If your hiring process is feeling stale or you aren’t sure why new candidates aren’t working out, try treating your next hire like a research project. It will give you a fresh perspective on what you should be looking for, and where your current evaluation process may be falling short.
Every good research project starts with a question. In this case, ask yourself: What knowledge, skills, abilities and other characteristics (KSAO) do successful performers have in this role? It may seem obvious, but often hiring managers skip over this step and go straight to the candidate search.
When recruiters dig into this question they may find that no-one really knows the answer; or worse, that the skills and abilities everyone thought were appropriate for this role are off-base, causing them to hire the wrong people and cut the right ones from the candidate pool.
To answer the question, you need to conduct a job analysis, which is a study of the role – not the individual people filling the role. This is a key distinction because it helps remove bias and allows recruiters to identify the best combination of KSAOs for the job, rather than merely looking for a copy of the last person to hold the position.
A job analysis involves determining which activities the role is responsible for, the qualifications necessary to thrive in the role, and its relative importance to other jobs or employees.
Once you know what the job involves, you can research your hypothesis: if someone has the required combination of KSAOs, then they will be successful in the job.
The next step is to collect the data to test your hypothesis. Look at current and past people in the role, review assessment data, interview them about what the most important aspects of the job are, read their past performance reviews, gauge their productivity on key tasks, talk to their supervisions and peers, and revisit their resumes. The goal in this step is to validate that the KSAOs you defined in your analysis actually align with job performance – and to identify where there may be gaps.
As you gather and assess this data, ask yourself these questions:
- What do high and low performers have in common?
- What skills and attributes do managers and peers most value in this subject?
- What skills and attributes do they often lack?
- What KSAOs are required to succeed in the most important aspects of the job?
- What makes outliers (good or bad) different from the norm?
- How do specific skills and attributes align with overall job performance?
When you analyze the data, it is important to compare the job assessment results, which are scientific measures of a candidate’s knowledge skills and abilities, with the more qualitative performance ratings, peer reviews and resume data. This will give you a clear picture of what traits truly add the most value, which ones are neutral, and which attributes may be getting more attention than they deserve. For example, managers might think problem-solving skills are the most important feature for a candidate, whereas the job assessments will show that candidates who are assertive and responsive are the most successful.
The insights gathered from this analysis will help you assess your hypothesis about what makes the best candidate profile. As you study the data, look for correlations between individual traits and business results. The more the data from each category correlate to the others, the more on target your hypothesis about the ideal candidate profile will be.
This process can be a little time consuming, which is tough to justify when you are in a hurry to fill an empty role but it’s worth the effort. When you take the time to identify the KSAOs that make a candidate special it can transform your hiring process, and ensure every hire will be a good fit for the job.
Though don’t assume this is a one-and-done activity. While job analyses and the accompanying validation that comes from it can inform hiring for a long time, jobs aren’t static and neither are the skills and attributes required to do them well. The job and the context of a job is always changing, so take the time to periodically revisit your assumptions so you know when it is time to head back to the lab.
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