Predictive Decision Making: The Future of Recruitment (Rank Princess – SEO)

We analyze trends in the blue collar job market using sophisticated mathematical techniques. This analysis can help recruiters identify the best candidates for the job. Additionally, it helps the job seekers find more jobs to their liking.

Presently, the context of the blue collar job market is rather vague regarding hiring practices. We have been trying to change that since 2013 and have taken another significant step in that direction.

Predictive decision making is something all companies, especially blue collar ones, can use to help recruit more reliable, productive, and loyal employees.

Why should you use predictive decision making?

Well, quite simply, because of its accuracy and succinctness.

By judging the job applicants by their past records, and any developing trends, predictive modelling helps you to choose between applicants.

Moreover, this method gives you a bird’s eye view of all the applicants’ position. Until now, candidates have been assessed rather subjectively. But by using this method, candidates can be assigned ranks and selection will become a much easier process.

What is predictive decision making?

Predictive decision making is a method of evaluating job seekers by observing trends of their past behaviour. It is a more succinct and accurate way of judging a candidate’s history. Until now, this has been done by personal judgment which can be subjective.

For example, parameters can include the candidates’ performance at earlier jobs, an improvement over time, punctuality, etc. We use several parameters in the assessment to develop more accurate predictions.

It sounds a bit experimental. Who else is using it?

Although predictive decision making might’ve seen most of its development in recent years, it has been quickly adopted by a vast variety of companies.

This is the same method being used by the best of all the big industries. To give an indication of the popularity of this method, here are a few industries where predictive decision making is being actively employed:

  • Financial Services
  • Actuarial Sciences
  • Healthcare
  • Travel
  • Retail
  • Pharmaceuticals
  • Telecommunications

And more! To us, it only seemed natural that the blue collar job market did not miss out in extracting the many benefits of this method.

Is everyone else also using it for job recruitment?

Not really. Surprisingly, the application of this method has been limited to the work that organisations and companies do themselves. That is, they use predictive analytics to help their customers, give advice to their clients, etc.

We wish to extend the sphere of applicability of this method to job hiring methods because it is at least as important as the work that companies do.

How are these predictions made?

The predictive modelling techniques used range from the crude to the sophisticated, and are listed as follows:

  • Time series: This is a weighted average of the candidate’s performance, with greater importance being given to recent work as compared to older work.
  • Machine learning: This involves software learning of the patterns and abilities of the job applicants as they develop over time. Very sophisticated predictive analytics algorithms are used.
  • Predictive modelling: A mathematical model is developed to predict the job recruitment rate and so on.
  • Data mining: This is the simple analysis of all the data available using a brute processing force.

Thus, you can see the level of sophistication predictive analytics techniques offer, and how it can help your company in improving recruitment practices.

Why do recruitment practices matter?

We appreciate the importance of hiring the best candidates who will prove valuable to the company in the long term and contribute to a better workspace. This method of predictive modelling is in line with our abiding commitment to improving the work environment in the blue collar sector.

LSI Keywords: predictive analytics, predictive modeling, predictive modeling techniques, predictive analytics techniques, predictive analytics algorithms.

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