Our main USP is the fact that we've developed a Candidate Matching Algorithm. This self-learning algorithm makes it possible for us to predict a candidate's job success right after this candidate has applied to your job. 

This prediction is based on two types of information:

  • Input from a company;
  • Input from us. 

Input from a company

Let's start with the input we're going to ask you. We use this input to create a benchmark of your organization and perspective on candidate success. This sounds more complicated than it actually is for you. Let's give an example.

Step 1. Create a new vacancy.

You can do that by either clicking on 'New vacancy' or by importing an existing vacancy from your integrated ATS (click on 'Import').

Step 2. Candidate matching categories.

The tab 'info' contains general information about your vacancy (job description, department, etc.). This information will be imported from your ATS. The next step is a more important one: 'Matching'. In this tab we're going to ask you to divide 100% under four candidate categories. By doing this, we learn more about your company's priorities/preferences when it comes to evaluating candidates.

The distribution of these categories tells us a lot about your company's culture. Companies that value ability to learn and culture fit are inclined to give a higher weight to 'personality & cognitive skills'. 

 

Step 3. Share desired qualifications. 

The next step is sharing your desired qualifications. We're going to ask you more about education, job experience, certificates, hard skills and languages. This is again valuable information for us because now we can see what you think is important for this vacancy. You can even mark a qualification as a 'hard requirement', meaning that all candidates who don't meet these requirements will be unqualified for the job.

Input from us

Now we have gathered enough information to analyze your preferences. The next step for us is to advize you in how to measure personality and cognitive traits: which traits are important for different vacancies/companies and what should the desired outcome be. 

Step 4. Our suggestion for personality & cognitive skills.

Based on what we know from your company and a specific vacancy, our matching algorithm is able to advize you in which games are relevant for your company and specific vacancy. This advice is based on your company's behaviour and industry analyses. The advice is based on two aspects:

  • Which games are relevant;
  • What should the desired score be for every relevant game. 

Of course you're free to ignore our suggestion, but we advize you to follow this advice since it's based on important data.

After completing this step we have gathered enough information to determine your ideal candidate. This information will be processed in our matching algorithm so that we can predict the job success for every candidate who's applied to this vacancy.

Curious to see how we translate this prediction into a candidate matching profile? 

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