Most common Decision-drivers in the Recruiting process
By: Rosalie Bourgeois De Boynes
Tags: Artificial Intelligence, Recruiting, Recruitment, Cv, Candidate Experience

We aimed to improve the recruiting process by following theses guidelines: 1. save time and boosts productivity; 2. better quality results; 3. Less discrimination; 4. Less money spent on data collecting. Offer an alternative to the “CV” linear way of thinking which leads to an absurd hyper-specialization. Capitalize on the concept of “Learning curves.”

Looking for benefits

Our question is “How might we improve the recruiting process?”, We need to understand the recruiting process and its drivers fully. What drives the recruiter’s final decision? (at the early stage when deciding to interview the candidate and at the final stage when signing the working contract) To be tested: Is it a combination of quality, price, and time spent, or is there more?

‘Most recruiters use an ATS.’ According to Jobscan.co, 90% of large companies use an ATS to search for qualified candidates from large applicant pools (Jobscan.co). I ignore the date and sample data of the study, and I want to test if it is the case in my sample in 2018.

‘Recruiters are not aware of how bad their ATS is.’ According to CIO.com, 75% of applicants are automatically eliminated in the hiring process by the ATS, and 62% of companies using ATS admit that some qualified candidates are likely automatically filtered out of the vetting process by mistake.

Avoid discrimination

How to build a powerful selective tool, without renewing discriminatory processes? Our hypotheses are:

‘Tree-based solutions and clustering algorithms.’

‘Complex models.’

‘Design Thinking focuses on extremes and is a way to be less discriminatory. Taken that we want to avoid discrimination, can Design Thinking to be automated and self-learning?’

Collect more data on candidates

What data is essential when evaluating a candidate?

According to the netnography we conducted (fig.4), we got the following results about which data were taken into account in the recruiting process. We want to test which data is the most important, because there might be a gap between the info asked on CV, and the info needed to measure job performance. Plus, that might show that that information should be available in the screening step, not in the interview step.

Figure 4 — Occurrences of data points taken into account in CV screening (netnography)

What makes a candidate a good fit to understand what should appear in a CV?

‘CV is the key entry point, so candidates’ profile needs to have all information needed.’ According to Retalent, “90% of candidates are rejected based on resumés alone.” According to VisualCV, “only 2% of applicants get the interview” (Retalent, 2018).

According to Jean, leader at onepoint, “a candidate is a good fit if he/she can accomplish the tasks he is being asked to do, if he can challenge the rules that do not fit the reality of his/her work and if the quality of the work equals or exceed the amount of money he is being paid.”

‘We can quantify qualitative info without altering it with Natural Language Processing methods.’

The indexing problem: if the CV does not contain the right word, it will remain unknown. What are the existing solutions today to solve this problem? Our hypotheses are: