passive talent pool via marketing outreach, convince a subset of this pool to begin the application process, then immediately start to lose numbers and go back to attracting more would-be applicants.
When that’s the reality, candidates are lost with little or no insight into how they would have performed on the job. Some gems are undoubtedly squandered. There has to be a better way, right?
Imagine if you could instead get a measure of a candidate’s fit for the role early in the process. That metric could then be elevated within a candidate’s application, acting as a predictive signal, with resources prioritized accordingly.
Where pre-hire assessments come in.
There are two ways an assessment can help capture the data you need to start predicting employee success more accurately, and ultimately, reducing turnover. The first is likely the most obvious to you. When you include one or more assessments in the application process, you collect data about a candidate. Whether you’re learning about that individual’s personality or their thinking and learning ability, you know more about that job seeker than you did before.
The second way?
Adding a pre-hire assessment helps collect data about your own jobs. During the implementations phase, a science-backed assessments program will involve gathering information about each of your roles. The process will go to the source: The people who actually manage or hold each job, who therefore live and witness those roles from day to day. What is a real day on the job like these days? Has that changed over time? What are the characteristics of the people who succeed in this role? How are they different from those who struggle?
The primary objective driving this process is to establish how a pre-hire assessment should be scored. As this is explored, you’re answering the question of who fits well in the job. In other words, who are you looking for? With an assessments program, you take a major leap toward having a data-based answer to that question. You can then set hiring guidance, based on the real-world evidence of who succeeds in the role.
The data-gathering — and the benefits — don’t stop there. There are more advantages to adding assessment data to your hiring process. Let’s take a look at three examples.
Assessment data helps you think about the impact of recruiter-to-recruiter biases.
When you bring data to bear on hiring decisions, you have the potential to enhance fairness by relying less on the individual impressions and biases that can differ measurably from one hiring manager to another — and even from one manager’s Monday to their Thursday. Assessment data brings uniformity to hiring and maximizes the value of recruiter time. We all have biases to some extent. Using assessment data to guide the consideration of a candidate, including during an interview, helps hiring personnel stay consistent and on track — resulting in higher assessment completion rates, reduced recruiter administrative work, and 21% reduction of short-term turnover (the kind where businesses lose the most money).
Assessment scores help you distinguish similarly qualified candidates.
For many roles, the available factors to consider in decision making, like education level, work history, and availability, don’t differ much from one candidate to the next. Moreover, biases can again enter in. The hiring team may overweight college coursework or devalue certain types of work history, like full-time parenting. In fact, research has shown that work history might not matter as much as we think. Having early insight into the candidate’s fit for the role limits those biases while giving good-fit candidates a longer look that helps them stand out from the crowd.
One hospitality organization implemented a Traitify assessment into their application process using "ideal candidate profiles" to find new hires. The scientifically validated super-fast assessment led to a huge boost in manager quality that corresponded to revenue increases averaging more than $90K per location.
Assessments bring attention to the “silver medalists” candidates.
The question you should ask after you extend a job offer is, “Who else should we keep in touch with?” With a quantitative match score added to a candidate’s record, you have the ability to prioritize candidates with an eye for their fit. That enables you to play a longer game, investing resources a quarter or more ahead. You preserve value from your so-called “silver medalists,” or your next-priority tier. If you’ve fulfilled a requisition for the month, why let the remaining candidates drift away? You could instead have insight into your silver medalist class, based on assessment scores, and selectively nurture a relationship with a promising pool of candidates.
The use of data across business functions continues to expand. Now that science-backed assessments are available that gather information in a fraction of the time (with an average of just 2 minutes to complete) once required, there’s a tremendous opportunity to include job-fit data in the hiring process.
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