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 career.place:
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:
For a job (or set of similar jobs), collect the following data to the best of your ability:
Number of candidates interviewed in initial screening interview
Number of candidates that passed initial screening interview
Why candidates did not pass screening interview
Number of candidates rejected by hiring team without progressing to next steps
Why candidates were rejected without subsequent steps
Why candidates were rejected with subsequent steps
Demographics of candidates applied and candidates that met minimum requirements
Step 2: Analyze
Once you have the data, look for patterns. Is there opportunity for improvements such as to increase the number of candidates accepted by hiring team, to save time by increasing the likelihood that the candidates that make it to the next step will pass, or to increase the range of diversity within the candidate pool?
For example:
Patterns in the candidates that make it through the screening interview but are being rejected either prior to or within the next step.
If there are patterns, it could be a sign that either the requirements aren’t complete, or the screening questions aren’t effectively evaluating certain criteria. It could also be an indicator that the notes are not complete (i.e. even though the candidate answered correctly, there isn’t enough evidence to show it to the hiring team). Address this by modifying the questions or what the interviewer is listening for in the answers. Or, if the problem is evidence, consider switching to a written or recorded screening.
One or more demographics disappearing between stages such as 39% of the candidates that are minimally qualified are over the age of 40 but only 15% of those who pass screening are over the age of 40.
Drops in demographics could be due to either questions that favor one demographic over another or biases in how the answers are interpreted. For example, a question around ‘energy level’ and ability to learn quickly often gives the signal to candidates that the interviewer is looking for younger candidates. This could affect the confidence and engagement of those who don’t identify as being part of that ‘younger’ group. On the flip side, Interviewer biases could interpret methodology-based answers (i.e. step 1 do this, step 2 do that) from older candidates as being ‘set in their ways’ and assume confidence as a signal that the candidate will get bored quickly and want a promotion. Remove bias-triggering language in questions to avoid the former, and, for the latter, consider bias-reducing techniques such as anonymity, recorded responses, and panel reviews.
Step 3: Change
Apply the findings to the screening interview – changing the questions, the method of collecting results, or the process itself.
Like with requirements, don’t just modify and move on. Watch the results to verify they had the intended effect, and keep in mind that applied learning is a process – one that works best when applied continuously – data, analyze, change.
Bringing it all together
When it comes to candidate screening, or any other activity, it’s worth embracing one of life’s lesson: ‘practice makes perfect.’
Rather than expect perfection from the beginning, build in every opportunity to use applied learning – data, analyze, change. By applying continuous learning, you will drive continuous improvement in candidate screening.
You can do it, we can help.
Career.Place’s structured and anonymous screening process provides data at every step that is easily viewed and analyzed. See a place to improve? With career.place, you can change the screening process while maintaining the highest standards of equity. You can drive continuous improvement, and career.place is here to help.
Comments