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The secret to optimizing candidate screening

One of life’s most consistent, persistent, and common lessons is a variation of ‘practice makes perfect.’

From the time we are children getting frustrated over coloring outside the lines, or not understanding the lesson, or stumbling over a new word, we are told “practice.” We must try, learn from the mistakes we make, and then try again – over and over until we get it right. From endless hours of math homework to reading book after book to years of music lessons, we do, learn from our mistakes, and do again. This is how we become experts.

Practice makes perfect(ish).

Businesses work the same way. The most effective businesses aren’t focused on getting it right the first time as that is an unrealistic expectation. They are experts at ‘practice makes perfect’ – doing, learning, doing again, building up expertise.

And that is the secret to optimizing candidate screening (or any other process) – doing, learning, applying, doing again.

There are three key elements to effectively apply learnings:

Three key elements of applied learning:

1. Data. Data is required to learn. How will you know what is working and what is not without the data?

2. Analyze. Analyzing the data is required to understand the lesson being taught. How will you know what the data is showing you without taking the time to interpret and understand it?

3. Change. Change is the application of the lessons learned through analyzing the data. Without change, what value will the lessons have?

Collect the data, analysis it, apply changes based on lessons learned – great. But…

“I don’t have time for this in talent acquisition, we have so many recs open”

“We are not big into change around here.”

“We don’t have budget for change.”

Excuses like these are prevalent and detrimental. They are also false. Change through applied learning is not just doable (with techniques like small steps and centers of excellence), it is also necessary for solving challenges, improving processes, and succeeding in initiatives such as creating more inclusive, equitable hiring programs.

Here are a couple of examples of how to use applied learnings (or the change cycle) in candidate screening.

Techniques to optimize candidate screening with applied learning

1. Job Requirements

The first step to most candidate screening processes are a form of basic requirements. Candidates that meet the basic requirements move forward. Candidates who don’t are filtered out. But is it working? Is it working well?

Step 1: Data:

For a job (or set of similar jobs), collect the following data to the best of your ability:

  • Number of candidates that applied

  • Number of candidates that meet the minimum requirements

  • Why candidates did not meet the minimum requirements (i.e. which requirements)

  • Number of candidates who met the minimum requirements that did not qualify through the subsequent stage (usually an assessment or screening interview stage)

  • Why candidates did not pass the subsequent stage (usually an assessment or screening interview stage)

  • Demographics of candidates applied and of candidates that met minimum requirements

For example, this is what some of the data looks like in

Number of candidates who applied and those who qualified on requirements:

Why candidates disqualified in the requirements stage:

Demographics of candidates who applied and qualified through requirements (portion of full report)

Step 2: Analyze

Once you have the data, look for patterns. Is there opportunity for improvements such as saving time, engaging a wider and more diverse range of candidates, or improving results?

For example:

Patterns in the candidates that make it through the requirements but do not pass the next stage of the process.

If there are patterns, it could indicate that some of the must-have requirements are missing. For example, if candidates are not passing the next step of screening due to a lack of particular type of knowledge, adding that knowledge to the must-have requirements will prevent candidates from progressing and avoid wasting their time and yours. This is especially useful if there is a challenge with too many candidates to screen.

One or more requirements are disqualifying the bulk of the disqualified candidates.

If there are a few requirements doing most of the disqualification, they could be unnecessarily limiting the candidate pool. Question if they are truly ‘must-have’ or if the hiring manager will consider hiring candidates who don’t meet one or more of those requirement. This is especially useful if there are challenges with not enough candidates to screen.

One or more demographics disappearing between apply and those that minimally qualify by meeting the must-have requirements such as 50% of the candidates that apply are women but only 20% of those who minimally qualify are women.

Drops in demographics from apply to minimally qualify could be due to one or more requirements that favor one demographic over another. For example, requirements around physical activity, restrictive work schedules and intensive travel tend to favor men over women. Question if these demographically leaning requirements are truly must-have or if they can be removed or modified to bring more neutrality. This is especially useful if there are diversity challenges.

Step 3: Change

Apply the findings to the requirements. If requirements need to be removed, added, or modified, do it.

Don’t just modify and move on. Watch the results to verify they had the intended effect. If they don’t, or if they don’t have enough of an effect, do the process again – data, analyze, change.

2. Initial screening interview

One of the most common complaints we here about the screening process is that hiring managers reject most of the candidates presented to them. Recruiters spend hour after hour on calls screening candidates only to be told none of the ones they presented meet the expectations and they are left to start over.

This is the perfect scenario to use applied learning. Are the interview questions truly evaluating what the hiring manager/team needs in a candidate?

Step 1: Data: