Using Data-Driven Decisions in your Hiring Process


What Does Data-Driven Mean?


Everyone wants to be “data-driven” these days. If you’re not data-driven, you must be getting left behind. But what is data-driven anyway? Data-driven means using data analysis to make strategic decisions instead of intuition or personal experience.

Well, when you put it that way, many of us in the world of recruiting are behind. We instinctively know which candidates will become great employees, don’t we?

After all, we have years of experience, and nothing can replace good old-fashioned experience. But the reality is that we could do a whole lot more with data in the hiring process.

 In the world of high-volume hiring, employers get a lot of candidates for every hire. Some companies that we work with get 200 candidates for every hire. But, for the sake of this discussion, let’s say we get 50 candidates for every hire. How do we choose the best?


Is Your Current Hiring Process Data-Driven?


OK, we know how to do this. We have a funnel. Candidates answer so-called knock-out questions. Can you legally work in the country? Are you willing to work specific shifts? And so forth. Often these knock-out questions rule out a number of candidates and might get our stack from 50 down to 40 or so. Let’s call this Phase 1.

Phase 2 might be something like a personality or situational judgment test. These can be rather lengthy, and some candidates will drop out of the race at this point. They don’t want to take the time, or they’re just opposed to the concept. Some will complete the test and get a passing score. Phase 2 might take our funnel down to 15 candidates.

Now it’s time for Phase 3. Let’s schedule interviews with these 15 candidates. You already know what’s going to happen. It’s impossible to schedule all 15. Some have already taken other jobs. Some are no longer interested, and some are scheduled for an interview but never show.

You interview four or five candidates. Your intuition and personal experience tell you or your hiring manager that candidate #3 isn’t perfect, but they’re better than the others. Thankfully, candidate #3 passes the drug test and background check. The offer is made and accepted.

A good job was done all around, and we can’t wait to see candidate #3 become employee of the month. Except that candidate #3 quits in 30 days. What happened? Who is to blame?

We may think, well, we probably didn’t get enough qualified candidates, so let’s get to work on that problem. We decide to increase our budget for Indeed. 

There you go. That’s our data-driven hiring decision process? Unfortunately, it’s not even close. Let’s see what would happen with a real data-driven hiring decision process.


A Real Data-Driven Hiring Process Looks Like This

Step 1 – We develop an employee data model. What are the traits and characteristics of our best employees? It could be a bunch of different variables.

How far from the job do they live? How much and what type of experience did they have when we hired them? How many jobs did they have before their current position with us? The employee data model will likely have 30 or so variables. Some data elements will be more critical than others.


Step 2 – We evaluate new candidates against the employee data model. However, be advised that this effort is not humanly possible. The number of combinations and permutations for a multitude of variables is beyond our comprehension. All the intuition and personal experience in the world cannot compete with computer processing in this arena.

Fortunately, machine learning algorithms can be developed to determine which variables and combinations of variables are most important in predicting which candidates will become quality hires.

Step 3 – Use those machine learning algorithms to evaluate every candidate that applies for your job. The job of the machine learning algorithms is to tell you which of your candidates (it doesn’t matter how many you have) are the best candidates for the job. You can immediately identify your top candidates and focus your attention on them – before they get away!


You Can Measure and Improve Quality of Hire


You can still ask the knock-out questions and require the assessments if you like. However, you can now evaluate whether these steps make any difference at all in your quality of hire.

When I apply for a job at your company, and you ask me which shift I’m willing to work, I’m going to say all of them. Will you work weekends? Of course! Not really, but I’ll deal with that later after you see how invaluable I am. Unfortunately, sometimes these answers are not as reliable as we think.

Some of the knock-out questions may make absolutely no difference as to whether the candidate will become a quality of hire. That assessment or situational judgment test you rely on so much might be insignificant as to predicting the tenure of a candidate as an employee.

The point is you’ll never know whether each step in the recruiting process is adding value unless you embrace a data-driven hiring process. Do intuition and personal experience have a place in hiring? Absolutely!  But I would put some of my money on data analytics!


In Conclusion  


Using data-driven decisions can help your managers hire the best candidates, increase diversity, improve customer experience and business outcomes. Make your hiring process data-driven.

Curious to learn more about data-driven hiring decisions? You'll want to check out these additional resources:

If you're serious about making data-driven hiring decisions, you will want to watch these short videos to see short demonstrations on Cadient's new analytics dashboards:

Cadient Diversity Hiring Dashboard. This dashboard allows you to easily see and report your diversity hiring efforts and identify where improvements are needed.

Cadient Decision Point Dashboard. This dashboard shows you the data you need to hire the workforce you need. 



Jim Buchanan