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Predictive Analytics: Finding the Perfect (Employee) Match

How effective are your recruiters in finding the right candidates for your open job requirements? Are they overlooking resumes of candidates who might be that perfect match?

Combining Natural Language Processing (NLP) with predictive analytics can help you zero in on the perfect employee match for any job. The trick is to combine thoughtful text analytics with machine learning, a process we’ve refined. Our software is able to identify the most meaningful screening factors particular to your company’s measures of success and find the candidates who measure up.

Don’t Through Away All Those Old Resumes!

If you regularly read our blog, you know that we work backwards. Understanding what you need now is often based on what worked for your company in the past (history repeats itself…).

When ZeroedIn “trains” our software to find your “Perfect Match,” success may depend on your HR resume database. We want to compare job descriptions not only against current applicants for the job, but also against old applicant resumes of now-successful employees.

The perfect resume database shows us what the hiring success funnel looks like at your company:

  1. Applied
  2. Selected for screening
  3. Interviewed
  4. Given an offer
  5. Hired
  6. Still here/top performers

Feeding the Predictive Analytics Model So It Grows Smarter

On the machine learning side of the Perfect Match equation, we feed our algorithm the key words that identify characteristics of successful employees at your company. We might start with the resumes of people who were hired and didn’t work out. Then we look at resumes of those who were interviewed but not hired, then those who were screened and not interviewed. It’s a process of refinement that helps the algorithm, in math-speak, identify a probability threshold for the perfect match.

At each step of the process, we look at the predicted value of a Perfect Match. That way, we can see how well the model is learning and make adjustments. For example, if the model says a resume is a perfect match and we know that person was hired but didn’t work out, we’ll tweak the algorithm. Then we can give the model some new information to digest so that it gets smarter and more accurate.

How The Perfect Match Model Helps Save Time and Resources

Once we’ve developed the model for your company’s Perfect Match, we’ll provide you with a list of the key resume factors that most often zero in on the best match. These factors can translate to the top two or three things HR asks when pre-screening a job applicant, reducing the number of people you need to meet. Knowing key predictors of success helps you increase the probability of finding a perfect match right from the start of the applicant process, saving valuable time and resources.

We Didn’t Invent The Perfect Match But We Make It More Perfect For You

ZeroedIn didn’t invent the concept or approach to the employee Perfect Match – there is plenty of academic research published on the subject. What we did, however, is structure a model that can be trained to meet your company’s very specific needs. If your HR department is struggling to fill open jobs with the perfect employees, perhaps we’re the perfect match for you!

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